WO2018113224A1 - Picture reduction method and device - Google Patents
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- the present invention relates to the field of image processing technologies, and in particular, to an image reduction method and apparatus.
- the bilinear interpolation algorithm is an algorithm that is relatively simple in hardware implementation and can also take into account image effects between 1 and 1/2, and is therefore widely used.
- the ratio is reduced to a smaller ratio than 1/2, the image display effect is deteriorated, and the content is missing, and the problem cannot be clearly seen. The problem is particularly noticeable when the text is displayed.
- other better scaling algorithms such as polynomial interpolation algorithms and bicubic interpolation algorithms, are used, good image reduction can be achieved, but hardware resources such as multipliers and data buffers will multiply.
- An image reduction method and device provided by an embodiment of the present invention solves the current image reduction
- the spatial characteristics are mainly due to the high complexity of the current image reduction algorithm, resulting in a technical problem with low accuracy.
- the reduction factor Determining the reduction factor, if the reduction factor is greater than 2 and less than 3, or the reduction factor is greater than 3 and less than 4, performing image mean reduction processing, image edge enhancement processing, and image double on the image sequentially Linear reduction processing;
- the reduction factor is an integer, the image average reduction processing and the image edge enhancement processing are sequentially performed on the image;
- the method before outputting the processed image, the method further includes:
- the reduction factor is greater than 1 and less than 2, the image bilinear reduction processing is directly performed on the image.
- the image mean reduction process specifically includes:
- a pixel point mean calculation of adjacent lines is performed on the image.
- the image edge enhancement processing specifically includes:
- the image edge position is calculated, and the image edge information is enhanced and compensated.
- the image bilinear reduction processing specifically includes:
- a reduction unit configured to calculate a reduction factor of the output image size to be output according to the input image size of the image
- a determining unit configured to determine the reduction factor, if the reduction factor is greater than 2 and less than 3, or, the reduction factor is greater than 3 and less than 4, triggering the first execution unit; and is further configured to trigger the second execution unit if the reduction factor is an integer;
- the first execution unit is configured to trigger an image mean reduction module, an image edge enhancement module, and an image bilinear reduction module to sequentially perform image mean reduction processing, image edge enhancement processing, and image bilinear reduction processing on the image;
- the second execution unit is configured to trigger the image mean reduction module, and the image edge enhancement module sequentially performs image mean reduction processing and image edge enhancement processing on the image;
- An output unit for outputting the processed image.
- the method further includes: a third execution unit;
- the determining unit is further configured to trigger the third execution unit if the reduction factor is greater than 1 and less than 2;
- the third execution unit is configured to trigger the image bilinear reduction module to directly perform image bilinear reduction processing on the image.
- the image mean reduction module is configured to perform pixel point mean calculation of adjacent rows on the image.
- the image edge enhancement module is configured to calculate an image edge position according to pixel point information of an adjacent point of the image, and enhance image edge information compensation. .
- the image bilinear reduction module is configured to calculate an interpolation coefficient and a corresponding pixel point coordinate according to the reduction factor, and perform interpolation calculation on the image.
- An image reduction method and apparatus comprising: calculating a reduction factor of an output image size to be output according to an input image size of the image; and determining a reduction factor, if the reduction factor is greater than 2 If the reduction factor is greater than 3 and less than 4, the image average reduction processing, the image edge enhancement processing, and the image bilinear reduction processing are sequentially performed on the image; if the reduction factor is an integer, the image average reduction processing is sequentially performed on the image.
- Image edge enhancement processing Output the processed image.
- the reduction factor of the output image size to be output is calculated according to the input image size of the image; and the reduction factor is determined, if the reduction factor is greater than 2 and less than 3, or the reduction factor is greater than 3 and less than 4, then
- the image sequentially performs image mean reduction processing, image edge enhancement processing, and image bilinear reduction processing; if the reduction factor is an integer, the image average reduction processing and image edge enhancement processing are sequentially performed on the image; and the processed image is output, and the current image is solved.
- the inability to achieve better image reduction effects becomes a technical problem in image processing.
- FIG. 1 is a schematic flowchart of an embodiment of an image reduction method according to an embodiment of the present invention
- FIG. 2 is a schematic structural diagram of an embodiment of an image reducing apparatus according to an embodiment of the present invention.
- FIG. 3 to 5 are schematic views of the application examples of Figs. 1 and 2 .
- the image reduction method and device provided by the embodiments of the present invention solve the problem that the current image reduction is mainly based on spatial features. Due to the high complexity of the current image reduction algorithm, the technical problem of low accuracy is caused.
- an embodiment of an image reduction method according to an embodiment of the present invention includes:
- step 102 Determine the reduction factor. If the reduction factor is greater than 2 and less than 3, or the reduction factor is greater than 3 and less than 4, step 103 is performed; if the reduction factor is an integer, step 104 is performed; if the reduction factor is greater than 1 and less than 2, then perform step 105;
- step 103 is performed. If the reduction factor is an integer, step 104 is performed; if the reduction factor is greater than 1 and less than 2, step 105 is performed.
- the image mean reduction processing, the image edge enhancement processing, and the image bilinear reduction processing are sequentially performed on the image.
- the image average reduction processing and the image edge enhancement processing are sequentially performed on the image.
- the reduction factor is greater than 1 and less than 2, the image is directly subjected to image bilinear reduction processing.
- step 103 or 104 or 105 the processed image is output.
- the image mean reduction process specifically includes: performing pixel point mean calculation on adjacent lines of the image.
- the image edge enhancement processing specifically includes: calculating image edge positions according to pixel point information of adjacent points of the image, and enhancing image edge information compensation.
- the image bilinear reduction processing specifically includes: calculating an interpolation coefficient and a corresponding pixel point coordinate according to the reduction factor, and then performing interpolation calculation on the image.
- the application examples include:
- a two-level reduction module and an image edge enhancement module are adopted.
- the image reduction factor different image reduction modules are selected, and the image edge enhancement module is used to sharpen the image, and this method can be reduced to 1/2. After a smaller ratio, the image clarity is still maintained, and the method is actually verified and applied in an FPGA (Field Programmable Gate Array) chip.
- FPGA Field Programmable Gate Array
- the control module is responsible for calculating the reduction factor according to the size of the input and output image, and then feeding the reduction factor into the image mean reduction module and the image bilinear reduction module and controlling the operation thereof, and controlling to select the relevant module or bypass the module.
- the image mean reduction module uses an average value method to perform integer reduction processing on the image. For example, if 1/2 is reduced (the reduction factor is 2), the two adjacent pixels of the image are added horizontally, and then 2 is averaged, and the pixels of the adjacent two rows are added correspondingly, and then 2 is averaged. If it is reduced by 1/3 (the reduction factor is 3), the adjacent three pixels of the image are added horizontally, and then the average is divided by 3, and the pixels of the adjacent three rows are correspondingly added, and then 3 is averaged.
- the image mean reduction module can preserve the image information well when zooming out, but is only suitable for integer multiple reduction, and the edge information becomes weak after the image takes the average value.
- the image edge enhancement module calculates the edge position of the image according to the information of the adjacent points of the image, and enhances the image edge information. For example, when the values of adjacent points differ by more than the threshold, such as 45, it is considered to be the edge position of the image.
- the threshold can be adjusted in real time, such as 1/2 reduction, the threshold is set to 90; 1/2, the threshold is set to 45; the reduction is 1/3, the threshold is set to 30, and so on.
- the edge position of the image is found, the pixel points at the edge position are enlarged correspondingly by the reduced multiple. For example, if the image is reduced by 1/2, the pixel is multiplied by 2 to enlarge the image edge information.
- the image bilinear reduction module uses a bilinear interpolation algorithm to calculate the interpolation coefficient and the corresponding pixel point coordinates according to the reduction factor, and then interpolates the image, and finally outputs the reduced image.
- the bilinear interpolation algorithm is actually the most common linear interpolation, because linear interpolation is decomposed into two directions, horizontal and vertical, so it is called bilinear interpolation. It calculates the gray value of the interpolated pixel point through the two-dimensional linear weighted average calculation through the gray value of the four relevant pixel points around the pixel to be interpolated. As shown in Fig.
- the original 4 pixel points are a1, a2, a3, a4, and the horizontal and vertical points are 1 unit length, and their coordinate positions are a1(x, y), a2(x+1). , y), a3(x, y+1), a4(x+1, y+1), the coordinates of the pixel to be interpolated are P(x+dx, y+dy), which are calculated by bilinear interpolation
- the gray value of the interpolated pixel point P is:
- control module calculates the reduction factor according to the input and output image size, and then controls the different modules according to the reduced multiple.
- the bypass image mean reduction module and the image edge enhancement module are then entered into the image bilinear reduction module for reduction processing. After the output.
- the image mean reduction module is used to perform a larger integer multiple reduction in the interval according to the reduction magnification, and then enter the image edge enhancement module to perform edge information enhancement. Then recalculate the new reduction factor based on the size of the integer multiple reduction, and finally The image is output after the image is processed by the bilinear reduction module.
- the input image resolution is 1024x768, and the output image resolution is 320x240.
- First calculate the reduction ratio, 320/1024 0.3125, then reduce the multiple (the reciprocal of the reduction ratio) between 2-3, so use the reduction factor 2 to reduce the image mean, reduce the image size to 512x384, and then enter the edge of the image.
- the enhancement module performs edge information enhancement.
- calculate the new reduction ratio, 320/512 0.625, then reduce the multiple between 1-2, enter the image bilinear reduction module and then reduce the output again to get the final output image.
- the reduction factor of the output image size to be output is calculated according to the input image size of the image; and the reduction factor is determined, if the reduction factor is greater than 2 and less than 3, or the reduction factor is greater than 3 and less than 4, then
- the image sequentially performs image mean reduction processing, image edge enhancement processing, and image bilinear reduction processing; if the reduction factor is an integer, the image average reduction processing and image edge enhancement processing are sequentially performed on the image; and the processed image is output, and the current image is solved.
- the inability to achieve better image reduction effects becomes a technical problem in image processing.
- an embodiment of an image reduction apparatus includes:
- the reducing unit 201 is configured to calculate a reduction factor of the output image size to be output according to the input image size of the image;
- the determining unit 202 is configured to determine the reduction factor. If the reduction factor is greater than 2 and less than 3, or the reduction factor is greater than 3 and less than 4, the first execution unit 203 is triggered; and if the reduction factor is an integer, the trigger is triggered. a second execution unit 204;
- the first execution unit 203 is configured to trigger the image mean reduction module a, the image edge enhancement module b, and the image bilinear reduction module c to sequentially perform image mean reduction processing, image edge enhancement processing, and image bilinear reduction processing on the image;
- the second execution unit 204 is configured to trigger the image mean reduction module a and the image edge enhancement module b to sequentially perform image mean reduction processing and image edge enhancement processing on the image.
- the determining unit 202 is further configured to trigger the third executing unit 205 if the reduction factor is greater than 1 and less than 2;
- the third executing unit 205 is configured to trigger the image bilinear reduction module to directly perform image bilinear reduction processing on the image.
- the output unit 206 is configured to output the processed image.
- the image mean reduction module a is used for performing pixel point mean calculation of adjacent lines on the image.
- the image edge enhancement module b is configured to calculate an image edge position according to pixel point information of an adjacent point of the image, and enhance the image edge information to compensate.
- the image bilinear reduction module c is configured to calculate an interpolation coefficient and a corresponding pixel point coordinate according to the reduction factor, and then perform interpolation calculation on the image.
- the current image reduction is mainly based on spatial features. Due to the high complexity of the current image reduction algorithm, the technical problem of low accuracy is caused.
- the disclosed system, apparatus, and method may be implemented in other manners.
- the device embodiments described above are merely illustrative.
- the division of the unit is only a logical function division.
- there may be another division manner for example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored or not executed.
- the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be in an electrical, mechanical or other form.
- the unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, may be located in one place, or It can also be distributed to multiple network elements. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
- each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
- the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
- the integrated unit if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium.
- the technical solution of the present invention which is essential or contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product stored in a storage medium.
- a number of instructions are included to cause a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention.
- the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .
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Abstract
Description
本申请要求于2016年12月19日提交中国专利局、申请号为201611178321.6、发明名称为“一种图像缩小方法及装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。The present application claims priority to Chinese Patent Application No. 201611178321.6, entitled "A Method for Image Reduction and Apparatus", filed on Dec. 19, 2016, the entire disclosure of which is incorporated herein by reference. .
本发明涉及图像处理技术领域，尤其涉及一种图像缩小方法及装置。The present invention relates to the field of image processing technologies, and in particular, to an image reduction method and apparatus.
图像(Picture)有多种含义，其中最常见的定义是指各种图形和影像的总称。在理科的学习以及日常的学习或统计中，图像都是必不可少的组成部分，它为人类构建了一个形象的思维模式，有助于我们学习、思考问题。Picture has many meanings, the most common definition of which refers to the general name of various graphics and images. In science learning and daily learning or statistics, images are an indispensable part of it. It builds an image of human minds that helps us learn and think about problems.
在众多图像缩小算法中，双线性插值算法是硬件实现相对简单，同时在缩小1～1/2之间也能兼顾到图像效果的一种算法，因此也被广泛地使用。但是当缩小到比1/2更小的比例时，图像显示效果就会变差，出现内容缺失，无法看清楚的问题，在显示文字的时候问题尤其明显。如果采用其他更好的缩放算法，如多项式插值算法，双立方插值算法，可以取得很好的图像缩小效果，但是硬件资源例如乘法器，数据缓存，就会成倍增加。例如，以RGB格式图像为例，使用双线性插值算法缩小，一般需使用12个乘法器，6行数据缓存；而使用双立方插值算法缩小，一般则需使用24个乘法器，12行数据缓存；如使用多项式插值算法缩小，则需要更多的乘法器和数据缓存。Among many image reduction algorithms, the bilinear interpolation algorithm is an algorithm that is relatively simple in hardware implementation and can also take into account image effects between 1 and 1/2, and is therefore widely used. However, when the ratio is reduced to a smaller ratio than 1/2, the image display effect is deteriorated, and the content is missing, and the problem cannot be clearly seen. The problem is particularly noticeable when the text is displayed. If other better scaling algorithms, such as polynomial interpolation algorithms and bicubic interpolation algorithms, are used, good image reduction can be achieved, but hardware resources such as multipliers and data buffers will multiply. For example, taking an RGB format image as an example, using a bilinear interpolation algorithm to reduce, generally requires 12 multipliers, 6 rows of data buffer; and using a bicubic interpolation algorithm to reduce, generally requires 24 multipliers, 12 rows of data. Cache; if you use a polynomial interpolation algorithm to shrink, you need more multipliers and data cache.
因此如何在较少的硬件资源下，又能实现较好的图像缩小效果成为图像处理实际应用中的一个难题。Therefore, how to achieve better image reduction effect with less hardware resources becomes a difficult problem in the practical application of image processing.
发明内容Summary of the invention
本发明实施例提供的一种图像缩小方法及装置，解决了目前的图像缩小都 以空间特征为主，由于目前的图像缩小算法的复杂度高，导致了准确率低的技术问题。An image reduction method and device provided by an embodiment of the present invention solves the current image reduction The spatial characteristics are mainly due to the high complexity of the current image reduction algorithm, resulting in a technical problem with low accuracy.
本发明实施例提供的一种图像缩小方法，包括：An image reduction method provided by an embodiment of the present invention includes:
根据图像的输入图像大小计算待输出的输出图像大小的缩小倍数；Calculating a reduction factor of the output image size to be output according to the input image size of the image;
对所述缩小倍数进行判断，若所述缩小倍数大于2且小于3，或者，所述缩小倍数大于3且小于4，则对所述图像依次进行图像均值缩小处理、图像边缘增强处理、图像双线性缩小处理；Determining the reduction factor, if the reduction factor is greater than 2 and less than 3, or the reduction factor is greater than 3 and less than 4, performing image mean reduction processing, image edge enhancement processing, and image double on the image sequentially Linear reduction processing;
若所述缩小倍数为整数，则对所述图像依次进行图像均值缩小处理、图像边缘增强处理；If the reduction factor is an integer, the image average reduction processing and the image edge enhancement processing are sequentially performed on the image;
输出处理后的图像。Output the processed image.
可选地，输出处理后的图像之前还包括：Optionally, before outputting the processed image, the method further includes:
若所述缩小倍数大于1且小于2，则直接对所述图像进行图像双线性缩小处理。If the reduction factor is greater than 1 and less than 2, the image bilinear reduction processing is directly performed on the image.
可选地，图像均值缩小处理具体包括：Optionally, the image mean reduction process specifically includes:
对所述图像进行相邻行的像素点均值计算。A pixel point mean calculation of adjacent lines is performed on the image.
可选地，图像边缘增强处理具体包括：Optionally, the image edge enhancement processing specifically includes:
根据所述图像相邻点的像素点信息，计算出图像边缘位置，并把图像边缘信息增强补偿。According to the pixel point information of the adjacent points of the image, the image edge position is calculated, and the image edge information is enhanced and compensated.
可选地，图像双线性缩小处理具体包括：Optionally, the image bilinear reduction processing specifically includes:
根据所述缩小倍数计算插值系数和对应像素点坐标，再对所述图像进行插值计算。Calculating the interpolation coefficient and the corresponding pixel point coordinates according to the reduction factor, and performing interpolation calculation on the image.
本发明实施例提供的一种图像缩小装置，包括：An image reduction device provided by an embodiment of the present invention includes:
缩小单元，用于根据图像的输入图像大小计算待输出的输出图像大小的缩小倍数；a reduction unit, configured to calculate a reduction factor of the output image size to be output according to the input image size of the image;
判断单元，用于对所述缩小倍数进行判断，若所述缩小倍数大于2且小于 3，或者，所述缩小倍数大于3且小于4，则触发第一执行单元；还用于若所述缩小倍数为整数，则触发第二执行单元；a determining unit, configured to determine the reduction factor, if the reduction factor is greater than 2 and less than 3, or, the reduction factor is greater than 3 and less than 4, triggering the first execution unit; and is further configured to trigger the second execution unit if the reduction factor is an integer;
所述第一执行单元，用于触发图像均值缩小模块、图像边缘增强模块、图像双线性缩小模块对所述图像依次进行图像均值缩小处理、图像边缘增强处理、图像双线性缩小处理；The first execution unit is configured to trigger an image mean reduction module, an image edge enhancement module, and an image bilinear reduction module to sequentially perform image mean reduction processing, image edge enhancement processing, and image bilinear reduction processing on the image;
所述第二执行单元，用于触发所述图像均值缩小模块、所述图像边缘增强模块对所述图像依次进行图像均值缩小处理、图像边缘增强处理；The second execution unit is configured to trigger the image mean reduction module, and the image edge enhancement module sequentially performs image mean reduction processing and image edge enhancement processing on the image;
输出单元，用于输出处理后的图像。An output unit for outputting the processed image.
可选地，还包括：第三执行单元；Optionally, the method further includes: a third execution unit;
所述判断单元，还用于若所述缩小倍数大于1且小于2，则触发第三执行单元；The determining unit is further configured to trigger the third execution unit if the reduction factor is greater than 1 and less than 2;
所述第三执行单元，用于触发所述图像双线性缩小模块直接对所述图像进行图像双线性缩小处理。The third execution unit is configured to trigger the image bilinear reduction module to directly perform image bilinear reduction processing on the image.
可选地，所述图像均值缩小模块，用于对所述图像进行相邻行的像素点均值计算。Optionally, the image mean reduction module is configured to perform pixel point mean calculation of adjacent rows on the image.
可选地，所述图像边缘增强模块，用于根据所述图像相邻点的像素点信息，计算出图像边缘位置，并把图像边缘信息增强补偿。。Optionally, the image edge enhancement module is configured to calculate an image edge position according to pixel point information of an adjacent point of the image, and enhance image edge information compensation. .
可选地，所述图像双线性缩小模块，用于根据所述缩小倍数计算插值系数和对应像素点坐标，再对所述图像进行插值计算。Optionally, the image bilinear reduction module is configured to calculate an interpolation coefficient and a corresponding pixel point coordinate according to the reduction factor, and perform interpolation calculation on the image.
从以上技术方案可以看出，本发明实施例具有以下优点：It can be seen from the above technical solutions that the embodiments of the present invention have the following advantages:
本发明实施例提供的一种图像缩小方法及装置，其中，图像缩小方法包括：根据图像的输入图像大小计算待输出的输出图像大小的缩小倍数；对缩小倍数进行判断，若缩小倍数大于2且小于3，或者，缩小倍数大于3且小于4，则对图像依次进行图像均值缩小处理、图像边缘增强处理、图像双线性缩小处理；若缩小倍数为整数，则对图像依次进行图像均值缩小处理、图像边缘增强处理； 输出处理后的图像。本实施例中，通过根据图像的输入图像大小计算待输出的输出图像大小的缩小倍数；对缩小倍数进行判断，若缩小倍数大于2且小于3，或者，缩小倍数大于3且小于4，则对图像依次进行图像均值缩小处理、图像边缘增强处理、图像双线性缩小处理；若缩小倍数为整数，则对图像依次进行图像均值缩小处理、图像边缘增强处理；输出处理后的图像，解决了目前的在较少的硬件资源下，无法实现较好的图像缩小效果成为图像处理的技术问题。An image reduction method and apparatus according to an embodiment of the present invention, wherein the image reduction method comprises: calculating a reduction factor of an output image size to be output according to an input image size of the image; and determining a reduction factor, if the reduction factor is greater than 2 If the reduction factor is greater than 3 and less than 4, the image average reduction processing, the image edge enhancement processing, and the image bilinear reduction processing are sequentially performed on the image; if the reduction factor is an integer, the image average reduction processing is sequentially performed on the image. Image edge enhancement processing; Output the processed image. In this embodiment, the reduction factor of the output image size to be output is calculated according to the input image size of the image; and the reduction factor is determined, if the reduction factor is greater than 2 and less than 3, or the reduction factor is greater than 3 and less than 4, then The image sequentially performs image mean reduction processing, image edge enhancement processing, and image bilinear reduction processing; if the reduction factor is an integer, the image average reduction processing and image edge enhancement processing are sequentially performed on the image; and the processed image is output, and the current image is solved. With less hardware resources, the inability to achieve better image reduction effects becomes a technical problem in image processing.
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简要介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域的普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the present invention, Those skilled in the art can also obtain other drawings based on these drawings without paying any creative work.
图1为本发明实施例提供的一种图像缩小方法的一个实施例的流程示意图；1 is a schematic flowchart of an embodiment of an image reduction method according to an embodiment of the present invention;
图2为本发明实施例提供的一种图像缩小装置的一个实施例的结构示意图；2 is a schematic structural diagram of an embodiment of an image reducing apparatus according to an embodiment of the present invention;
图3至图5为图1、图2的应用例示意图。3 to 5 are schematic views of the application examples of Figs. 1 and 2 .
本发明实施例提供的一种图像缩小方法及装置，解决了目前的图像缩小都以空间特征为主，由于目前的图像缩小算法的复杂度高，导致了准确率低的技术问题。The image reduction method and device provided by the embodiments of the present invention solve the problem that the current image reduction is mainly based on spatial features. Due to the high complexity of the current image reduction algorithm, the technical problem of low accuracy is caused.
为了使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明作进一步地详细描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做 出创造性劳动前提下所获得的所有其它实施例，都属于本发明保护的范围。The present invention will be further described in detail with reference to the accompanying drawings, in which FIG. Based on the embodiments of the present invention, those of ordinary skill in the art are not doing All other embodiments obtained under the premise of creative labor are within the scope of the invention.
请参阅图1，本发明实施例提供的一种图像缩小方法的一个实施例包括：Referring to FIG. 1, an embodiment of an image reduction method according to an embodiment of the present invention includes:
101、根据图像的输入图像大小计算待输出的输出图像大小的缩小倍数；101. Calculate a reduction factor of the output image size to be output according to the input image size of the image;
本实施例中，当需要进行图像缩小时，首先需要根据图像的输入图像大小计算待输出的输出图像大小的缩小倍数。In this embodiment, when image reduction is required, it is first necessary to calculate the reduction factor of the output image size to be output according to the input image size of the image.
102、对缩小倍数进行判断，若缩小倍数大于2且小于3，或者，缩小倍数大于3且小于4，则执行步骤103；若缩小倍数为整数，则执行步骤104；若缩小倍数大于1且小于2，则执行步骤105；102. Determine the reduction factor. If the reduction factor is greater than 2 and less than 3, or the reduction factor is greater than 3 and less than 4,
当根据图像的输入图像大小计算待输出的输出图像大小的缩小倍数之后，需要对缩小倍数进行判断，若缩小倍数大于2且小于3，或者，缩小倍数大于3且小于4，则执行步骤103，若缩小倍数为整数，则执行步骤104；若缩小倍数大于1且小于2，则执行步骤105。After calculating the reduction factor of the output image size to be output according to the input image size of the image, it is necessary to determine the reduction factor. If the reduction factor is greater than 2 and less than 3, or the reduction factor is greater than 3 and less than 4,
103、对图像依次进行图像均值缩小处理、图像边缘增强处理、图像双线性缩小处理；103. Perform image mean reduction processing, image edge enhancement processing, and image bilinear reduction processing on the image in sequence;
当缩小倍数大于2且小于3，或者，缩小倍数大于3且小于4时，对图像依次进行图像均值缩小处理、图像边缘增强处理、图像双线性缩小处理。When the reduction factor is greater than 2 and less than 3, or the reduction factor is greater than 3 and less than 4, the image mean reduction processing, the image edge enhancement processing, and the image bilinear reduction processing are sequentially performed on the image.
104、对图像依次进行图像均值缩小处理、图像边缘增强处理；104. Perform image mean reduction processing and image edge enhancement processing on the image sequentially;
当缩小倍数为整数时，则对图像依次进行图像均值缩小处理、图像边缘增强处理。When the reduction factor is an integer, the image average reduction processing and the image edge enhancement processing are sequentially performed on the image.
105、直接对图像进行图像双线性缩小处理；105. Perform image bilinear reduction processing on the image directly;
若缩小倍数大于1且小于2，则直接对图像进行图像双线性缩小处理。If the reduction factor is greater than 1 and less than 2, the image is directly subjected to image bilinear reduction processing.
106、输出处理后的图像。106. Output the processed image.
步骤103或104或105之后，输出处理后的图像。After
需要说明的是，图像均值缩小处理具体包括：对图像进行相邻行的像素点均值计算。 It should be noted that the image mean reduction process specifically includes: performing pixel point mean calculation on adjacent lines of the image.
图像边缘增强处理具体包括：根据图像相邻点的像素点信息，计算出图像边缘位置，并把图像边缘信息增强补偿。The image edge enhancement processing specifically includes: calculating image edge positions according to pixel point information of adjacent points of the image, and enhancing image edge information compensation.
图像双线性缩小处理具体包括：根据缩小倍数计算插值系数和对应像素点坐标，再对图像进行插值计算。The image bilinear reduction processing specifically includes: calculating an interpolation coefficient and a corresponding pixel point coordinate according to the reduction factor, and then performing interpolation calculation on the image.
下面以一具体应用场景进行描述，如图3至图5所示，应用例包括：The following describes a specific application scenario. As shown in FIG. 3 to FIG. 5, the application examples include:
采用了两级缩小模块加图像边缘增强模块，根据图像缩小倍数关系，选择不同的图像缩小模块，并利用图像边缘增强模块对图像进行锐化补偿，采用这种方法可以在缩小到比1/2更小的比例后，仍然保持较好的图像清晰度，并且本方法在FPGA(Field Programmable Gate Array，现场可编程门阵列)芯片中进行了实际验证和应用。A two-level reduction module and an image edge enhancement module are adopted. According to the image reduction factor, different image reduction modules are selected, and the image edge enhancement module is used to sharpen the image, and this method can be reduced to 1/2. After a smaller ratio, the image clarity is still maintained, and the method is actually verified and applied in an FPGA (Field Programmable Gate Array) chip.
(1)控制模块，负责根据输入输出图像的大小计算缩小倍数，然后将缩小倍数送入图像均值缩小模块和图像双线性缩小模块并控制其工作，同时控制选择使用相关模块或bypass该模块。(1) The control module is responsible for calculating the reduction factor according to the size of the input and output image, and then feeding the reduction factor into the image mean reduction module and the image bilinear reduction module and controlling the operation thereof, and controlling to select the relevant module or bypass the module.
(2)图像均值缩小模块，采用取平均值得方法对图像进行整数倍的缩小处理。例如，缩小1/2(缩小倍数为2)，则对图像水平相邻两像素点相加，再除2取平均，同时对相邻两行的像素点对应相加，再除2取平均。如果缩小1/3(缩小倍数为3)，则对图像水平相邻三像素点相加，再除3取平均，同时对相邻三行的像素点对应相加，再除3取平均。图像均值缩小模块能够在缩小时很好地保留图像信息，但是只适合于整数倍缩小，并且图像取均值后边缘信息变弱。(2) The image mean reduction module uses an average value method to perform integer reduction processing on the image. For example, if 1/2 is reduced (the reduction factor is 2), the two adjacent pixels of the image are added horizontally, and then 2 is averaged, and the pixels of the adjacent two rows are added correspondingly, and then 2 is averaged. If it is reduced by 1/3 (the reduction factor is 3), the adjacent three pixels of the image are added horizontally, and then the average is divided by 3, and the pixels of the adjacent three rows are correspondingly added, and then 3 is averaged. The image mean reduction module can preserve the image information well when zooming out, but is only suitable for integer multiple reduction, and the edge information becomes weak after the image takes the average value.
(3)图像边缘增强模块，根据图像相邻点的信息，计算出图像边缘位置，并把图像边缘信息增强补偿。例如，当相邻点的数值相差超过门限值，比如45，则认为是图像边缘位置，根据缩小的倍数，可以实时调整门限值，如缩小1/2，门限值设为90；缩小1/2，门限值设为45；缩小1/3，门限值设为30，依次类推。当发现图像边缘位置时，将边缘位置的像素点按缩小的倍数对应放大， 例如图像是缩小1/2的，则将该像素点乘2放大，这样可以使图像边缘信息重新得到增强。(3) The image edge enhancement module calculates the edge position of the image according to the information of the adjacent points of the image, and enhances the image edge information. For example, when the values of adjacent points differ by more than the threshold, such as 45, it is considered to be the edge position of the image. According to the reduced multiple, the threshold can be adjusted in real time, such as 1/2 reduction, the threshold is set to 90; 1/2, the threshold is set to 45; the reduction is 1/3, the threshold is set to 30, and so on. When the edge position of the image is found, the pixel points at the edge position are enlarged correspondingly by the reduced multiple. For example, if the image is reduced by 1/2, the pixel is multiplied by 2 to enlarge the image edge information.
(4)图像双线性缩小模块，采用双线性插值算法，根据缩小倍数计算插值系数和对应像素点坐标，再对图像进行插值计算，最后输出缩小后图像。双线性插值算法实际就是最常见的线性插值，因为将线性插值分解成水平和垂直两个方向进行，所以称为双线性插值。它通过待插值像素点周围4个相关像素点的灰度值，经过二维线性加权平均计算得到插值像素点灰度值。如图4所示，原始4个像素点为a1，a2，a3，a4，水平和垂直两个方向点距为单位长度1，其坐标位置分别为a1(x,y)，a2(x+1,y)，a3(x,y+1)，a4(x+1,y+1)，待插值像素点坐标为P(x+dx,y+dy)，用双线性插值法计算得到待插值像素点P的灰度值为：(4) The image bilinear reduction module uses a bilinear interpolation algorithm to calculate the interpolation coefficient and the corresponding pixel point coordinates according to the reduction factor, and then interpolates the image, and finally outputs the reduced image. The bilinear interpolation algorithm is actually the most common linear interpolation, because linear interpolation is decomposed into two directions, horizontal and vertical, so it is called bilinear interpolation. It calculates the gray value of the interpolated pixel point through the two-dimensional linear weighted average calculation through the gray value of the four relevant pixel points around the pixel to be interpolated. As shown in Fig. 4, the original 4 pixel points are a1, a2, a3, a4, and the horizontal and vertical points are 1 unit length, and their coordinate positions are a1(x, y), a2(x+1). , y), a3(x, y+1), a4(x+1, y+1), the coordinates of the pixel to be interpolated are P(x+dx, y+dy), which are calculated by bilinear interpolation The gray value of the interpolated pixel point P is:
P(x+dx,y+dy)＝(1-dx)(1-dy)a1+dx(1-dy)a2+(1-dx)dya3+dxdya4＝a1+dx(a2-a1)+dy(a3-a1)+dxdy(a1-a2-a3+a4)P(x+dx,y+dy)=(1-dx)(1-dy)a1+dx(1-dy)a2+(1-dx)dya3+dxdya4=a1+dx(a2-a1)+dy( A3-a1)+dxdy(a1-a2-a3+a4)
计算公式经过改写后，只需4次乘法和简单的加减法即可计算出待插值的像素点。After the calculation formula is rewritten, only 4 times of multiplication and simple addition and subtraction can be used to calculate the pixel to be interpolated.
图像缩小的处理流程，如图3所示。首先控制模块根据输入输出图像大小计算缩小倍数，然后根据缩小的倍数，控制选通不同的模块。The process of image reduction is shown in Figure 3. First, the control module calculates the reduction factor according to the input and output image size, and then controls the different modules according to the reduced multiple.
1)当缩小倍数介于1-2之间时，适合直接使用图像双线性缩小模块，图像输入后，bypass图像均值缩小模块和图像边缘增强模块，然后进入图像双线性缩小模块进行缩小处理后输出。1) When the reduction factor is between 1-2, it is suitable to directly use the image bilinear reduction module. After the image is input, the bypass image mean reduction module and the image edge enhancement module are then entered into the image bilinear reduction module for reduction processing. After the output.
2)当缩小整数倍时，如缩小2、3、4倍等，图像输入后，根据缩小倍率使用图像均值缩小模块进行整数倍缩小，然后进入图像边缘增强模块进行边缘信息增强，最后bypass图像双线性缩小模块后输出。2) When reducing the integer multiple, such as reducing 2, 3, 4 times, etc., after the image is input, use the image mean reduction module to perform integer multiple reduction according to the reduction magnification, and then enter the image edge enhancement module to enhance the edge information, and finally the bypass image double Linearly shrinks the module and outputs it.
3)当缩小倍数介于2-3或3-4之间，图像输入后，根据缩小倍率使用图像均值缩小模块进行区间内较大的整数倍缩小，然后进入图像边缘增强模块进行边缘信息增强，之后根据整数倍缩小后的大小重新计算新的缩小倍数，最后进 入图像双线性缩小模块处理后输出。3) When the reduction factor is between 2-3 or 3-4, after the image is input, the image mean reduction module is used to perform a larger integer multiple reduction in the interval according to the reduction magnification, and then enter the image edge enhancement module to perform edge information enhancement. Then recalculate the new reduction factor based on the size of the integer multiple reduction, and finally The image is output after the image is processed by the bilinear reduction module.
举例说明，输入图像分辨率为1024x768，输出图像分辨率要求320x240。首先计算缩小比例，320/1024＝0.3125，则缩小倍数(为缩小比例的倒数)介于2-3之间，所以使用缩小倍数2进行图像均值缩小，缩小后图像大小为512x384，然后进入图像边缘增强模块进行边缘信息增强。接下来计算新的缩小比例，320/512＝0.625，则缩小倍数介于1-2之间，进入图像双线性缩小模块进行再次缩小后输出，得到最终输出图像。For example, the input image resolution is 1024x768, and the output image resolution is 320x240. First calculate the reduction ratio, 320/1024=0.3125, then reduce the multiple (the reciprocal of the reduction ratio) between 2-3, so use the
本实施例中，通过根据图像的输入图像大小计算待输出的输出图像大小的缩小倍数；对缩小倍数进行判断，若缩小倍数大于2且小于3，或者，缩小倍数大于3且小于4，则对图像依次进行图像均值缩小处理、图像边缘增强处理、图像双线性缩小处理；若缩小倍数为整数，则对图像依次进行图像均值缩小处理、图像边缘增强处理；输出处理后的图像，解决了目前的在较少的硬件资源下，无法实现较好的图像缩小效果成为图像处理的技术问题。In this embodiment, the reduction factor of the output image size to be output is calculated according to the input image size of the image; and the reduction factor is determined, if the reduction factor is greater than 2 and less than 3, or the reduction factor is greater than 3 and less than 4, then The image sequentially performs image mean reduction processing, image edge enhancement processing, and image bilinear reduction processing; if the reduction factor is an integer, the image average reduction processing and image edge enhancement processing are sequentially performed on the image; and the processed image is output, and the current image is solved. With less hardware resources, the inability to achieve better image reduction effects becomes a technical problem in image processing.
请参阅图2，本发明实施例提供的一种图像缩小装置的一个实施例包括：Referring to FIG. 2, an embodiment of an image reduction apparatus according to an embodiment of the present invention includes:
缩小单元201，用于根据图像的输入图像大小计算待输出的输出图像大小的缩小倍数；The reducing
判断单元202，用于对缩小倍数进行判断，若缩小倍数大于2且小于3，或者，缩小倍数大于3且小于4，则触发第一执行单元203；还用于若缩小倍数为整数，则触发第二执行单元204；The determining
第一执行单元203，用于触发图像均值缩小模块a、图像边缘增强模块b、图像双线性缩小模块c对图像依次进行图像均值缩小处理、图像边缘增强处理、图像双线性缩小处理；The
第二执行单元204，用于触发图像均值缩小模块a、图像边缘增强模块b对图像依次进行图像均值缩小处理、图像边缘增强处理。
The
判断单元202，还用于若缩小倍数大于1且小于2，则触发第三执行单元205；The determining
第三执行单元205，用于触发所述图像双线性缩小模块直接对图像进行图像双线性缩小处理。The third executing
输出单元206，用于输出处理后的图像。The
需要说明的是，图像均值缩小模块a，用于对图像进行相邻行的像素点均值计算。It should be noted that the image mean reduction module a is used for performing pixel point mean calculation of adjacent lines on the image.
图像边缘增强模块b，用于根据图像相邻点的像素点信息，计算出图像边缘位置，并把图像边缘信息增强补偿。The image edge enhancement module b is configured to calculate an image edge position according to pixel point information of an adjacent point of the image, and enhance the image edge information to compensate.
图像双线性缩小模块c，用于根据缩小倍数计算插值系数和对应像素点坐标，再对图像进行插值计算。The image bilinear reduction module c is configured to calculate an interpolation coefficient and a corresponding pixel point coordinate according to the reduction factor, and then perform interpolation calculation on the image.
解决了目前的图像缩小都以空间特征为主，由于目前的图像缩小算法的复杂度高，导致了准确率低的技术问题。The current image reduction is mainly based on spatial features. Due to the high complexity of the current image reduction algorithm, the technical problem of low accuracy is caused.
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统，装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。A person skilled in the art can clearly understand that for the convenience and brevity of the description, the specific working process of the system, the device and the unit described above can refer to the corresponding process in the foregoing method embodiment, and details are not described herein again.
在本申请所提供的几个实施例中，应该理解到，所揭露的系统，装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。In the several embodiments provided by the present application, it should be understood that the disclosed system, apparatus, and method may be implemented in other manners. For example, the device embodiments described above are merely illustrative. For example, the division of the unit is only a logical function division. In actual implementation, there may be another division manner, for example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be in an electrical, mechanical or other form.
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者 也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。The unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, may be located in one place, or It can also be distributed to multiple network elements. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
另外，在本发明各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。The integrated unit, if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention, which is essential or contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product stored in a storage medium. A number of instructions are included to cause a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .
以上所述，以上实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围。 The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to be limiting; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that The technical solutions described in the embodiments are modified, or the equivalents of the technical features are replaced by the equivalents of the technical solutions of the embodiments of the present invention.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112034676A (en) * | 2020-09-25 | 2020-12-04 | 上海华力微电子有限公司 | Pretreatment method of pixel graph |
| CN114820313A (en) * | 2022-04-26 | 2022-07-29 | 深圳鲲云信息科技有限公司 | Image scaling device, method, equipment and storage medium based on data stream architecture |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106780336B (en) * | 2016-12-19 | 2020-04-03 | 广东威创视讯科技股份有限公司 | Image reduction method and device |
| CN110335198B (en) * | 2019-07-08 | 2021-06-08 | 威创集团股份有限公司 | Image processing method and system |
| CN112164109B (en) * | 2020-07-08 | 2024-09-06 | 浙江大华技术股份有限公司 | Coordinate correction method, coordinate correction device, storage medium and electronic device |
| CN113034367B (en) * | 2021-03-29 | 2024-04-16 | 京东科技控股股份有限公司 | Information processing method, information processing device, computer system and readable storage medium |
| CN113989114B (en) * | 2021-10-08 | 2025-12-16 | 广东卓维网络有限公司 | Image reduction processing method and device |
| CN116012223A (en) * | 2021-10-21 | 2023-04-25 | 广州视源电子科技股份有限公司 | Image resolution reduction method, system, device and storage medium |
| CN116416128A (en) * | 2021-12-31 | 2023-07-11 | 北京字跳网络技术有限公司 | An image down-sampling method and device |
| CN118072684A (en) * | 2023-11-09 | 2024-05-24 | 中国航空工业集团公司洛阳电光设备研究所 | A simulation display system based on reverse bilinear interpolation |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101296338A (en) * | 2008-06-11 | 2008-10-29 | 四川虹微技术有限公司 | Image scaling method and device |
| CN102186044A (en) * | 2010-06-22 | 2011-09-14 | 上海盈方微电子有限公司 | Edge correlation image stepless scaling algorithm and hardware realization device thereof |
| US20150235345A1 (en) * | 2014-02-17 | 2015-08-20 | Delta Electronics, Inc. | Method and device for processing a super-resolution image |
| CN106780336A (en) * | 2016-12-19 | 2017-05-31 | 广东威创视讯科技股份有限公司 | A kind of image downscaling method and device |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102282586B (en) * | 2009-01-19 | 2017-05-10 | 诺基亚技术有限公司 | Method and apparatus for reducing size of image data |
| CN101923704B (en) * | 2009-06-12 | 2012-09-05 | 深圳市融创天下科技股份有限公司 | Adaptive image scaling method |
| CN102547068B (en) * | 2011-12-31 | 2014-03-05 | 中山大学 | Improved Bilinear Interpolation Video Scaling Method |
| CN103310820B (en) * | 2012-03-14 | 2016-03-09 | 三星电子（中国）研发中心 | A kind of method that multimedia player is optimized |
| CN103034973B (en) * | 2012-12-05 | 2015-08-12 | 焦点科技股份有限公司 | Based on the adaptive image scaling method of bicubic interpolation |
| TWI531852B (en) * | 2014-09-16 | 2016-05-01 | 聚晶半導體股份有限公司 | Device of capturing images and method of digital focusing |
| CN104700360B (en) * | 2015-04-01 | 2018-06-05 | 北京思朗科技有限责任公司 | Image-scaling method and system based on edge self-adaption |
-
2016
- 2016-12-19 CN CN201611178321.6A patent/CN106780336B/en active Active
-
2017
- 2017-06-23 WO PCT/CN2017/089698 patent/WO2018113224A1/en not_active Ceased
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101296338A (en) * | 2008-06-11 | 2008-10-29 | 四川虹微技术有限公司 | Image scaling method and device |
| CN102186044A (en) * | 2010-06-22 | 2011-09-14 | 上海盈方微电子有限公司 | Edge correlation image stepless scaling algorithm and hardware realization device thereof |
| US20150235345A1 (en) * | 2014-02-17 | 2015-08-20 | Delta Electronics, Inc. | Method and device for processing a super-resolution image |
| CN106780336A (en) * | 2016-12-19 | 2017-05-31 | 广东威创视讯科技股份有限公司 | A kind of image downscaling method and device |
Non-Patent Citations (1)
| Title |
|---|
| CHEN, SUQIONG ET AL.: "Design and Realization of Multiple-screen Function of Digital Television", CABLE TELEVISION TECHNOLOGY, vol. 4, 30 April 2006 (2006-04-30), pages 42 - 43 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112034676A (en) * | 2020-09-25 | 2020-12-04 | 上海华力微电子有限公司 | Pretreatment method of pixel graph |
| CN112034676B (en) * | 2020-09-25 | 2024-03-08 | 上海华力微电子有限公司 | Pretreatment method of pixel graph |
| CN114820313A (en) * | 2022-04-26 | 2022-07-29 | 深圳鲲云信息科技有限公司 | Image scaling device, method, equipment and storage medium based on data stream architecture |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106780336A (en) | 2017-05-31 |
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