JND-based Perceptual Rate Distortion Optimization for AV1 Encoder


AV1 is the next-generation open video coding format, and it can achieve significant coding efficiency with novel coding tools. It supports Lagrangian rate distortion optimization (RDO) method to optimize the coding performance. However, the distortion and the Lagrangian multiplier used in RDO ignore the characteristics of human visual system (HVS), which leads to insufficiency for perceptual video coding. To solve this problem, a perceptual RDO scheme based on the Just Noticeable Distortion (JND) threshold of HVS is proposed. The JND for each pixel is first measured according to three perceptual features: luminance adaptation, masking effects and structure sensitivity. Based on the observation that the regions with smaller distortion visibility thresholds are more sensitive to HVS, a JND-based Lagrangian multiplier is derived to adaptively adjust the rate-distortion (RD) performance for each coding block. Experiments demonstrate that the proposed method can achieve an average SSIM-based -3.93% BD-Rate saving compared with the original AV1 encoder, which effectively improve the coding performance.

2019 Picture Coding Symposium (PCS)
Li Song
Li Song
Professor, IEEE Senior Member

Professor, Doctoral Supervisor, the Deputy Director of the Institute of Image Communication and Network Engineering of Shanghai Jiao Tong University, the Double-Appointed Professor of the Institute of Artificial Intelligence and the Collaborative Innovation Center of Future Media Network, the Deputy Secretary-General of the China Video User Experience Alliance and head of the standards group.