Intra prediction mode selection method for 3D video depth map based on Bayesian criterion

A technology of intra-frame prediction mode and intra-frame mode, which is applied in the field of video coding and decoding, can solve the problems of reduced complexity, without considering the high proportion of SDM and low time consumption, etc., to achieve reduced complexity, good coding performance, and reduced The effect of encoding complexity

Inactive Publication Date: 2019-10-18
NANJING UNIV OF SCI & TECH
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Problems solved by technology

However, the existing encoding technology does not take into account the high proportion and low time consumption of SDM in the depth map intra mode. In other words, the complexity of the existing depth map intra mode selection algorithm can still be further reduced.

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  • Intra prediction mode selection method for 3D video depth map based on Bayesian criterion
  • Intra prediction mode selection method for 3D video depth map based on Bayesian criterion
  • Intra prediction mode selection method for 3D video depth map based on Bayesian criterion

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Embodiment

[0073] In this embodiment, the flow of the fast selection algorithm for the intra-frame prediction mode of the 3D video depth image based on the Bayesian classifier is as follows: figure 1 As shown, the steps include:

[0074] Step 1: For the input video sequence, judge whether the current coding frame is a learning frame (used as a training set), if yes, then proceed to step 2, if not, then skip to step 3;

[0075] Step 2: Carry out the model learning process, that is, use the training set data to train the Bayesian classification model. Finally return to step 1;

[0076] The training data includes P(S 0 ), P(S 1 ), P(x|S 1 ), P(x|S 0 ), SDM cost and CIM cost . P(S 0 ), P(S 1 ) represent category S respectively 0 , S 1 the prior probability of . x = SDM cost , P(x|S 1 ), P(x|S 0 ) represents the likelihood function of the two categories, and its calculation method is as in formula (1). while SDM cost and CIM cost Indicates the minimum rate-distortion cost f...

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Abstract

The invention discloses a 3D video depth map intra-frame prediction mode selection method based on the Bayes criterion. The method comprises the following steps that firstly a training set is obtained from the intra-frame coding prediction mode data of previous multiple coded frames and used for training a Bayesian dichotomy model; then the RD-Cost value of the SDM mode of the given PU of frames to be coded acts as the input characteristic of a trained classifier, and the PU is divided into two classes including S0 and S1; as for the PU belonging to S0, the single depth mode is the final optimal intra-frame prediction mode and mode selection is terminated in advance; and as for the PU belonging to S1, the encoder performs conventional intra-frame mode selection. The depth map intra-frame prediction coding complexity can be effectively reduced so that coding time required for intra-frame prediction can be reduced; besides, the coding speed can be enhanced and the video quality of the final decoding end synthesis perspective can be guaranteed.

Description

technical field [0001] The invention belongs to the technical field of video encoding and decoding, and in particular relates to a method for selecting an intra-frame prediction mode of a 3D video depth map based on a Bayesian criterion. Background technique [0002] The emerging video format of multi-view plus depth map is the most important format of the next generation 3D video system. It uses texture map information of a small number of viewpoints and additional depth map information of corresponding viewpoints to represent a 3D video scene, and more viewpoint information can be synthesized by 3D rendering technology based on depth images. Since the depth map plays a key role in providing disparity information and guiding the synthesis process in current 3D video systems, the encoding of the depth map should be extremely rigorous. Therefore, some new techniques such as Single Depth Mode (SDM), Depth Modeling Mode (DMM), Segmental DC Coding (SDC) and View Synthesis Optim...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04N19/597H04N19/11H04N19/149
Inventor 伏长虹陈浩张洪彬赵亚文杨梦梦高梽强汪海燕王瑾
Owner NANJING UNIV OF SCI & TECH
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