CU segmentation prediction and mode decision texture coding method based on JND model

Abstract

The invention provides a CU segmentation prediction and mode decision texture coding method based on a JND model. The method comprises the following steps: firstly starting a mode decision, and constructing a multi-view texture JND model of a texture video through a brightness JND model, a space JND model and a time JND model; secondly, setting a threshold value of a multi-view texture JND model according to the content of the texture video, and dividing tree blocks of the texture video into three types according to the threshold value; performing adaptive CU segmentation on the current tree block according to the type of the current tree block; and finally, according to the type to which the current tree block belongs, performing fast mode decision prediction on the tree block segmented by the adaptive CU, and determining the optimal coding mode of the tree block. The JND model is used to analyze the tree block features of the texture image, some tree blocks of the texture video are skipped in the early stage, the coding complexity of 3D-HEVC can be significantly reduced, and the loss of the RD performance of the video can be ignored.

Description

Technical field [0001] The present invention relates to the technical field of image processing, in particular to a CU segmentation prediction and mode decision texture coding method based on a JND model. Background technique [0002] In the past few years, with the development of stereoscopic displays and related applications such as 3D TV (3DTV), FTV, 3D games and 3D movies, 3D video has become more and more popular. In order to improve the coding efficiency of multi-texture video, the latest video standard HEVC has developed 3D-HEVC, and other coding tools have been designed using the correlation between components to effectively compress texture video data. The official 3D-HEVC reference software can save 46% of the bitrate compared to HEVC for 3D video content. In addition to the traditional video encoder HEVC, significant compression improvements can also be obtained through several encoding tools, including: Neighbor block disparity vector (NBDV)", "inter-view motion pred...

AI Technical Summary

Problems solved by technology

[0005] In view of the deficiencies in the above-mentioned background technology, the present invention proposes a CU segmentation prediction and mode decision texture coding method based on the JND model, which solves the problem that the existing coding technology does not combine the JND model and the perceptual characteristics of the HVS, resulting in complicated coding. high technical issues

Images