A Survey on Perceptually Optimized Video Coding

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

3 Scopus Citations
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Detail(s)

Original languageEnglish
Article number245
Journal / PublicationACM Computing Surveys
Volume55
Issue number12
Online published2 Mar 2023
Publication statusPublished - Dec 2023

Abstract

To provide users with more realistic visual experiences, videos are developing in the trends of Ultra High Definition (UHD), High Frame Rate (HFR), High Dynamic Range (HDR), Wide Color Gammut (WCG), and high clarity. However, the data amount of videos increases exponentially, which requires high efficiency video compression for storage and network transmission. Perceptually optimized video coding aims to maximize compression efficiency by exploiting visual redundancies. In this article, we present a broad and systematic survey on perceptually optimized video coding. Firstly, we present problem formulation and framework of the perceptually optimized video coding, which includes visual perception modeling, visual quality assessment, and perceptual video coding optimization. Secondly, recent advances on visual factors, computational perceptual models, and quality assessment models are presented. Thirdly, we review perceptual video coding optimizations from four key aspects, including perceptually optimized bit allocation, rate-distortion optimization, transform and quantization, and filtering and enhancement. In each part, problem formulation, working flow, recent advances, advantages, and challenges are presented. Fourthly, perceptual coding performances of the latest coding standards and tools are experimentally analyzed. Finally, challenging issues and future opportunities are identified. © 2023 Association for Computing Machinery.

Research Area(s)

  • Perceptual video coding, human visual system, just noticeable difference, quality assessment, rate-distortion optimization, visual attention

Citation Format(s)

A Survey on Perceptually Optimized Video Coding. / ZHANG, Yun; ZHU, Linwei; JIANG, Gangyi et al.
In: ACM Computing Surveys, Vol. 55, No. 12, 245, 12.2023.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review