CN111193911B - Fast transmission processing method and device for big data video - Google Patents
Fast transmission processing method and device for big data video Download PDFInfo
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- 230000005540 biological transmission Effects 0.000 title claims abstract description 38
- 238000003672 processing method Methods 0.000 title claims description 12
- 238000012545 processing Methods 0.000 claims abstract description 38
- 238000000034 method Methods 0.000 claims abstract description 16
- 238000004458 analytical method Methods 0.000 claims abstract description 15
- 239000011159 matrix material Substances 0.000 claims abstract description 13
- 230000011218 segmentation Effects 0.000 claims abstract description 9
- 238000000605 extraction Methods 0.000 claims abstract description 5
- 230000001276 controlling effect Effects 0.000 claims description 11
- 230000001105 regulatory effect Effects 0.000 claims description 10
- 230000000593 degrading effect Effects 0.000 claims description 5
- 238000003709 image segmentation Methods 0.000 claims description 5
- 230000006870 function Effects 0.000 claims description 4
- 238000013135 deep learning Methods 0.000 claims description 2
- 238000007781 pre-processing Methods 0.000 abstract description 7
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/234—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs
- H04N21/2343—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs involving reformatting operations of video signals for distribution or compliance with end-user requests or end-user device requirements
- H04N21/234363—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs involving reformatting operations of video signals for distribution or compliance with end-user requests or end-user device requirements by altering the spatial resolution, e.g. for clients with a lower screen resolution
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/262—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists
- H04N21/26208—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists the scheduling operation being performed under constraints
- H04N21/26216—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists the scheduling operation being performed under constraints involving the channel capacity, e.g. network bandwidth
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/266—Channel or content management, e.g. generation and management of keys and entitlement messages in a conditional access system, merging a VOD unicast channel into a multicast channel
- H04N21/2662—Controlling the complexity of the video stream, e.g. by scaling the resolution or bitrate of the video stream based on the client capabilities
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- Compression Or Coding Systems Of Tv Signals (AREA)
Abstract
本发明公开了一种大数据视频的速传处理方法和装置，步骤为：获取视频中的第i帧图像，对所述第i帧图像同时进行分割预处理和图像预处理；所述分割预处理将所述图像分割为分割图像和区域信息，所述图像预处理对所述图像进行纹理分析、边缘提取及分析获取图像细节信息；对获取的图像区域质量矩阵进行图像区块划分，获得图像区块和对应质量数据，遍历并分析区块质量数据，及降低、合并区块质量，生成金字塔区块；依据网络实况及用户需求实时调控所述区块码流的传输，在不影响视频效果的前提下，大大缩小了所述视频的传输流量，提高了传输速度，满足了人们观看大数据视频和高质量视频时实时传输的需求。
The invention discloses a method and device for fast transmission processing of big data video. The steps are as follows: acquiring the i-th frame image in the video, and simultaneously performing segmentation preprocessing and image preprocessing on the i-th frame image; The process divides the image into segmented images and area information, and the image preprocessing performs texture analysis, edge extraction and analysis on the image to obtain image detail information; the obtained image area quality matrix is divided into image blocks to obtain an image Blocks and corresponding quality data, traverse and analyze block quality data, reduce and combine block quality, and generate pyramid blocks; real-time control the transmission of the block code stream according to the network situation and user needs, without affecting the video effect On the premise that the video transmission flow is greatly reduced, the transmission speed is improved, and the real-time transmission requirements of people watching big data video and high-quality video are met.
Description
Technical Field
The invention relates to the technical field of video transmission, in particular to a method and a device for processing fast transmission of big data video.
Background
Video technology has been applied to many fields, on the one hand, use traffic supervision, the real time monitoring of safety supervision, on the other hand, use trades such as propaganda, investigation, tourism, utilize the aircraft to carry on 360 degrees panorama shots of camera such as mountain and lake, the historical sites of the attraction, important geographic features etc., it is shooting with 360 degrees of square camera to shoot the best effect, video speed and focus after the shooting can change, satisfy people to the demand of definition and speed, but because the data bulk of video is very big, because bandwidth and other network reasons, the jamming phenomenon is serious when causing the video to play, can not reach real-time smooth broadcast video purpose, consequently, can not satisfy the demand that people took the video and watched while.
Disclosure of Invention
In order to solve the problems, the invention provides a method and a device for processing the fast transmission of the big data video, which greatly reduce the transmission flow of the video, improve the transmission speed and meet the real-time transmission requirement when people watch the big data video and the high quality video by carrying out gridding processing on different types of video frames, carrying out block division on grids according to the complexity of texture characteristics and carrying out merging and stream control on the blocks.
The specific technical scheme provided by the invention is as follows:
a fast transmission processing method of big data video comprises the following steps:
step 1: acquiring an ith frame image in a video, and simultaneously performing segmentation pretreatment and image pretreatment on the ith frame image, wherein the segmentation pretreatment is used for segmenting the image into a segmented image and area information, and the image pretreatment is used for performing texture analysis, edge extraction and analysis on the image to acquire image detail information;
step 2: dividing image blocks of the obtained image area quality matrix to obtain image blocks and corresponding quality data;
and step 3: traversing and analyzing the block quality data, reducing the block quality according to a principle, and generating a block quality matrix;
and 4, step 4: traversing the blocks and combining the blocks according to the principle to generate a pyramid block;
and 5: and regulating and controlling the form and transmission of the block code stream in real time according to the network live condition and the user requirements, so that the video is smoothly played.
Preferably, step 1 further comprises: and performing image segmentation by using deep learning, extracting texture features and edges of the image, and performing significance analysis on the image.
Preferably, step 2 further comprises:
dividing the image into a plurality of levels of unit blocks, wherein the size of the unit blocks is changed in proportion and is matched with a block preset according to the size of the image; and determining a quality characteristic value according to the complexity of the texture characteristic of the image, wherein the quality characteristic value changes in proportion and is matched with a quality step threshold preset according to the image.
Preferably, step 3 further comprises: the forming method of the multilevel unit block comprises the following steps:
and traversing and identifying the quality feature type of each scene, respectively forming the high-quality scenes with complex texture features into small unit blocks, forming the scenes with medium-complex and medium-quality texture features into medium unit blocks, and forming the low-quality scenes with simple texture features into large unit blocks.
Preferably, if a high-quality cell block is surrounded by low-quality cell blocks, the high-quality cell block is degraded to have the same level as the surrounding low-quality cell blocks, and a final block quality matrix is generated through traversal.
Preferably, step 4 further comprises: the method for respectively combining the unit blocks according to the quality comprises the following steps:
merging adjacent, equal and close unit blocks: respectively merging the unit blocks with low quality, the unit blocks with medium quality and the unit blocks with high quality to form corresponding blocks;
merging the degraded high-quality unit block with the surrounding low-quality unit blocks.
Preferably, traversing the blocks and forming the first-level block data on the pyramid according to the number of the low-quality blocks, the medium-quality blocks and the high-quality blocks to generate the pyramid blocks.
Preferably, step 5 further comprises:
regulating and controlling the form and transmission of block code streams in real time according to the network bandwidth, the real-time network condition and the definition requirement of a user target; and when the network can not meet the transmission requirement but meets the definition of the user target, degrading the high-quality block code stream and transmitting the high-quality block code stream by using the low-quality block code stream.
Preferably, the method for regulating and controlling the code stream in real time further comprises:
and further combining the high-quality blocks to form the high-quality large block, thereby reducing the flow control.
The invention also includes a fast transmission processing device of big data video, the device includes:
a processor:
the method comprises an identification unit: the recognition function is provided, and object recognition is carried out according to the texture features of the ith frame of image;
an image processing unit: carrying out image segmentation processing and image quality analysis processing on the ith frame of image;
a block processing unit: respectively combining adjacent unit blocks with equal quality and similar different quality grades to form corresponding blocks;
a code stream control unit: and regulating and controlling the transmission state of the code stream in real time.
The invention has the beneficial effects that: by providing the fast transmission processing method and the fast transmission processing device for the big data video, the video frames are subjected to gridding processing according to different object characteristics and scene characteristics, grids are divided into different quality blocks according to the complexity of texture characteristics, and then the blocks of different types are combined respectively to form a multi-stage block pyramid, so that the purpose of controlling the stream is achieved.
Drawings
FIG. 1 is a schematic view of a processing apparatus according to the present invention;
FIG. 2 is a flow chart of a processing side of the present invention.
Wherein: 1-an identification unit; 2-an image processing unit; 3-a block processing unit; 4-code stream control unit.
Detailed Description
As used in the specification and in the claims, certain terms are used to refer to particular components. As one skilled in the art will appreciate, manufacturers may refer to a component by different names. This specification and claims do not intend to distinguish between components that differ in name but not function. As used in the following description and in the claims, the terms "include," "include," and "comprise" are used in an open-ended fashion, and thus should be interpreted to mean "include, but not limited to,"; "multistage" should be interpreted as "more than or equal to three"; the description which follows is a preferred embodiment of the present application, but is made for the purpose of illustrating the general principles of the application and not for the purpose of limiting the scope of the application. The protection scope of the present application shall be subject to the definitions of the appended claims.
As shown in fig. 1-2, in one embodiment, there is provided a fast transmission processing apparatus for big data video, the apparatus comprising:
a processor:
comprising an identification unit 1: the recognition function is provided, and object recognition is carried out according to the texture features of the ith frame of image;
the image processing unit 2: carrying out image segmentation processing and image quality analysis processing on the ith frame of image;
the block processing unit 3: respectively combining adjacent unit blocks with equal quality and similar different quality grades to form corresponding blocks;
code stream control unit 4: and regulating and controlling the transmission state of the code stream in real time.
The invention provides a fast transmission processing method of big data video, which comprises the following steps:
step 1: the identification unit 1 acquires an ith frame image of a video, the image processing unit 2 simultaneously performs segmentation preprocessing and image preprocessing on the ith frame image, the segmentation preprocessing divides the image into a segmented image and area information, and the image preprocessing performs texture analysis, edge extraction and analysis on the image to acquire image detail information;
preferably, step 1 further comprises: the image processing unit 2 performs image segmentation using depth learning, extracts texture features and edges of the image, and performs saliency analysis on the image.
Step 2: the block processing unit 3 divides the image blocks of the acquired image area quality matrix according to the area quality and a blocking principle to obtain image blocks and corresponding quality data;
the block processing unit 3 divides the image into a plurality of levels of unit blocks, the size of the unit blocks is changed in proportion and is matched with a block preset according to the size of the image; and determining a quality characteristic value according to the complexity of the texture characteristic of the image, wherein the quality characteristic value changes in proportion and is matched with a quality step threshold preset according to the image.
It should be noted that, before processing an image, a block is preset according to the size of the image, the size of the block changes in proportion, and when the block processing unit 3 performs block multi-level division on the image, the division is performed according to the size of the preset block;
before processing an image, a quality threshold is preset according to the complexity of texture features of the image, the quality threshold is a step threshold and changes proportionally, and the block processing unit 3 determines the quality feature value of the image according to the preset step threshold.
And step 3: the block processing unit 3 traverses and analyzes the block quality data, reduces the block quality according to the principle, and repeatedly traverses until all the blocks are finished to generate a block quality matrix;
preferably, step 3 further comprises: the forming method of the multilevel unit block comprises the following steps:
the block processing unit 3 traverses and identifies the quality feature type of each scene, and respectively forms the high-quality scenes with complex texture features into small unit blocks, forms the medium unit blocks with the scenes with medium-complex and medium-quality texture features, and forms the low-quality scenes with simple texture features into large unit blocks.
Preferably, if all the surrounding high quality cell blocks are low quality cell blocks, the block processing unit 3 degrades the high quality cell blocks, and generates the final block quality matrix in the same level as the surrounding low quality cell blocks.
And 4, step 4: the block processing unit 3 traverses the blocks and merges the blocks according to the principle, and repeats traversal until all the blocks are finished to generate a pyramid block;
preferably, step 4 further comprises: the method for respectively combining the unit blocks according to the quality comprises the following steps:
the block processing unit 3 merges adjacent, equal and close unit blocks: respectively merging the unit blocks with low quality, the unit blocks with medium quality and the unit blocks with high quality to form corresponding blocks;
block processing unit 3 merges the high-quality unit blocks that have been degraded with the surrounding low-quality unit blocks.
Preferably, the block processing unit 3 traverses the blocks and forms the first-level block data on the pyramid according to the number of the low-quality blocks, the medium-quality blocks and the high-quality blocks, so as to generate the pyramid blocks.
And 5: the code stream control unit 4 regulates and controls the form and transmission of the block code stream in real time according to the network live condition and the user requirements, so that the video is smoothly played.
Preferably, step 5 further comprises:
the code stream control unit 4 regulates and controls the form and transmission of block code streams in real time according to the network bandwidth, the real-time network condition and the definition requirement of a user target; and when the network can not meet the transmission requirement but meets the definition of the user target, degrading the high-quality block code stream and transmitting the high-quality block code stream by using the low-quality block code stream.
Preferably, the method for regulating and controlling the code stream in real time further comprises:
the code stream control unit 4 further merges the plurality of high-quality blocks to form a high-quality large block, thereby reducing the flow control.
The foregoing describes several preferred embodiments of the present application, but, as noted above, it is to be understood that the application is not limited to the forms disclosed herein, but is not to be construed as excluding other embodiments and is capable of use in various other combinations, modifications, and environments and is capable of changes within the scope of the inventive concept as expressed herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the application, which is to be protected by the claims appended hereto.
Claims (8)
1. A fast transmission processing method of big data video is characterized in that: the method comprises the following steps:
step 1: acquiring an ith frame image in a video, and simultaneously performing segmentation pretreatment and image pretreatment on the ith frame image, wherein the segmentation pretreatment is used for segmenting the image into a segmented image and area information, and the image pretreatment is used for performing texture analysis, edge extraction and analysis on the image to acquire image detail information;
step 2: dividing the obtained image area quality matrix into multi-level unit blocks, wherein the size of the unit blocks is changed in proportion, and quality characteristic values are determined according to the complexity of texture characteristics of the image and are changed in proportion; obtaining image blocks and corresponding quality data;
and step 3: traversing and analyzing the block quality data, reducing the block quality according to a principle, and generating a block quality matrix;
and 4, step 4: traversing the blocks and combining the blocks according to the principle to generate a pyramid block;
and 5: regulating and controlling the form and transmission of block code streams in real time according to the network live condition and the user requirements, so that the video is smoothly played;
wherein, the reducing the block quality according to the principle specifically comprises: if the surrounding of a certain high-quality unit block is low-quality unit block, degrading the high-quality unit block, having the same level with the surrounding low-quality unit block, and traversing to generate a final block quality matrix;
the merging blocks according to the principle is to merge the adjacent unit blocks with equal and close quality: respectively merging the low-quality unit blocks, the medium-quality unit blocks and the high-quality unit blocks to form corresponding blocks; merging the degraded high-quality unit blocks with the surrounding low-quality unit blocks.
2. The big data video fast transmission processing method according to claim 1, wherein:
the step 1 further comprises: and performing image segmentation by using deep learning, extracting texture features and edges of the image, and performing significance analysis on the image.
3. The big data video fast transmission processing method according to claim 2, wherein:
the step 2 further comprises:
dividing the image into a plurality of levels of unit blocks, wherein the size of the unit blocks is changed in proportion and is matched with a block preset according to the size of the image; and determining a quality characteristic value according to the complexity of the texture characteristic of the image, wherein the quality characteristic value changes in proportion and is matched with a quality step threshold preset according to the image.
4. The big data video fast transmission processing method according to claim 3, wherein:
step 3 also includes: the forming method of the multilevel unit block comprises the following steps:
and traversing and identifying the quality feature type of each scene, respectively forming the high-quality scenes with complex texture features into small unit blocks, forming the scenes with medium-complex and medium-quality texture features into medium unit blocks, and forming the low-quality scenes with simple texture features into large unit blocks.
5. The big data video fast transmission processing method according to claim 4, wherein:
and traversing the blocks, forming primary block data on the pyramid according to the number of the low-quality blocks, the medium-quality blocks and the high-quality blocks, and generating the pyramid blocks.
6. The big data video fast transmission processing method according to claim 5, wherein:
step 5 also includes:
regulating and controlling the form and transmission of block code streams in real time according to the network bandwidth, the real-time network condition and the definition requirement of a user target; and when the network can not meet the transmission requirement but meets the definition of the user target, degrading the high-quality block code stream and transmitting the high-quality block code stream by using the low-quality block code stream.
7. The big data video fast transmission processing method according to claim 6, wherein:
the method for regulating and controlling the code stream in real time further comprises the following steps:
and further combining the high-quality blocks to form the high-quality large block, thereby reducing the flow control.
8. A fast processing apparatus that passes of big data video, its characterized in that: the device comprises:
a processor:
the method comprises an identification unit: the method has an identification function, and performs object identification according to the texture characteristics of the ith frame of image to obtain the ith frame of image in the video;
an image processing unit: performing segmentation pretreatment and image pretreatment on an ith frame of image at the same time, wherein the segmentation pretreatment is used for segmenting the image into a segmented image and area information, and the image pretreatment is used for performing texture analysis, edge extraction and analysis on the image to obtain image detail information;
a block processing unit: dividing the obtained image area quality matrix into multi-level unit blocks, wherein the size of the unit blocks is changed in proportion, and quality characteristic values are determined according to the complexity of texture characteristics of the image and are changed in proportion; obtaining image blocks and corresponding quality data;
traversing and analyzing the block quality data, reducing the block quality according to a principle, and generating a block quality matrix;
traversing the blocks and combining the blocks according to the principle to generate a pyramid block;
a code stream control unit: regulating and controlling the form and transmission of block code streams in real time according to the network live condition and the user requirements, so that the video is smoothly played;
wherein, the reducing the block quality according to the principle specifically comprises: if the surrounding of a certain high-quality unit block is low-quality unit block, degrading the high-quality unit block, having the same level with the surrounding low-quality unit block, and traversing to generate a final block quality matrix;
the merging blocks according to the principle is to merge the adjacent unit blocks with equal and close quality: respectively merging the low-quality unit blocks, the medium-quality unit blocks and the high-quality unit blocks to form corresponding blocks; merging the degraded high-quality unit blocks with the surrounding low-quality unit blocks.
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