Complexity reduction of 3D-HEVC based on depth analysis for background and ROI classification

The 3D extension of the High Efficiency Video Coding (HEVC) standard achieves large compression rates thanks to the addition of several tools to encode multiview and depth information on top of those available in HEVC. The use of such tools incur in a very large computational demand, which can be a serious problem in power and computationally-constrained devices and applications. However, not all information contained in an image is fundamental to the viewer, so that different levels of computational effort can be employed when encoding different image regions. The Region of Interest (ROI) concept is used in this work to classify each Coding Unit (CU) as foreground, heterogeneous background and homogeneous background. Then, a simplified encoding process is employed in those regions classified as homogeneous background, terminating earlier the partitioning process in texture CUs, while still still performing the regular decisions in areas classified as ROI. Experimental results show an average reduction of 22.6% in computational complexity for texture coding with negligible or non-perceived image quality degradation.