A study of the extended perceptually weighted peak signal-to-noise ratio (xpsnr) for video compression with different resolutions and bit depths

Fast and accurate estimation of the visual quality of compressed video content, particularly for quality-of-experience (QoE) monitoring in video broadcasting and streaming, has become important. Given the relatively poor performance of the well-known peak signal-to-noise ratio (PSNR) for such tasks, several video quality assessment (VQA) methods have been developed. In this study, the authors’ own recent work on an extension of the perceptually weighted PSNR, termed XPSNR, is analyzed in terms of its suitability for objectively predicting the subjective quality of videos with different resolutions (up to UHD) and bit depths (up to 10 bits/sample). Performance evaluations on various subjective-MOS annotated video databases and investigations of the computational complexity in comparison with state-of-the-art VQA solutions like VMAF and (MS-)SSIM confirm the merit of the XPSNR approach. The use of XPSNR as a reference model for visually motivated control of the bit allocation in modern video encoders for, e. g., HEVC and VVC is outlined as well.