Visual perception based rate distortion optimization method for high dynamic range video coding

Abstract

In view of the drastic increase of storage resources and transmission bandwidth requirement for high dynamic range (HDR) video compared to the traditional low dynamic range (LDR) video, we propose a dynamic rate distortion optimization algorithm based on visual perception for HDR Video encoding to improve the performance of high efficiency video coding (HEVC) Main 10 for coding HDR video. With the information of visual selective attention, we design a non-uniform distortion weight distribution strategy to different regions of interest and improve the conventional method of distortion calculation. At the same time, in order to further eliminate the perceptive redundancy in HDR video coding, the texture characteristics of video content are used to adjust Lagrange multipliers adaptively, which is applied to the encoder to dynamically adjust the quantization parameters to realize reasonably the trade-off between coded bits and distortion perception. The experimental results show that the proposed algorithm can save an average of 7.46% and 6.53% bitrate with the same HDR-visible difference predictor-2.2(HDR-VDP-2.2) and PSNR_DE compared with HEVC Main 10, saving the maximum of 18.52 % and 11.49% respectively. The proposed algorithm can effectively reduce the consumption of the overall bitrates and still maintain the visual quality of the reconstructed HDR video.