# A Survey on Perceptually Optimized Video Coding

arXiv: https://arxiv.org/abs/2112.12284 · HTML: https://mlsanity.com/paper/2112.12284/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2021.

## Built on by (most cited first, 10 of 10)

- [Perceptual Video Coding for Machines via Satisfied Machine Ratio Modeling](https://mlsanity.com/paper/2211.06797/) (2022)
- [Deep Video Codec Control for Vision Models](https://mlsanity.com/paper/2308.16215/) (2023)
- [DT-JRD: Deep Transformer based Just Recognizable Difference Prediction Model for Video Coding for Machines](https://mlsanity.com/paper/2411.09308/) (2024)
- [Evaluating quality metrics through the lenses of psychophysical measurements of low-level vision](https://mlsanity.com/paper/2503.16264/) (2025)
- [Latent-Compressed Variational Autoencoder for Video Diffusion Models](https://mlsanity.com/paper/2604.16479/) (2026)
- [Perceptual Learned Image Compression via End-to-End JND-Based Optimization](https://mlsanity.com/paper/2402.02836/) (2024)
- [A Perspective on Deep Vision Performance with Standard Image and Video Codecs](https://mlsanity.com/paper/2404.12330/) (2024)
- [Predicting Satisfied User and Machine Ratio for Compressed Images: A Unified Approach](https://mlsanity.com/paper/2412.17477/) (2024)
- [CGVQM+D: Computer Graphics Video Quality Metric and Dataset](https://mlsanity.com/paper/2506.11546/) (2025)
- [Multi-task Just Recognizable Difference for Video Coding for Machines: Database, Model, and Coding Application](https://mlsanity.com/paper/2604.09421/) (2026)

## 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.