Abstract
The emerging 3D-HEVC has achieved the highest coding efficiency but requires a very high computational complexity. To speed up the encoding process for the dependent texture views, we propose a fast CU depth range selection algorithm by jointly making use of the interview and temporal-spatial correlations. Firstly, adaptive correlation weights are proposed to predict coding unit(CU) depth range and skip some specific depth levels rarely used in independent view, the previous frame and neighboring CUs. Besides, a new early termination algorithm is proposed to further reduce the coding time. Experimental results demonstrate that the proposed method saves about 56% coding time on average compared to HTM with maintaining the similar video quality.
Highlights
With the rapid development of multimedia technology, compared to text, voice, images, video is used more and more abroad
The Joint Collaborative Team on 3D Video Coding Extension Development (JCT-3V) develops High Efficiency Video Coding (HEVC) based 3D video coding standard (3D-HEVC), which is for the compression of multi view video plus depth (MVD) format[2]
In 3D-HEVC, similar to HEVC, the mode decision process in HTM is performed using all the possible coding unit (CU) sizes, prediction modes, and coding tools(disparity-compensated prediction (DCP), interview motion prediction, backward view synthesis prediction (BVSP)) to find the optimal one with the least rate distortion (RD) cost using Lagrange multiplier, which leads to high computational workloads
Summary
With the rapid development of multimedia technology, compared to text, voice, images, video is used more and more abroad. In 3D-HEVC, similar to HEVC, the mode decision process in HTM is performed using all the possible CU sizes , prediction modes, and coding tools(disparity-compensated prediction (DCP), interview motion prediction, backward view synthesis prediction (BVSP)) to find the optimal one with the least rate distortion (RD) cost using Lagrange multiplier, which leads to high computational workloads. A fast mode decision algorithm based on variable size CU and disparity estimation in [11] is proposed to reduce 3D-HEVC computational complexity.
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