# Perceptual Video Quality Assessment: A Survey

arXiv: https://arxiv.org/abs/2402.03413 · HTML: https://mlsanity.com/paper/2402.03413/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 75 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 20 of 20)

- [A Deep Learning based No-reference Quality Assessment Model for UGC Videos](https://mlsanity.com/paper/2204.14047/) (2022)
- [Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training](https://mlsanity.com/paper/2011.04263/) (2020)
- [Perceptual Quality Prediction on Authentically Distorted Images Using a Bag of Features Approach](https://mlsanity.com/paper/1609.04757/) (2016)
- [MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2209.00244/) (2022)
- [Green Learning: Introduction, Examples and Outlook](https://mlsanity.com/paper/2210.00965/) (2022)
- [Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment](https://mlsanity.com/paper/2207.03723/) (2022)
- [Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment](https://mlsanity.com/paper/2308.00729/) (2023)
- [Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model](https://mlsanity.com/paper/1807.10990/) (2018)
- [Capturing Co-existing Distortions in User-Generated Content for No-reference Video Quality Assessment](https://mlsanity.com/paper/2307.16813/) (2023)
- [Saliency in Augmented Reality](https://mlsanity.com/paper/2204.08308/) (2022)
- [A Survey on Rain Removal from Video and Single Image](https://mlsanity.com/paper/1909.08326/) (2019)
- [StableVQA: A Deep No-Reference Quality Assessment Model for Video Stability](https://mlsanity.com/paper/2308.04904/) (2023)
- [FAVER: Blind Quality Prediction of Variable Frame Rate Videos](https://mlsanity.com/paper/2201.01492/) (2022)
- [Perceptual Quality Assessment for Fine-Grained Compressed Images](https://mlsanity.com/paper/2206.03862/) (2022)
- [Multiview Contrastive Learning for Completely Blind Video Quality Assessment of User Generated Content](https://mlsanity.com/paper/2207.06148/) (2022)
- [2BiVQA: Double Bi-LSTM based Video Quality Assessment of UGC Videos](https://mlsanity.com/paper/2208.14774/) (2022)
- [MRET: Multi-resolution Transformer for Video Quality Assessment](https://mlsanity.com/paper/2303.07489/) (2023)
- [HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos](https://mlsanity.com/paper/2304.13156/) (2023)
- [A Brief Survey on Adaptive Video Streaming Quality Assessment](https://mlsanity.com/paper/2202.12987/) (2022)
- [The Effect of Frame Rate on 3D Video Quality and Bitrate](https://mlsanity.com/paper/1803.04826/) (2018)

## Built on by (most cited first, 30 of 43)

- [LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models](https://mlsanity.com/paper/2408.14008/) (2024)
- [FineVQ: Fine-Grained User Generated Content Video Quality Assessment](https://mlsanity.com/paper/2412.19238/) (2024)
- [Enhancing Blind Video Quality Assessment with Rich Quality-aware Features](https://mlsanity.com/paper/2405.08745/) (2024)
- [VQA2: Visual Question Answering for Video Quality Assessment](https://mlsanity.com/paper/2411.03795/) (2024)
- [Quality Assessment for AI Generated Images with Instruction Tuning](https://mlsanity.com/paper/2405.07346/) (2024)
- [Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified Model](https://mlsanity.com/paper/2407.21408/) (2024)
- [Exploring Rich Subjective Quality Information for Image Quality Assessment in the Wild](https://mlsanity.com/paper/2409.05540/) (2024)
- [MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis](https://mlsanity.com/paper/2411.11235/) (2024)
- [F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration](https://mlsanity.com/paper/2412.13155/) (2024)
- [VQAThinker: Exploring Generalizable and Explainable Video Quality Assessment via Reinforcement Learning](https://mlsanity.com/paper/2508.06051/) (2025)
- [Omni2: Unifying Omnidirectional Image Generation and Editing in an Omni Model](https://mlsanity.com/paper/2504.11379/) (2025)
- [G-Refine: A General Quality Refiner for Text-to-Image Generation](https://mlsanity.com/paper/2404.18343/) (2024)
- [How is Visual Attention Influenced by Text Guidance? Database and Model](https://mlsanity.com/paper/2404.07537/) (2024)
- [ESIQA: Perceptual Quality Assessment of Vision-Pro-based Egocentric Spatial Images](https://mlsanity.com/paper/2407.21363/) (2024)
- [HarmonyIQA: Pioneering Benchmark and Model for Image Harmonization Quality Assessment](https://mlsanity.com/paper/2501.01116/) (2025)
- [T2VEval: Benchmark Dataset and Objective Evaluation Method for T2V-generated Videos](https://mlsanity.com/paper/2501.08545/) (2025)
- [Generalizable Video Quality Assessment via Weak-to-Strong Learning](https://mlsanity.com/paper/2505.03631/) (2025)
- [Scaling-up Perceptual Video Quality Assessment](https://mlsanity.com/paper/2505.22543/) (2025)
- [CompressedVQA-HDR: Generalized Full-reference and No-reference Quality Assessment Models for Compressed High Dynamic Range Videos](https://mlsanity.com/paper/2507.11900/) (2025)
- [How Does Audio Influence Visual Attention in Omnidirectional Videos? Database and Model](https://mlsanity.com/paper/2408.05411/) (2024)
- [Medical Manifestation-Aware De-Identification](https://mlsanity.com/paper/2412.10804/) (2024)
- [Video Quality Assessment for Online Processing: From Spatial to Temporal Sampling](https://mlsanity.com/paper/2501.07087/) (2025)
- [Engagement Prediction of Short Videos with Large Multimodal Models](https://mlsanity.com/paper/2508.02516/) (2025)
- [MVBIND: Self-Supervised Music Recommendation For Videos Via Embedding Space Binding](https://mlsanity.com/paper/2405.09286/) (2024)
- [Quality-guided Skin Tone Enhancement for Portrait Photography](https://mlsanity.com/paper/2406.15848/) (2024)
- [ESVQA: Perceptual Quality Assessment of Egocentric Spatial Videos](https://mlsanity.com/paper/2412.20423/) (2024)
- [Quality Assessment and Distortion-aware Saliency Prediction for AI-Generated Omnidirectional Images](https://mlsanity.com/paper/2506.21925/) (2025)
- [MVQA-68K: A Multi-dimensional and Causally-annotated Dataset with Quality Interpretability for Video Assessment](https://mlsanity.com/paper/2509.11589/) (2025)
- [CAMP-VQA: Caption-Embedded Multimodal Perception for No-Reference Quality Assessment of Compressed Video](https://mlsanity.com/paper/2511.07290/) (2025)
- [QualiRAG: Retrieval-Augmented Generation for Visual Quality Understanding](https://mlsanity.com/paper/2601.18195/) (2026)

## Abstract (arXiv)

Perceptual video quality assessment plays a vital role in the field of video processing due to the existence of quality degradations introduced in various stages of video signal acquisition, compression, transmission and display. With the advancement of internet communication and cloud service technology, video content and traffic are growing exponentially, which further emphasizes the requirement for accurate and rapid assessment of video quality. Therefore, numerous subjective and objective video quality assessment studies have been conducted over the past two decades for both generic videos and specific videos such as streaming, user-generated content (UGC), 3D, virtual and augmented reality (VR and AR), high frame rate (HFR), audio-visual, etc. This survey provides an up-to-date and comprehensive review of these video quality assessment studies. Specifically, we first review the subjective video quality assessment methodologies and databases, which are necessary for validating the performance of video quality metrics. Second, the objective video quality assessment algorithms for general purposes are surveyed and concluded according to the methodologies utilized in the quality measures. Third, we overview the objective video quality assessment measures for specific applications and emerging topics. Finally, the performances of the state-of-the-art video quality assessment measures are compared and analyzed. This survey provides a systematic overview of both classical works and recent progresses in the realm of video quality assessment, which can help other researchers quickly access the field and conduct relevant research.