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An optimized video transcoding method from avs to hevc based on support vector machine

A support vector machine and video transcoding technology, which is applied in digital video signal modification, electrical components, image communication, etc., can solve the problems of high implementation complexity and no coding information, so as to improve the transcoding speed and reduce the complexity of transcoding speed and save transcoding time

Inactive Publication Date: 2018-01-02
SHANGHAI JIAOTONG UNIV
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Problems solved by technology

However, the input of this technology is the original code stream, and the encoding information contained in the compressed code stream cannot be used. Moreover, for HEVC’s more complex encoders, the encoding paths are diverse, and the implementation complexity will be relatively high.

Method used

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  • An optimized video transcoding method from avs to hevc based on support vector machine
  • An optimized video transcoding method from avs to hevc based on support vector machine
  • An optimized video transcoding method from avs to hevc based on support vector machine

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Experimental program
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Embodiment 1

[0020] like figure 2 As shown, this embodiment is divided into the following four steps:

[0021] Step 1. Collecting the feature vectors of the AVS code stream, specifically: collecting the CU division information of corresponding positions in the AVS code stream and corresponding HEVC depths of 0 and 1.

[0022] The AVS feature vector is collected from the AVS code stream, including information such as macroblock coding mode, motion vector and transformation coefficient.

[0023] The macroblock coding mode refers to: when the depth of HEVC corresponding to AVS is 0, each CU contains 16 macroblocks, and therefore contains 16 features; when the depth is 1, each CU contains There are 4 macroblocks, corresponding to 4 mode features.

[0024] The motion vector refers to: the motion vector in the AVS is 8×8, and a macroblock contains 4 motion vector basic units, and each motion vector is respectively moduloed, and then the four motion vectors in a macroblock are The mean value ...

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Abstract

An optimized video transcoding method from AVS to HEVC based on support vector machine, by collecting the feature vector of AVS code stream, and using the support vector machine to learn it and obtain the training model, the extracted AVS feature vector is divided into HEVC The CU at the corresponding position in the CU can be divided or not divided into two categories. In the transcoding stage, the training model is used to predict whether the CU needs to be divided. When the current CU needs to be divided, the 2N×2N mode and the SKIP mode are calculated at the current HEVC depth. , and select the optimal prediction mode from these two modes. When it is predicted that the current CU does not need to be divided, the optimal mode selection is performed according to the HEVC standard encoding process. The present invention combines the basic idea of ​​machine learning, divides the entire transcoding process into a training phase and a transcoding phase, obtains a training model through learning, predicts the division of CUs in HEVC, and combines the fast mode selection algorithm to improve the speed of transcoding , and ensure the overall video quality of the transcoded video.

Description

technical field [0001] The present invention relates to a technology in the field of video signal processing, in particular to an optimized video transcoding method from AVS to HEVC based on a support vector machine. Background technique [0002] Video transcoding technology is to decode and re-encode the compressed code stream to obtain the target code stream that meets the requirements. With the wide application and rapid development of multimedia technology and the Internet, the transmission of various video data on the network has become the development trend of network technology. At present, a variety of video coding standards have emerged, including MPEG-4, MPEG-2, H.264, AVS, HEVC, etc. Due to the variety of video resources, and the differences in the display capabilities, storage capabilities, and processing capabilities of different terminal devices, users have different requirements for video in different scenarios. Therefore, how to achieve efficient conversion ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04N19/40H04N19/147H04N19/103
Inventor 解蓉罗瑞张文军张良
Owner SHANGHAI JIAOTONG UNIV
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