Joint estimation method for movement and parallax error in multi-view video coding

Abstract

A joint estimation method for movement and parallax error in multi-view video coding of the invention has the following steps: 1. taking parallax error vector and movement vector of image corresponding block of same view and adjacent moment as the initial value of the current coding block parallax error vector; 2. comparing the initial value with the prediction vectors of coded adjacent blocks respectively, and selecting the optimal prediction vector as the search initial point according to the match error minimum rule; and 3. combining geometric relationship between the movement vector and parallax error vector of adjacent images, estimating and obtaining a candidate from a previous movement / parallax error to perform the next movement / parallax error, correcting the current movement and parallax error vector continuously until obtaining the optimal movement vector and parallax error vector of the current coding block. The method only needs one time search process to simultaneously confirm the optimal movement vector and parallax error vector. Compared with full search algorithm, the peak SNR of the invention reduces not more than 0.09dB, code rate bits are slightly saved, code rate ranges between minus 14.20% and 0.60% and coding time is saved by over 90%.

Description

technical field [0001] The invention relates to the field of H.264-based multi-viewpoint video coding, in particular to a motion and parallax joint estimation algorithm in multi-viewpoint video coding. Background technique [0002] Multi-viewpoint video contains the depth information of the scene, and is more realistic in the representation of natural scenes. It has broad application prospects in the fields of 3D TV, free-viewpoint TV, visual conference with a sense of presence, and virtual reality [1] . The multi-viewpoint video technology with "three-dimensional" and "interactive" is getting more and more attention from academia and industry, and has become one of the research hotspots in recent years. [0003] Compared with traditional single-viewpoint video, the amount of data to be processed by multi-viewpoint video doubles with the increase of the number of cameras, which brings a huge burden to transmission and decoding. Therefore, how to efficiently compress and en...

AI Technical Summary

Problems solved by technology

Ding et al. [5] used the high similarity of the corresponding blocks of adjacent views to find the corresponding blocks in the reference frames of adjacent views through disparity estimation. The coded information such as the mode and motion vector of the coded view can be used again, but this The method is only for motion estimation, and the disparity estimation still uses the full search method

[0005] To sum up, the current fast algorithms for motion estimation and disparity estimation are relatively independent, and do not make full use of the characteristics of multi-viewpoint video, and combine the relationship between adjacent viewpoints to design a joint motion and disparity estimation algorithm to reduce motion estimation and disparity at the same time. estimated complexity

Therefore, most of the encoding time is still wasted on motion estimation and disparity estimation

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