A Perceptual Video Coding Based on JND Model

Abstract

In view of the fact that the current high efficiency video coding standard does not consider the characteristics of human vision, this paper proposes a perceptual video coding algorithm based on the just noticeable distortion model (JND). The adjusted JND model is combined into the transformation quantization process in high efficiency video coding (HEVC) to remove more visual redundancy and maintain compatibility. First of all, we design the JND model based on pixel domain and transform domain respectively, and the pixel domain model can give the JND threshold more intuitively on the pixel. The transform domain model introduces the contrast sensitive function into the model, making the threshold estimation more precise. Secondly, the proposed JND model is embedded in the HEVC video coding framework. For the transformation skip mode (TSM) in HEVC, we adopt the existing pixel domain called nonlinear additively model (NAMM). For the non-transformation skip mode (non-TSM) in HEVC, we use transform domain JND model to further reduce visual redundancy. The simulation results show that in the case of the same visual subjective quality, the algorithm can save more bitrates.

High Efficiency Video Coding Compliant Perceptual Video Coding Using Entropy Based Visual Saliency Model

“…The spatiotemporal saliency map is then used for QP selection at coding unit level to guide bit allocation in the HEVC encoding framework. The JND model is employed in transformation and quantization blocks to phase out visually redundant information in HEVC [21]. For the transform skip mode in HEVC, the JND threshold is computed in the pixel domain by taking into account the luminance adaptation and contrast masking effects.…”