A Survey on Perceptually Optimized Video Coding
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Abstract (arXiv)
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 paper, 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 modelling, 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, 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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Dec 2021.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 4; 2025: 2; 2026: 2.
10 papers.
- 2022Perceptual Video Coding for Machines via Satisfied Machine Ratio Modelingcited by 7
- 2023Deep Video Codec Control for Vision Modelscited by 2
- 2024DT-JRD: Deep Transformer based Just Recognizable Difference Prediction Model for Video Coding for Machinescited by 2
- 2025Evaluating quality metrics through the lenses of psychophysical measurements of low-level visioncited by 2
- 2024Perceptual Learned Image Compression via End-to-End JND-Based Optimizationcited by 1
- 2024A Perspective on Deep Vision Performance with Standard Image and Video Codecscited by 1
- 2024Predicting Satisfied User and Machine Ratio for Compressed Images: A Unified Approachcited by 1
- 2025CGVQM+D: Computer Graphics Video Quality Metric and Datasetcited by 1
- 2026Latent-Compressed Variational Autoencoder for Video Diffusion Modelscited by 1
- 2026Multi-task Just Recognizable Difference for Video Coding for Machines: Database, Model, and Coding Applicationcited by 0
- 2026Latent-Compressed Variational Autoencoder for Video Diffusion Models12 Apr 2026
- 2026Multi-task Just Recognizable Difference for Video Coding for Machines: Database, Model, and Coding Application10 Apr 2026
- 2025CGVQM+D: Computer Graphics Video Quality Metric and Dataset13 Jun 2025
- 2025Evaluating quality metrics through the lenses of psychophysical measurements of low-level vision20 Mar 2025
- 2024Predicting Satisfied User and Machine Ratio for Compressed Images: A Unified Approach23 Dec 2024
- 2024DT-JRD: Deep Transformer based Just Recognizable Difference Prediction Model for Video Coding for Machines14 Nov 2024
- 2024A Perspective on Deep Vision Performance with Standard Image and Video Codecs18 Apr 2024
- 2024Perceptual Learned Image Compression via End-to-End JND-Based Optimization5 Feb 2024
- 2023Deep Video Codec Control for Vision Models30 Aug 2023
- 2022Perceptual Video Coding for Machines via Satisfied Machine Ratio Modeling13 Nov 2022