CN103050110B - Method, device and system for image adjustment - Google Patents

Abstract

Embodiments of the present invention provide an image adjustment method, device, and system, which relate to the field of multimedia technology, expand the zoom range of multimedia resources, and avoid distortion or distortion of multimedia resources when the zoom range is large. Embodiments of the present invention include: obtaining the target adjustment value of the image to be displayed, obtaining redundant information and intermediate images of the image to be displayed through the seam carving algorithm, and performing shrinkage processing on the image to be displayed through the shift map algorithm; during the process of image enlargement , after obtaining the intermediate image, it is necessary to enlarge the intermediate image first, and then use the shift map algorithm to reduce the enlarged image. The present invention is mainly applied in the multimedia resource scaling process.

Description

Method, device and system for image adjustment

technical field

The present invention relates to the field of multimedia technology, in particular to an image adjustment method, device and system.

Background technique

At present, the multimedia playing devices used by people are increasingly diversified, which puts forward higher requirements for displaying and playing multimedia resources on the multimedia playing devices. Usually, people hope that a multimedia playback device can play multimedia resources suitable for the resolution of its display component. The existing technology provides a content-aware-based adaptation technology, which can scale the currently displayed multimedia resource according to the structure and semantic information of the multimedia resource, thereby realizing the size adjustment of the currently displayed multimedia resource. Constraints, and can better maintain the visual effect of the multimedia and the integrity of the content.

In the process of realizing the above-mentioned prior art, the inventors found the following problems in the prior art: the currently displayed multimedia resource will be significantly distorted or distorted when the zoom range is large in the prior art.

Contents of the invention

Embodiments of the present invention provide a method, device and system for image adjustment, which expand the zoom range of multimedia resources and can avoid distortion or distortion of multimedia resources when the zoom range is large.

In order to achieve the above object, embodiments of the present invention adopt the following technical solutions:

In one aspect, a method for image adjustment is provided, including:

Obtain the target adjustment value and redundant information of the image to be displayed;

determining a first gap of the image to be displayed according to the redundant information;

comparing the determined quantity of the first gap with the target adjustment value of the image to be displayed;

When it is determined that the number of the first gaps is smaller than the target adjustment value, deleting the first gaps from the image to be displayed to obtain an intermediate image;

Perform shrinkage processing on the intermediate image to obtain a target image corresponding to the image to be displayed.

In a first possible implementation manner of the first aspect, the acquiring redundant information of the image to be processed includes:

performing calculations according to the image information of the image to be displayed to obtain a gradient map of the image to be displayed;

Obtain redundant information of the image to be displayed according to the calculated gradient map.

With reference to the first possible implementation of the first aspect, in the second possible implementation of the first aspect, the deleting the first gap from the image to be displayed to obtain the intermediate image includes:

Acquiring the forward energy standard of the image to be displayed;

performing calculations according to the forward energy standard and the gradient map to obtain a gap map corresponding to the image to be displayed;

The first seam is deleted from the seam map.

In a third possible implementation manner of the first aspect, the stepping down the intermediate image to obtain the target image corresponding to the image to be displayed includes:

performing calculations based on the intermediate image to obtain a saliency map corresponding to the intermediate image;

filtering the saliency map to obtain an important map corresponding to the saliency map;

performing calculations according to the importance map to obtain a pixel displacement gradient map corresponding to the importance map;

Perform calculation according to the pixel displacement gradient map to obtain a target image corresponding to the image to be displayed.

In combination with the third possible implementation of the first aspect, in the fourth possible implementation of the lichen aspect, after obtaining the significance map corresponding to the saliency map, it includes:

The important map is filtered to obtain a filtered map.

In combination with the first aspect, the first possible implementation of the first aspect, the second possible implementation of the first aspect, the third possible implementation of the first aspect, and the fourth possible implementation of the first aspect, in In the fifth possible implementation manner of the first aspect, before acquiring the target adjustment value and redundant information of the image to be displayed, it further includes:

When image adjustment is performed on the video to be displayed, the video to be displayed is converted into several images to be displayed by frame;

Obtaining the target adjustment value and redundant information of the image to be displayed includes:

The target adjustment values and redundant information corresponding to each of the plurality of images to be displayed are acquired.

With reference to the fifth possible implementation of the first aspect, in the sixth possible implementation of the first aspect, after shrinking the intermediate image to obtain a target image corresponding to the image to be displayed, further include :

Combining target images corresponding to each of the plurality of images to be displayed to obtain a target video corresponding to the video to be displayed.

In the second aspect, a method for image adjustment is provided, including:

Obtain the target adjustment value and redundant information of the image to be displayed;

determining a first gap of the image to be displayed according to the redundant information;

Deleting the first gap from the image to be displayed to obtain a first processed image;

Enlarging the first processed image according to the target adjustment value to obtain a second processed image;

performing shrinking processing on the second processed image to obtain a target image.

In a first possible implementation manner of the second aspect, the acquiring redundant information of the image to be processed includes:

performing calculations according to the image information of the image to be displayed to obtain a gradient map of the image to be displayed;

Obtain redundant information of the image to be processed according to the calculated gradient map.

With reference to the first possible implementation of the second aspect, in the second possible implementation of the second aspect, the deleting the first gap from the image to be displayed to obtain the first processed image includes:

Acquiring the forward energy standard of the image to be displayed;

performing calculations according to the forward energy standard and the gradient map to obtain a gap map corresponding to the image to be displayed;

The optimal seam is deleted from the seam map.

In a third possible implementation manner of the second aspect, performing shrinking processing on the second processed image to obtain a target image includes:

performing calculations based on the second processed image to obtain a saliency map corresponding to the second processed image;

filtering the saliency map to obtain an important map corresponding to the saliency map;

performing calculations according to the importance map to obtain a pixel displacement gradient map;

Perform calculation according to the pixel displacement gradient map to obtain a target image corresponding to the image to be displayed.

In combination with the third possible implementation of the second aspect, in the fourth possible implementation of the second aspect, after obtaining the significance map corresponding to the saliency map, the method includes:

The important map is filtered to obtain a filtered map.

In combination with the second aspect, the first possible implementation of the second aspect, the second possible implementation of the second aspect, the third possible implementation of the second aspect, and the fourth possible implementation of the second aspect, in In the fifth possible implementation manner of the second aspect, before acquiring the target adjustment value and redundant information of the image to be displayed, it further includes:

When adjusting the video to be displayed, converting the video to be displayed into several images to be displayed frame by frame;

Obtaining the target adjustment value and redundant information of the image to be displayed includes:

The target adjustment values and redundant information corresponding to each of the plurality of images to be displayed are acquired.

With reference to the fifth possible implementation of the second aspect, in the sixth possible implementation of the second aspect, after shrinking the second processed image to obtain the target image, further include:

Combining target images corresponding to each of the plurality of images to be displayed to obtain a target video corresponding to the video to be displayed.

In a third aspect, an image adjustment device is provided, including:

an acquisition unit, configured to acquire a target adjustment value and redundant information of the image to be displayed;

a determining unit, configured to determine the first gap of the image to be displayed according to the redundant information acquired by the acquiring unit;

a comparing unit, configured to compare the quantity of the first gap determined by the determining unit with the target adjustment value of the image to be displayed;

A deleting unit, configured to, according to the comparison result of the comparing unit, determine that when the number of the first gaps is less than the target adjustment value, delete the first gaps from the image to be displayed to obtain an intermediate image;

A processing unit, configured to perform reduction processing on the intermediate image obtained by the deleting unit to obtain a target image corresponding to the image to be displayed.

In a first possible implementation manner of the third aspect, the obtaining unit includes:

The first calculation module is used to perform calculation according to the image information of the image to be displayed to obtain the gradient map of the image to be displayed;

The first obtaining module is configured to obtain redundant information of the image to be displayed according to the gradient map obtained by the first calculating module.

With reference to the first possible implementation manner of the third aspect, in a second possible implementation manner of the third aspect, the deleting unit includes:

A second acquisition module, configured to acquire the forward energy standard of the image to be displayed;

A second calculation module, configured to perform calculations according to the forward energy standard acquired by the second acquisition module and the gradient map obtained by the first calculation module, to obtain a gap map corresponding to the image to be displayed;

A deletion module, configured to delete the first gap from the gap map obtained by the second calculation module.

In a third possible implementation manner of the third aspect, the processing unit includes:

A third calculation module, configured to perform calculations based on the intermediate image to obtain a saliency map corresponding to the intermediate image;

a filtering module, configured to filter the saliency map obtained by the third calculation module, to obtain an important map corresponding to the saliency map;

A fourth calculation module, configured to perform calculations according to the importance map obtained by the filtering module, to obtain a pixel displacement gradient map corresponding to the importance map;

A fifth calculation module, configured to perform calculation according to the pixel displacement gradient map obtained by the fourth calculation module, to obtain a target image corresponding to the image to be displayed.

With reference to the third possible implementation manner of the third aspect, in a fourth possible implementation manner of the third aspect, the filtering module is further configured to filter the importance map to obtain a filtered map.

In combination with the third aspect, the first possible realization of the third aspect, the second possible realization of the third aspect, the third possible realization of the third aspect, and the fourth possible realization of the third aspect, in In the fifth possible implementation manner of the third aspect, it also includes:

A conversion unit, configured to convert the video to be displayed into several images to be displayed frame by frame when performing image adjustment on the video to be displayed;

The acquiring unit is further configured to acquire target adjustment values and redundant information corresponding to each of the plurality of images to be displayed obtained by the converting unit.

In combination with the fifth possible implementation of the third aspect, the sixth possible implementation of the third aspect also includes:

The merging unit is configured to merge the target images corresponding to the plurality of images to be displayed to obtain the target video corresponding to the video to be displayed.

In a fourth aspect, an image adjustment device is provided, including:

an acquisition unit, configured to acquire a target adjustment value and redundant information of the image to be displayed;

a determining unit, configured to determine the first gap of the image to be displayed according to the redundant information acquired by the acquiring unit;

A deleting unit, configured to delete the first gap determined by the slit determining unit from the image to be displayed, to obtain a first processed image;

a first processing unit, configured to amplify the first processed image according to the target adjustment value to obtain a second processed image;

The second processing unit is configured to perform reduction processing on the second processed image obtained by the first processing unit to obtain a target image.

In a first possible implementation manner of the fourth aspect, the obtaining unit includes:

The first calculation module is used to perform calculation according to the image information of the image to be displayed to obtain the gradient map of the image to be displayed;

The first acquiring module is configured to acquire redundant information of the image to be processed according to the gradient map calculated by the first calculating module.

With reference to the first possible implementation manner of the fourth aspect, in a second possible implementation manner of the fourth aspect, the deleting unit includes:

A second acquisition module, configured to acquire the forward energy standard of the image to be displayed;

A second calculation module, configured to perform calculations according to the forward energy standard acquired by the second acquisition module and the gradient map obtained by the first calculation module, to obtain a gap map corresponding to the image to be displayed;

A deletion module, configured to delete the optimal gap from the gap map obtained by the second calculation module.

In a third possible implementation manner of the fourth aspect, the second processing unit includes:

A third calculation module, configured to perform calculations based on the second processed image obtained by the first processing unit, to obtain a saliency map corresponding to the second processed image;

a filtering module, configured to filter the saliency map obtained by the third calculation module, to obtain an important map corresponding to the saliency map;

A fourth calculation module, configured to perform calculations according to the importance map to obtain a pixel displacement gradient map;

The fifth calculation module is configured to perform calculation according to the pixel displacement gradient map to obtain a target image corresponding to the image to be displayed.

In a fourth possible implementation manner with reference to the fourth aspect, the filtering module is further configured to filter the importance map to obtain a filtered map.

In combination with the fourth aspect, the first possible realization of the fourth aspect, the second possible realization of the fourth aspect, the third possible realization of the fourth aspect, and the fourth possible realization of the fourth aspect, in In the fifth possible implementation manner of the fourth aspect, it also includes:

A conversion unit, configured to convert the video to be displayed into several images to be displayed frame by frame when performing image adjustment on the video to be displayed;

The acquiring unit is further configured to acquire target adjustment values and redundant information corresponding to each of the plurality of images to be displayed obtained by the conversion unit.

In combination with the fifth possible implementation of the fourth aspect, the sixth possible implementation of the fifth aspect also includes:

The merging unit is configured to merge the target images corresponding to the plurality of images to be displayed to obtain the target video corresponding to the video to be displayed.

A fifth aspect provides an image adjustment system, including the image adjustment device provided in the third aspect and the image adjustment device provided in the fourth aspect.

The image adjustment method, device, and system provided by the embodiments of the present invention realize the zooming of the image to be displayed, obtain the target adjustment value of the image to be displayed, and obtain redundant information and intermediate images of the image to be displayed through the seam carving algorithm. The image to be displayed is reduced through the shift map algorithm; in the image enlargement process, after the intermediate image is obtained, the intermediate image needs to be enlarged first, and then the enlarged image is reduced using the shift map algorithm. The existing technology uses a single algorithm to realize image adjustment, and when the adjustment range is large, image distortion or distortion may occur. However, the present invention is a technical solution of an embodiment, which expands the adjustment range of multimedia resources and can avoid distortion or distortion of multimedia resources when the zoom range is large.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. Those skilled in the art can also obtain other drawings based on these drawings without creative work.

FIG. 1 is a flowchart of an image adjustment method in Embodiment 1 of the present invention;

FIG. 2 is a flowchart of another image adjustment method in Embodiment 1 of the present invention;

FIG. 3 is a flow chart of an image adjustment method in Embodiment 2 of the present invention;

FIG. 4 is a flow chart of another image adjustment method in Embodiment 2 of the present invention;

FIG. 5 is a flow chart of another image adjustment method in Embodiment 2 of the present invention;

FIG. 6 is a flow chart of an image adjustment method in Embodiment 3 of the present invention;

FIG. 7 is a block diagram of an image adjustment device in Embodiment 4 of the present invention;

FIG. 8 is a block diagram of another image adjustment device in Embodiment 4 of the present invention;

FIG. 9 is a block diagram of another image adjustment device in Embodiment 4 of the present invention;

FIG. 10 is a block diagram of another image adjustment device in Embodiment 4 of the present invention;

FIG. 11 is a block diagram of an image adjustment system in Embodiment 4 of the present invention;

FIG. 12 is a block diagram of an image adjustment device in Embodiment 5 of the present invention;

Fig. 13 is a block diagram of another image adjustment device in Embodiment 5 of the present invention.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

Example 1

An embodiment of the present invention provides a method for image adjustment, which can be used for image reduction. As shown in FIG. 1, the image reduction method includes:

101. Acquire a target adjustment value and redundant information of an image.

Wherein, the target adjustment value refers to a target reduction value of the image to be displayed.

Wherein, the redundant information in this step refers to the black border or white border of the image and other unimportant information. The unimportant information refers to information that has no significant effect on image display and will not cause image distortion or distortion after being removed. These unimportant information may be embodied in the form of pixels, pixel strips, or pixel blocks.

102. Determine a first gap of the image to be displayed according to the redundant information.

Among them, the first gap is the non-salient information of the image to be displayed, which is a set of gaps that need to be deleted by the seam carving algorithm.

It is worth noting that seam carving is a preferred content-aware adaptation technology in this embodiment. The core of the algorithm is to find the seam (gap) that needs to be removed or inserted, that is, from left to right or from left to right A connected top-to-bottom pixel with the lowest cumulative energy.

Optionally, the seam carving algorithm calculates the gradient map of the original image through a corresponding formula, and then judges the redundant information of the image through the gradient map, so as to obtain the number of seams to be deleted. For example, the gradient map is analyzed by the seam carving algorithm to obtain several pixels with the lowest cumulative energy, so as to obtain the number of seams to be deleted.

103. Compare the determined quantity of the first gap with the target adjustment value of the image to be displayed.

It should be noted that the comparison mentioned in this step is a numerical comparison between the number of gaps and the target adjustment value.

104. When it is determined that the number of the first gaps is smaller than the target adjustment value, delete the first gaps from the image to be displayed to obtain an intermediate image.

In this embodiment, the intermediate image can delete the first gap from the image to be displayed through the method provided in the seam carving algorithm.

Furthermore, when the number of the first gaps is less than the target adjustment value, the dynamic programming method is used to select the optimal gaps, and delete these first gaps from the image to be displayed in order to obtain the intermediate image. It is worth noting that, This method is realized by seam carving.

105. Perform shrinkage processing on the intermediate image to obtain a target image corresponding to the image to be displayed.

It is worth noting that, in this embodiment, this step can realize the reduction processing of the intermediate image by executing the shift map algorithm. Among them, the shift map algorithm is a preferred adaptation technology based on content awareness in this embodiment, and the core of the algorithm is to find the seam that needs to be removed or inserted.

The image adjustment method provided by the embodiment of the present invention can also be used for image enlargement, as shown in Figure 2, the image enlargement method includes:

1. Obtain the target adjustment value and redundant information of the image to be displayed.

Wherein, the target adjustment value refers to a target magnification value of the image to be displayed.

It is worth noting that the description of the redundant information is the same as that in step 102, and will not be described in detail here.

2. Determine the first gap of the image to be displayed according to the redundant information.

It should be noted that this step corresponds to the above-mentioned step 102, and the related description is the same, and will not be described in detail here.

3. Deleting the first gap from the image to be displayed to obtain a first processed image.

Wherein, the first image is the intermediate image described in step 104, that is, the first processed image can delete the first gap from the image to be displayed through the method provided in the seam carving algorithm.

Furthermore, the dynamic programming method is used to select the optimal seam, and delete these first seams in sequence from the image to be displayed, so as to obtain the first processed image. It is worth noting that this method is realized through seamcarving.

4. Enlarge the first processed image according to the target adjustment value to obtain a second processed image.

Preferably, the first processed image is enlarged by 1.1 times by using the nearest neighbor method, so as to obtain a second processed image with a pixel size of (w+cw)*1.1. Wherein, w is the pixel of the image to be displayed, and cw refers to the target magnification value of the image to be displayed.

It is worth noting that the nearest neighbor method calculates a certain coordinate of the first processed image to obtain a floating-point coordinate, and performs a simple rounding process on it to obtain an integer coordinate corresponding to the original first processed image, namely Get the pixel value of the second processed image coordinate. The operation process for other coordinates is similar. In this embodiment, taking the pixel value corresponding to the upper left corner closest to the floating point coordinates as an example, the above is only an implementation method of the nearest neighbor, which is not limited in the embodiment of the present invention.

In addition, choosing to enlarge by 1.1 times can reduce the complexity of the calculation without affecting the integrity of the image to be displayed.

5. Perform reduction processing on the second processed image to obtain a target image.

Wherein, the reduction processing is performed on the second processed image, and the reduction factor is related to the image enlargement factor in step 4. For example, in step 4, the first processed image is magnified by 1.1 times, correspondingly the first processed image should be reduced by (w+cw)*0.1 at this time, and the reduction method is realized by the shift map algorithm. The relevant description of the shift map algorithm is the same as the description in the above step 105, and will not be described in detail here.

The image adjustment method provided by the embodiment of the present invention realizes the scaling of the image to be displayed, obtains the target adjustment value of the image to be displayed, obtains redundant information and intermediate images of the image to be displayed through the seam carving algorithm, and then uses the shift map algorithm The image to be displayed is reduced; in the process of image enlargement, after the intermediate image is obtained, the intermediate image needs to be enlarged first, using the shift map algorithm. The existing technology uses a single algorithm to realize image adjustment, and when the adjustment range is large, image distortion or distortion may occur. However, the present invention is a technical solution of an embodiment, which expands the adjustment range of multimedia resources and can avoid distortion or distortion of multimedia resources when the zoom range is large.

Example 2

The present invention provides a method for image adjustment. This embodiment is a method for shrinking a picture. As shown in FIG. 3 , the method includes:

201. Acquire a target adjustment value of an image to be displayed, where the target adjustment value is a target reduction value of the image to be displayed.

Wherein, the target adjustment value refers to a target reduction value of the image to be displayed, that is, a value that needs to be reduced on the basis of the original size of the image to be displayed for the current multimedia playback device. The target adjustment value may be an adjustment value that the image to be displayed needs to satisfy the size constraint of the image to be displayed on the current multimedia playback device; it may also be an adjustment value customized by the user for the image to be displayed.

202. Perform calculation according to the image information of the image to be displayed to obtain a gradient map of the image to be displayed. Wherein, formula can be used for the calculation of gradient map: for example, for image I, the calculation method of its gradient map, is In this calculation formula, I represents an image, Indicates the vertical gradient, Indicates the gradient in the horizontal direction.

203. Acquire redundant information of an image to be displayed according to the calculated gradient map.

Wherein, the relevant description of the redundant information is the same as the description in step 101 of Embodiment 1, and will not be repeated here.

Further, obtaining redundant information includes judging whether the image has black borders or white borders and obtaining other non-salient information.

204. Determine a first gap of the image to be displayed according to the redundant information.

205. Compare the determined quantity of the first gap with the target adjustment value of the image to be displayed.

206. Acquire a forward energy standard of the image to be displayed when it is determined that the number of the first gaps is smaller than the target adjustment value.

Among them, the forward energy standard is the standard for determining which seam will be removed in the seam carving algorithm. Embedded energy is generated when previously disconnected pixels may become neighbors after the gaps are removed, forming new edges. Suppose an image is resized by removing k gaps (t=1, . . . , k). To measure the true change in image energy each time a seam is removed, we measure the difference between the energy of the image after seam removal and the energy of the part of the previous image that was not removed (i.e., image energy minus seam energy). A small difference means that removing the gap will have little effect on the image.

It is worth noting that when the first seam number is greater than the target adjustment value, that is, the target adjustment value can be directly processed by the seam carving algorithm, which belongs to a small adjustment range, and will not be described in detail here.

207. Perform calculation according to the forward energy standard and the gradient map to obtain a gap map corresponding to the image to be displayed.

It is worth noting that the calculation of the gap map can be realized by the following formula:

M(i, j)=e(i, j)+min(M(i-1, j-1), M(i-1, j), M(i-1, j+1)), where, M represents the gap map, and e(i, j) represents the gradient value of each pixel position. M represents the minimum cumulative energy of seam for all possible connections of each entry (i, j).

208. Delete the first seam from the seam map to obtain an intermediate image.

Wherein, the relevant description of this step is the same as the description in the above-mentioned step 104, and will not be repeated here.

209. Perform shrinkage processing on the intermediate image to obtain a target image corresponding to the image to be displayed.

It is worth noting that this step is the same as the description of step 105 in Embodiment 1, and will not be repeated here.

Wherein, the implementation method of reducing the intermediate image, as shown in Figure 4, the implementation method includes:

2091. Perform calculation according to the intermediate image to obtain a saliency map corresponding to the intermediate image.

2092. Filter the saliency map to obtain an importance map corresponding to the saliency map.

It is worth noting that the specific implementation method of step 2092 includes using the intermediate image as the guidance, taking the saliency map as the input image, and using the guided filter to filter the saliency map to obtain the important map.

2093. Perform calculation according to the significance map to obtain a pixel displacement gradient map corresponding to the significance map.

Among them, calculating according to the importance map means constructing a mapping function according to the importance map, and calculating the gradient map of the pixel displacement on the basis of the importance map.

In addition, it is worth noting that the mapping function is used to estimate the displacement of each pixel when the current important map is changed to a pixel displacement gradient map.

2094. Perform calculation according to the pixel displacement gradient map to obtain a target image corresponding to the image to be displayed.

Wherein, the calculation is performed according to the pixel displacement gradient map, that is, the displacement gradient map is integrated on the entire image, and a smooth displacement map shiftmap is constructed in combination with importance-weighted filtering, so as to obtain the target image. The shift map stores the displacement corresponding to each pixel in the intermediate image. The position of each pixel in the image can be obtained by adding or subtracting the corresponding displacement to the position of the pixel in the target image.

Further, it is worth noting that the above steps 202-208 can be implemented in combination with the processing method provided by the seam carving algorithm, and step 209 (including: 2091-2094) can be implemented in combination with the processing method provided by the shift map algorithm.

In addition, it is worth noting that when shrinking the video to be displayed, the specific implementation methods include:

Converting the video to be displayed frame by frame into several images to be displayed;

It is worth noting that a video can be regarded as composed of several images, and the video processing for display is processed in a frame-by-frame order, and a frame is an image. Then the shrinking of the video can be regarded as the shrinking processing of several images converted from the video.

Target adjustment values corresponding to each of the plurality of images to be displayed are acquired.

Wherein, the target adjustment value refers to a target reduction value of the image to be displayed.

Further, after acquiring the corresponding target adjustment values of the several images to be displayed, it is transformed into a process of sequentially reducing the images of the several images to be displayed, that is, executing steps 202-209 in this embodiment.

It is worth noting that, the original steps 2091-2094, when performing video reduction, as shown in Figure 5, the specific execution method is as follows:

209a. Perform calculation according to the intermediate image to obtain a saliency map corresponding to the intermediate image.

209b. Filter the saliency map to obtain an importance map corresponding to the saliency map.

209c. Filter the obtained important map to obtain a filtered map.

209d. Perform calculation according to the filter map to obtain a pixel displacement gradient map corresponding to the importance map.

209e. Perform calculation according to the pixel displacement gradient map to obtain a target image corresponding to the image to be displayed.

That is, compared to the original steps 2091-2094, a new step 209c is added. For step 209c, the important map is filtered, and in the process of obtaining the filtered map, the important map of the previous image is used as a guide, and the important map of the current image is used as a guide. Input to get the filter map of the current image. The execution of this step can better ensure the quality of the video, that is, when the video converted into the image to be displayed is reintegrated from the image into the video, it can prevent the video from shaking.

After step 209 is executed, the reduction processing of the images to be displayed converted from the video to be displayed is completed, and then the processed images to be displayed are merged to recompose the video.

The image adjustment method provided by the embodiment of the present invention realizes the reduction of the image, obtains the gradient map and the seam map of the image to be displayed through the seamcarving algorithm, and deletes the first seam by comparing the first seam with the target adjustment value of the image to be displayed. The gap obtains the intermediate image with relatively less redundant information, and then reduces the intermediate image through the shift map algorithm to obtain the target image of the image to be displayed. In the prior art solution, a single algorithm is used to realize image reduction, and when the reduction range is large, image distortion or distortion may occur. However, the present invention is a technical solution of an embodiment, which expands the narrowing range of multimedia resources and can avoid distortion or distortion of multimedia resources when the zooming range is large.

Example 3

The present invention provides a method for image adjustment. This embodiment is a method for image enlargement. As shown in FIG. 6, the method includes:

301. Acquire a target adjustment value of an image to be displayed, where the target adjustment value is a target magnification value of the image to be displayed.

Wherein, the target adjustment value refers to a target magnification value of the picture to be displayed, that is, a value that needs to be magnified on the basis of the original size of the picture to be displayed for the current multimedia playback device. The target adjustment value may be an adjustment value that the image to be displayed needs to satisfy the size constraint of the image to be displayed on the current multimedia playback device; it may also be an adjustment value customized by the user for the image to be displayed.

302. Perform calculation according to the image information of the image to be displayed to obtain a gradient map of the image to be displayed.

Wherein, the relevant description of step 202 of the calculation flow formula of the gradient map is the same, and will not be described in detail here.

303. Acquire redundant information of an image to be displayed according to the calculated gradient map.

Wherein, the explanation of the redundant information is the same as the related description of step 101 in the first embodiment, and the related description of obtaining the redundant information is the same as the related description of the step 203 in the second embodiment, and will not be described in detail here.

304. Determine a first gap of the image to be displayed according to the redundant information.

305. Acquire a forward energy standard of the image to be displayed.

Wherein, the relevant content of the forward energy is the same as the relevant description of step 206 in Embodiment 2, and will not be repeated here.

306. Perform calculation according to the forward energy standard and the gradient map to obtain a gap map corresponding to the image to be displayed.

Wherein, the calculation method (formula) of the gap map is the same as the relevant description of step 207 in Embodiment 2, and will not be repeated here.

307. Delete the first gap from the image to be displayed to obtain a first processed image.

It is worth noting that this step is opposite to step 3 in Embodiment 1, and the relevant description is the same, and will not be described in detail here.

308. Enlarge the first processed image according to the target adjustment value to obtain a second processed image.

Wherein, the implementation method of enlarging the first processed image is the same as the relevant description of step 4 in Embodiment 1, and will not be repeated here. Further, for an example of zooming in on an image, reference may also be made to the example of Step 4 in Embodiment 1.

309. Perform reduction processing on the second processed image to obtain a target image.

Wherein, the reduction processing is performed on the second processed image, and the reduction factor corresponds to the image enlargement factor in step 4.

Further, as step 308 refers to the example of step 4 in embodiment 1, this step 309 can also refer to the example of step 105 in embodiment 1 for illustration, and will not be repeated here.

3091. Perform calculation according to the intermediate image to obtain a saliency map corresponding to the intermediate image.

3092. Filter the saliency map to obtain an importance map corresponding to the saliency map.

3093. Perform calculation according to the significance map to obtain a pixel displacement gradient map corresponding to the significance map.

3094. Perform calculation according to the pixel displacement gradient map to obtain a target image corresponding to the image to be displayed.

It is worth noting that the above-mentioned steps 3091-3094 correspond to steps 2091-2094 in Embodiment 2, and the relevant descriptions are the same. Refer to FIG. 4 for the corresponding method flow chart, and the description will not be repeated here.

Further, it is worth noting that the above steps 302-307 can be realized in combination with the corresponding method in the seam carving algorithm, step 308 can be realized in combination with the method of the nearest neighbor, and step 309 (including: 3091-3094) is realized in combination with the shift map algorithm implemented in the corresponding method.

In addition, it is worth noting that when the video to be displayed is enlarged, the specific implementation methods include:

Converting the video to be displayed frame by frame into several images to be displayed;

It is worth noting that a video can be regarded as composed of several images, and the video processing for display is processed in a frame-by-frame order, and a frame is an image. The enlargement of the video can be regarded as the enlargement processing of several images converted from the video.

Target adjustment values corresponding to each of the plurality of images to be displayed are acquired.

Wherein, the target adjustment value refers to a target magnification value of the image to be displayed.

Further, after the target adjustment values corresponding to the several images to be displayed are obtained, it is transformed into a process of sequentially zooming in on the several images to be displayed, that is, steps 302-309 in this embodiment are executed.

It is worth noting that, the original steps 3091-3094, when zooming in on the video, the specific execution method is as follows, and the corresponding flow chart of the method is shown in Figure 5:

309a. Perform calculation according to the intermediate image to obtain a saliency map corresponding to the intermediate image.

309b. Filter the saliency map to obtain an importance map corresponding to the saliency map.

309c. Filter the obtained important map to obtain a filtered map.

309d. Perform calculation according to the filter map to obtain a pixel displacement gradient map corresponding to the importance map.

309e. Perform calculation according to the pixel displacement gradient map to obtain a target image corresponding to the image to be displayed.

That is, compared to the original steps 3091-3094, a new step 309c is added. For step 309c, the important map is filtered, and in the process of obtaining the filtered map, the important map of the previous image is used as a guide, and the important map of the current image is used as a guide. Input to get the filter map of the current image. The execution of this step can better ensure the quality of the video, that is, when the video converted into the image to be displayed is reintegrated from the image into the video, it can prevent the video from shaking.

After step 309 is executed, the zoom-in processing of the images to be displayed converted from the video to be displayed is completed, and then the processed images to be displayed are merged to recompose the video.

The image adjustment method provided by the embodiment of the present invention realizes the enlargement of the picture, obtains the gradient map of the image to be displayed, the first processed image, etc. through the seamcarving algorithm, and obtains the first seam, and deletes the first seam to obtain redundant information There are relatively few intermediate images; and then the intermediate image is enlarged by the nearest neighbor method to obtain the second processed image; and then the second processed image is reduced by the shift map algorithm to obtain the target image of the image to be displayed. In the prior art solution, a single algorithm is used to enlarge the image, and when the enlargement range is large, distortion or distortion of the image may occur. However, the present invention is a technical solution of an embodiment, which expands the zoom range of multimedia resources and can avoid distortion or distortion of multimedia resources when the zoom range is large.

Example 4

An embodiment of the present invention provides an image adjustment device 4, as shown in FIG.

The acquiring unit 41 is configured to acquire the target adjustment value and redundant information of the image to be displayed.

The determining unit 42 is configured to determine the first gap of the image to be displayed according to the redundant information acquired by the acquiring unit 41 .

A comparing unit 43, configured to compare the number of first gaps determined by the determining unit 42 with the target adjustment value of the image to be displayed.

The deleting unit 44 is configured to delete the first gap from the image to be displayed to obtain an intermediate image when it is determined according to the comparison result of the comparing unit 43 that the number of the first gap is smaller than the target adjustment value.

The processing unit 45 is configured to perform reduction processing on the intermediate image obtained by the deleting unit 44 to obtain a target image corresponding to the image to be displayed.

Optionally, as shown in FIG. 8, the acquisition unit 41 includes: a first calculation module 411, a first acquisition module 412; the deletion unit 44 includes: a second acquisition module 441, a second calculation module 442, a deletion Module 443 ; the processing unit 45 includes: a third calculation module 451 , a filter module 452 , a fourth calculation module 453 , and a fifth calculation module 454 .

The first calculation module 411 is configured to perform calculation according to the image information of the image to be displayed to obtain a gradient map of the image to be displayed.

The first obtaining module 412 is configured to obtain redundant information of the image to be displayed according to the gradient map obtained by the first calculating module 411 .

The second acquiring module 441 is configured to acquire the forward energy standard of the image to be displayed.

The second calculation module 442 is configured to perform calculations according to the forward energy standard acquired by the second acquisition module 441 and the gradient map obtained by the first calculation module 412, to obtain the corresponding to the image to be displayed Gap map.

The deletion module 443 is configured to delete the first gap from the gap map obtained by the second calculation module 442 .

The third calculation module 451 is configured to perform calculation according to the intermediate image to obtain a saliency map corresponding to the intermediate image;

A filtering module 452, configured to filter the saliency map obtained by the third calculation module 451, to obtain an important map corresponding to the saliency map;

A fourth calculation module 453, configured to perform calculations according to the importance map obtained by the filtering module 452, to obtain a pixel displacement gradient map corresponding to the importance map;

The fifth calculation module 454 is configured to perform calculation according to the pixel displacement gradient map obtained by the fourth calculation module 453 to obtain a target image corresponding to the image to be displayed.

Optionally, the device also includes: a converting unit 46 and a merging unit 47, which are applied when video needs to be displayed, which are assigned to FIG. 8 and combined with FIG. 8 to form a complete device.

The conversion unit 46 is configured to convert the video to be displayed into several images to be displayed frame by frame when performing image adjustment on the video to be displayed.

The acquiring unit 41 is further configured to acquire target adjustment values and redundant information corresponding to each of the plurality of images to be displayed obtained by the conversion unit 46 .

The merging unit 47 is configured to merge the target images corresponding to the plurality of images to be displayed to obtain the target video corresponding to the video to be displayed.

The embodiment of the present invention also provides an image adjustment device 5, as shown in FIG.

The acquisition unit 51 is configured to acquire the target adjustment value and redundant information of the image to be displayed.

The determining unit 52 is configured to determine the first gap of the image to be displayed according to the redundant information acquired by the acquiring unit 51 .

The deleting unit 53 is configured to delete the first gap determined by the gap determining unit 52 from the image to be displayed to obtain a first processed image.

The first processing unit 54 is configured to amplify the first processed image according to the target adjustment value to obtain a second processed image.

The second processing unit 55 is configured to perform reduction processing on the second processed image obtained by the first processing unit 54 to obtain a target image.

Optionally, as shown in FIG. 10, the acquisition unit 51 includes: a first calculation module 511, a first acquisition module 512; the deletion unit 53 includes: a second acquisition module 531, a second calculation module 532, a deletion module 533 ; the second processing unit 55 includes: a third computing module 551 , a filtering module 552 , a fourth computing module 553 , and a fifth computing module 554 .

The first calculation module 511 is configured to perform calculation according to the image information of the image to be displayed to obtain a gradient map of the image to be displayed.

The first acquiring module 512 is configured to acquire redundant information of the image to be processed according to the gradient map calculated by the first calculating module 511 .

The second acquiring module 531 is configured to acquire the forward energy standard of the image to be displayed.

The second calculation module 532 is configured to perform calculation according to the forward energy standard acquired by the second acquisition module 531 and the gradient map obtained by the first calculation module 511, to obtain the gap corresponding to the image to be displayed picture.

The deletion module 533 is configured to delete the optimal gap from the gap map obtained by the second calculation module 532 .

The third calculation module 551 is configured to perform calculation according to the second processed image obtained by the first processing unit, to obtain a saliency map corresponding to the second processed image.

The filtering module 552 is configured to filter the saliency map obtained by the third calculation module 551 to obtain an important map corresponding to the saliency map.

The fourth calculation module 553 is configured to perform calculation according to the importance map to obtain a pixel displacement gradient map.

The fifth calculation module 554 is configured to perform calculation according to the pixel displacement gradient map to obtain a target image corresponding to the image to be displayed.

Optionally, the device 5 further includes: a converting unit 56 and a combining unit 57 .

A conversion unit 56, configured to convert the video to be displayed into several images to be displayed frame by frame when performing image adjustment on the video to be displayed;

The acquiring unit 51 is further configured to acquire target adjustment values and redundant information corresponding to each of the plurality of images to be displayed obtained by the conversion unit 56 .

The merging unit 57 is configured to merge target images corresponding to the plurality of images to be displayed to obtain a target video corresponding to the video to be displayed.

The embodiment of the present invention also provides an image adjustment system, as shown in FIG. 11 , the system includes: an image adjustment device 4 and an image adjustment device 5 . Wherein, the image adjustment device 4 includes the above-mentioned units 41-47, and the image adjustment device 5 includes the above-mentioned units 51-57.

The image adjustment device and system provided by the embodiments of the present invention realize the scaling of the image to be displayed, obtain the target adjustment value of the image to be displayed, obtain the redundant information and the intermediate image of the image to be displayed through the seam carving algorithm, and then pass the shift The map algorithm shrinks the image to be displayed; in the process of image enlargement, after obtaining the intermediate image, the intermediate image needs to be enlarged first, using the shift map algorithm. The existing technology uses a single algorithm to realize image adjustment, and when the adjustment range is large, image distortion or distortion may occur. However, the present invention is a technical solution of an embodiment, which expands the adjustment range of multimedia resources and can avoid distortion or distortion of multimedia resources when the zoom range is large.

Example 5

An embodiment of the present invention provides an image adjustment device. As shown in FIG. 12 , the device includes: a processor 01 and a memory 02 .

Processor 01, configured to acquire a target adjustment value of an image to be displayed.

The memory 02 is configured to store the target adjustment value of the image to be displayed.

The processor 01 performs calculation according to the image information of the image to be displayed to obtain a gradient map of the image to be displayed.

The memory 02 is configured to store image information of the image to be displayed.

The processor 01 acquires redundant information of the image to be displayed according to the calculated gradient map.

The memory 02 is configured to store the redundant information.

The processor 01 determines a first gap of the image to be displayed according to the redundant information.

The memory 02 is configured to store the first gap.

The processor 01 compares the determined quantity of the first gap with the target adjustment value of the image to be displayed.

The memory 02 is used to store the comparison result.

Processor 01, configured to acquire a forward energy standard of the image to be displayed when it is determined that the number of the first gaps is smaller than the target adjustment value.

The memory 02 is configured to store the forward energy standard.

The processor 01 is configured to perform calculations according to the forward energy standard and the gradient map to obtain a gap map corresponding to the image to be displayed.

The memory 02 is configured to store the slit map.

Processor 01, configured to delete the first seam from the seam map to obtain an intermediate image.

The memory 02 is used to store the intermediate image.

The processor 01 is configured to shrink the intermediate image to obtain a target image corresponding to the image to be displayed.

The memory 02 is used to store the target image.

The processor 01 is further configured to perform calculations based on the intermediate image to obtain a saliency map corresponding to the intermediate image.

The memory 02 is configured to store the saliency map.

The processor 01 is configured to filter the saliency map to obtain an importance map corresponding to the saliency map.

The memory 02 is configured to store the importance map.

The processor 01 is further configured to perform calculations according to the significance map to obtain a pixel displacement gradient map corresponding to the significance map.

The memory 02 is also used to store the pixel displacement gradient map.

The processor 01 is configured to perform calculation according to the pixel displacement gradient map to obtain a target image corresponding to the image to be displayed.

The memory 02 is used to store the target image.

The processor 01 is also used to convert the video to be displayed into several images to be displayed frame by frame.

The memory 02 is used for storing the several images to be displayed.

Processor 01 is used to combine the processed images to be displayed to reconstruct the video.

The memory 02 is used for storing the recomposed video.

The embodiment of the present invention also provides an image adjustment device, as shown in FIG. 13 , the device includes: a processor 03 and a memory 04 .

A processor 03, configured to acquire a target adjustment value of the image to be displayed.

The memory 04 is configured to store the target adjustment value of the image to be displayed.

The processor 03 is configured to perform calculation according to the image information of the image to be displayed to obtain a gradient map of the image to be displayed.

The memory 04 is used to store the gradient map.

The processor 03 is configured to acquire redundant information of the image to be displayed according to the calculated gradient map.

The memory 04 is configured to store the redundant information.

The processor 03 is configured to determine the first gap of the image to be displayed according to the redundant information.

The memory 04 is also used to store the first slit.

A processor 03, configured to acquire the forward energy standard of the image to be displayed. .

The memory 04 is configured to store the forward energy standard.

The processor 03 is configured to perform calculations according to the forward energy standard and the gradient map to obtain a gap map corresponding to the image to be displayed.

The memory 04 is configured to store the slit map.

The processor 03 is configured to delete the first gap from the image to be displayed to obtain a first processed image.

The memory 04 is configured to store the first processed image.

The processor 03 is configured to amplify the first processed image according to the target adjustment value to obtain a second processed image.

The memory 04 is configured to store the second processed image.

The processor 03 is configured to perform reduction processing on the second processed image to obtain a target image.

The memory 04 is used to store the target image.

The processor 03 is further configured to perform calculations based on the intermediate image to obtain a saliency map corresponding to the intermediate image.

The memory 04 is also used to store the saliency map.

The processor 03 is further configured to filter the saliency map to obtain an importance map corresponding to the saliency map.

The memory 04 is also used to store the importance map.

The processor 03 is further configured to perform calculations according to the significance map to obtain a pixel displacement gradient map corresponding to the significance map.

The memory 04 is also used to store the pixel displacement gradient map.

The processor 03 is further configured to perform calculation according to the pixel displacement gradient map to obtain a target image corresponding to the image to be displayed.

The memory 04 is used to store the target image.

Optionally, the processor 03 is also configured to convert the video to be displayed into several images to be displayed frame by frame.

The processor 03 is also configured to combine the processed images to be displayed to recompose the video.

The memory 04 is also used for storing the recomposed video.

The image adjustment device provided by the embodiment of the present invention realizes the scaling of the image to be displayed, obtains the target adjustment value of the image to be displayed, obtains redundant information and intermediate images of the image to be displayed through the seam carving algorithm, and then uses the shift map algorithm The image to be displayed is reduced; in the process of image enlargement, after the intermediate image is obtained, the intermediate image needs to be enlarged first, using the shift map algorithm. The existing technology uses a single algorithm to realize image adjustment, and when the adjustment range is large, image distortion or distortion may occur. However, the present invention is a technical solution of an embodiment, which expands the adjustment range of multimedia resources and can avoid distortion or distortion of multimedia resources when the zoom range is large.

Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be realized by means of software plus necessary general-purpose hardware, and of course also by hardware, but in many cases the former is a better embodiment . Based on this understanding, the essence of the technical solution of the present invention or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product is stored in a readable storage medium, such as a floppy disk of a computer , a hard disk or an optical disk, etc., including several instructions for enabling a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

The above is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Anyone skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present invention. Should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the protection scope of the claims.

Claims (25)

1. a method for Image Adjusting, is characterized in that, comprising:

Obtain target adjustment value and the redundant information of image to be displayed;

According to described redundant information, determine the first gap of described image to be displayed;

The quantity in the first gap determined and the target adjustment value of described image to be displayed are compared, described target adjustment value refers to the shrinking of object value of described image to be displayed;

When determining that the quantity in described first gap is less than described target adjustment value, from image to be displayed, deleting the first gap, obtain intermediate image;

Process is reduced to described intermediate image, obtains the target image corresponding with described image to be displayed;

Described process is reduced to described intermediate image, obtains the target image corresponding with described image to be displayed and comprise:

Calculate according to described intermediate image, obtain the remarkable figure corresponding with described intermediate image;

Filtering is carried out to described remarkable figure, obtains the significance map corresponding with described remarkable figure;

Calculate according to described significance map, obtain the pixel displacement gradient map corresponding with described significance map;

Calculate according to described pixel displacement gradient map, obtain the target image corresponding with described image to be displayed.

2. the method for Image Adjusting according to claim 1, is characterized in that, the redundant information of the pending image of described acquisition comprises:

Image information according to image to be displayed calculates, and obtains the gradient map of described image to be displayed;

According to the gradient map of the described image to be displayed calculated, obtain the redundant information of image to be displayed.

3. the method for Image Adjusting according to claim 2, is characterized in that, describedly from image to be displayed, deletes the first gap, obtains intermediate image and comprises:

Obtain the forward power standard of described image to be displayed;

Gradient map according to described forward power standard and described image to be displayed calculates, and obtains the gap figure that described image to be displayed is corresponding;

Described first gap is deleted from the figure of gap.

4. the method for Image Adjusting according to claim 1, is characterized in that, after obtaining the significance map corresponding with described remarkable figure, comprising:

Filtering is carried out to described significance map, obtains filtering figure.

5. the method for the Image Adjusting according to claim 1-4 any one, is characterized in that, before the target adjustment value obtaining image to be displayed and redundant information, also comprises:

When treating display video and carrying out Image Adjusting, described video to be shown is converted into several images to be displayed frame by frame;

The target adjustment value and the redundant information that then obtain image to be displayed comprise:

Obtain the described each self-corresponding target adjustment value of several images to be displayed and redundant information.

6. the method for Image Adjusting according to claim 5, is characterized in that, is reducing process, after obtaining the target image corresponding with described image to be displayed, also comprise described intermediate image:

Merging treatment is carried out to each self-corresponding target image of several images to be displayed described, obtains the target video corresponding with described video to be shown.

7. a method for Image Adjusting, is characterized in that, comprising:

Obtain target adjustment value and the redundant information of image to be displayed;

According to described redundant information, determine the first gap of described image to be displayed;

From image to be displayed, delete the first gap, obtain the first process image;

According to described target adjustment value, amplify described first process image, obtain the second process image, described target adjustment value refers to the targeted amplification values of image to be displayed;

Process is reduced to described second process image, obtains target image;

Described to described second process image reduce process, obtain target image comprise:

Calculate according to described second process image, obtain processing remarkable figure corresponding to image with described second;

Filtering is carried out to described remarkable figure, obtains the significance map corresponding with described remarkable figure;

Calculate according to described significance map, obtain pixel displacement gradient map;

Calculate according to described pixel displacement gradient map, obtain the target image corresponding with described image to be displayed.

8. the method for Image Adjusting according to claim 7, is characterized in that, the redundant information of the pending image of described acquisition comprises:

Image information according to image to be displayed calculates, and obtains the gradient map of described image to be displayed;

According to the gradient map of the described image to be displayed calculated, obtain the redundant information of pending image.

9. the method for Image Adjusting according to claim 8, is characterized in that, describedly from image to be displayed, deletes the first gap, obtains the first process image and comprises:

Obtain the forward power standard of described image to be displayed;

Gradient map according to described forward power standard and described image to be displayed calculates, and obtains the gap figure that described image to be displayed is corresponding;

Described first gap is deleted from described gap figure.

10. the method for Image Adjusting according to claim 7, is characterized in that, after obtaining the significance map corresponding with described remarkable figure, comprising:

Filtering is carried out to described significance map, obtains filtering figure.

The method of 11. Image Adjustings according to claim 7-10 any one, is characterized in that, before the target adjustment value obtaining image to be displayed and redundant information, also comprises:

When treating display video and adjusting, described video to be shown is converted into several images to be displayed frame by frame;

The target adjustment value and the redundant information that then obtain image to be displayed comprise:

Obtain the described each self-corresponding target adjustment value of several images to be displayed and redundant information.

The method of 12. Image Adjustings according to claim 11, is characterized in that, is reducing process, after obtaining target image, also comprise described second process image:

Merging treatment is carried out to each self-corresponding target image of several images to be displayed described, obtains the target video corresponding with described video to be shown.

The device of 13. 1 kinds of Image Adjustings, is characterized in that, comprising:

Acquiring unit, for obtaining target adjustment value and the redundant information of image to be displayed;

Determining unit, for the described redundant information obtained according to described acquiring unit, determines the first gap of described image to be displayed;

Comparing unit, compare for the quantity in the first gap described determining unit determined and the target adjustment value of described image to be displayed, described target adjustment value refers to the shrinking of object value of described image to be displayed;

Delete cells, for the comparative result according to described comparing unit, determines, when the quantity in described first gap is less than described target adjustment value, deletes the first gap, obtain intermediate image from image to be displayed;

Processing unit, reduces process for the described intermediate image obtained described delete cells, obtains the target image corresponding with described image to be displayed;

Described processing unit comprises:

3rd computing module, for calculating according to described intermediate image, obtains the remarkable figure corresponding with described intermediate image;

Filtration module, carries out filtering for the described remarkable figure obtained described 3rd computing module, obtains the significance map corresponding with described remarkable figure;

4th computing module, calculates for the described significance map obtained according to described filtration module, obtains the pixel displacement gradient map corresponding with described significance map;

5th computing module, calculates for the described pixel displacement gradient map obtained according to described 4th computing module, obtains the target image corresponding with described image to be displayed.

The device of 14. Image Adjustings according to claim 13, is characterized in that, described acquiring unit comprises:

First computing module, calculates for the image information according to image to be displayed, obtains the gradient map of described image to be displayed;

First acquisition module, for the described gradient map obtained according to described first computing module, obtains the redundant information of image to be displayed.

The device of 15. Image Adjustings according to claim 14, is characterized in that, described delete cells comprises:

Second acquisition module, for obtaining the forward power standard of described image to be displayed;

Second computing module, the described gradient map obtained for the described forward power standard that gets according to described second acquisition module and described first computing module calculates, and obtains the gap figure that described image to be displayed is corresponding;

Removing module, deletes the first gap in the described gap figure that obtains from described second computing module.

The device of 16. Image Adjustings according to claim 13, is characterized in that, described filtration module also for carrying out filtering to described significance map, obtains filtering figure.

17. according to the device of claim 13-16 described in any one, and it is characterized in that, this device also comprises:

Conversion unit, for when treating display video and carrying out Image Adjusting, is converted into several images to be displayed frame by frame by described video to be shown;

Described acquiring unit, also for obtain described conversion unit obtain described in each self-corresponding target adjustment value of several images to be displayed and redundant information.

The device of 18. Image Adjustings according to claim 17, is characterized in that, this device also comprises:

Merge cells, for carrying out merging treatment to each self-corresponding target image of several images to be displayed described, obtains the target video corresponding with described video to be shown.

The device of 19. 1 kinds of Image Adjustings, is characterized in that, comprising:

Acquiring unit, for obtaining target adjustment value and the redundant information of image to be displayed;

Determining unit, for the redundant information got according to described acquiring unit, determines the first gap of described image to be displayed;

Delete cells, for deleting the first gap that described determining unit is determined from image to be displayed, obtains the first process image;

First processing unit, for according to described target adjustment value, amplify described first process image, obtain the second process image, described target adjustment value refers to the targeted amplification values of image to be displayed;

Second processing unit, reduces process for the second process image obtained described first processing unit, obtains target image;

Described second processing unit comprises:

3rd computing module, calculates for the described second process image obtained according to described first processing unit, obtains processing remarkable figure corresponding to image with described second;

Filtration module, carries out filtering for the described remarkable figure obtained described 3rd computing module, obtains the significance map corresponding with described remarkable figure;

4th computing module, for calculating according to described significance map, obtains pixel displacement gradient map;

5th computing module, for calculating according to described pixel displacement gradient map, obtains the target image corresponding with described image to be displayed.

The device of 20. Image Adjustings according to claim 19, is characterized in that, described acquiring unit comprises:

First computing module, calculates for the image information according to image to be displayed, obtains the gradient map of described image to be displayed;

First acquisition module, for the described gradient map calculated according to described first computing module, obtains the redundant information of pending image.

The device of 21. Image Adjustings according to claim 20, is characterized in that, described delete cells comprises:

Second acquisition module, for obtaining the forward power standard of described image to be displayed;

Second computing module, calculates for the described gradient map obtained according to described forward power standard and described first computing module of described second acquisition module acquisition, obtains the gap figure that described image to be displayed is corresponding;

Removing module, deletes described first gap in the gap figure that obtains from described second computing module.

The device of 22. Image Adjustings according to claim 19, is characterized in that, described filtration module also for carrying out filtering to described significance map, obtains filtering figure.

23. according to the device of claim 19-22 described in any one, and it is characterized in that, this device also comprises:

Conversion unit, for when treating display video and carrying out Image Adjusting, is converted into several images to be displayed frame by frame by described video to be shown;

Described acquiring unit, also for obtain described conversion unit obtain described in each self-corresponding target adjustment value of several images to be displayed and redundant information.

The device of 24. Image Adjustings according to claim 23, is characterized in that, this device also comprises:

Merge cells, for carrying out merging treatment to each self-corresponding target image of several images to be displayed described, obtains the target video corresponding with described video to be shown.

The system of 25. 1 kinds of Image Adjustings, is characterized in that, described system comprises the device of the device of the Image Adjusting as described in claim 13-18 any one and the Image Adjusting as described in claim 19-24 any one.