Perceptually Optimized Bit-Allocation and Associated Distortion Measure for Block-Based Image or Video Coding

Abstract

It is well known that input-invariant quantization in perceptual image or video coding often leads to visually suboptimal results and that quantization parameter adaptation (QPA) based on a model of the human visual system can improve subjective coding quality. This paper introduces a simple low-complexity QPA algorithm, controlled using a block-wise perceptually weighted distortion measure representing a generalization of the PSNR metric. The weighting scheme of this WPSNR metric is based on a psychovisual model. It directly leads to a perceptually adapted scaling of the block-wise Lagrange parameter used in the bit-allocation process in the encoder and, consequently, to a block-wise QPA. Unlike prior QPA approaches, the proposal avoids classifications of picture regions and easily extends from still-image or grayscale to video or chromatic coding. The WPSNR metric also uses fewer algorithmic operations than e. g. the multiscale structural similarity measure (MS-SSIM). Due to the results of two formal subjective tests indicating its visual benefit, the QPA proposal has been adopted into VTM, the currently developed Versatile Video Coding (VVC) reference software.