Research on key technology of next generation video coding standard

Abstract

The Joint Collaborative Team on Video Coding (JCT-VC) has recently accomplished a new standard, referred to as high efficiency video coding (HEVC) whose primary goal is to achieve 50% bit-rate reduction under an equal perceptual quality as compared to its predecessor. However, there is a constant hunger for higher quality video compression algorithms because the amounts of images and videos are now growing explosively. Therefore, what I focus on in this thesis is further improving video coding efficiency based on HEVC. First, I present a new block-based method for the HEVC intra coding. Pixels in each prediction block are divided into two parts: half pixels are coded via a novel padding technique together with a constrained quantization algorithm whereas the other half are reconstructed by linear interpolations. Second, I propose a new adaptive sharpening filter based on guided image filter and embed it between deblocking filter and SAO. The proposed algorithm classifies pixels of a frame into several groups according to each pixel’s Sum-Modified-Laplacian value and assigns identical optimal filtering parameters to the pixels belonging to the same group based on rate-distortion optimization. Third, instead of the default transforms in HEVC, I build an elliptical model with directionality and design some non-separable transforms based on KLT in closed-form for each intra-prediction mode. Fourth, I present a DC coefficient estimation algorithm for intra-predicted residual blocks, which solves a pixel domain optimal offset in a closed-form to recover the corresponding block edges based on the texture continuity priori hypotheses. Simulation results demonstrate that the overall performance can achieve 3.5%, 2.4%, 2.3% and 2.3% BD-rate reductions on average under AI, RA, LDP and LDP configurations, respectively, with slight complexity increase. For some specific sequences, the coding efficiency enhancement can be up to 10.2% and 8.8% under AI and LDP configurations, respectively.

Date of Award	2017
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology