Multipassl video coding rate allocation and control optimization method based on reinforcement learning

A technology of reinforcement learning and video coding, which is applied in the field of video coding and deep learning, can solve problems such as the inability to obtain the global optimal solution and the difficulty of generating labels by machine learning methods, achieve good practical significance and industrial value, improve coding compression efficiency, The effect of improving compression efficiency

Active Publication Date: 2019-10-18
杭州微帧信息科技有限公司
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Problems solved by technology

The invention solves the problem that the traditional method cannot obtain the global optimal solution, and the machine learning method is difficult to generate labels for training

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  • Multipassl video coding rate allocation and control optimization method based on reinforcement learning
  • Multipassl video coding rate allocation and control optimization method based on reinforcement learning

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[0022] The following examples will be used in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0023] The optimization method of code rate allocation and control in video multiple encoding of the present invention specifically optimizes the code control allocation and control strategy parameters of the second traversal of video encoding, including the following steps,

[0024] Step (1), create prediction network and discriminant network, and complete network parameter initialization.

[0025] The prediction network is a fully connected neural network, which is responsible for determining the code rate allocation in the picture group and controlling the corresponding code rate allocation and control strategy parameter optimal value based on the comprehensive statistical information generated by the first tra...

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Abstract

The invention discloses an optimization solution for code rate allocation and control in multi-pass video coding based on reinforcement learning. After the end of the first encoding, the video is divided into a plurality of non-overlapping consecutive picture groups. For comprehensive statistical information of each frame of image in different picture groups acquired after first pass, code rate allocation and control decision parameters are obtained through a prediction network, and second pass coding is executed and a coding result score is obtained. And the statistical information and the code control decision parameter are input into a discrimination network to obtain an estimated score, and an iterative training process of reinforcement learning is executed. The prediction network obtained by reinforcement learning training is utilized to obtain the optimal strategy parameters of code rate allocation and control of each picture group before the second pass of the coded video image,so that the coding compression efficiency is improved to the greatest extent.

Description

technical field [0001] The invention relates to video coding and deep learning, in particular to an optimization method for multi-traversal video coding code rate allocation and control based on reinforcement learning. Background technique [0002] With the continuous development of multimedia digital video applications and the continuous improvement of people's demand for video cloud computing, the data volume of the original video source makes the existing transmission network bandwidth and storage resources unbearable. Therefore, the compression of video signal has become one of the hot spots of academic research and industrial application at home and abroad. Video compression, also known as video coding, aims to eliminate redundant information between video signals. So far, domestic and foreign standardization organizations have successively formulated a variety of different video coding standards. Since the H.261 video coding standard, the mainstream video coding stan...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04N19/115H04N19/146
CPCH04N19/115H04N19/146
Inventor 朱政陈宇梅元刚丁丹丹
Owner 杭州微帧信息科技有限公司
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