Vision-oriented algorithm for fast decision in 3D video coding

This paper designs a novel method to reduce the coding complexity of 3D-HEVC encoder by utilizing the properties of human visual perception. Two vision-oriented edge detections are proposed: for colour texture detection, the authors adopt the Just-Noticeable Distortion (JND); for depth map, the authors combine the Sample Adaptive Offset (SAO) and the Just Noticeable Depth Difference (JNDD) model. The authors also analyse the properties of colour texture and depth map to classify the coding tree unit (CTU) into various kinds of types, including complex-edge CTU, moderate-edge CTU and homogeneous CTU. Besides, fast mode decisions and early termination criteria are performed individually on each type of CTUs according to their characteristics. Especially for those CTUs with more edge information, the proposed projection-based fast mode decision and residual-based early termination preserve important colour texture while speeding up the coding at the same time. The proposed vision-oriented algorithm reduces 31.981% of the overall average coding time with only 1.580% BD-Bitrate increase. Experimental results show that the proposed algorithm can provide considerable time-saving while still maintain the video quality, which outperforms the previous researches.