A collection of papers related to Video Quality Assessment (VQA).
The organization of papers refers to our survey "Video Quality Assessment: A Comprehensive Survey". We will continue to update both arxiv paper and this repo considering the fast development of this field.
Please let us know if you have any suggestions by e-mail: qzheng21@m.fudan.edu.cn and tzz@tamu.edu.
If you find our survey useful for your research, please cite the following paper:
@misc{zheng2024videoqualityassessmentcomprehensive,
title={Video Quality Assessment: A Comprehensive Survey},
author={Qi Zheng and Yibo Fan and Leilei Huang and Tianyu Zhu and Jiaming Liu and Zhijian Hao and Shuo Xing and Chia-Ju Chen and Xiongkuo Min and Alan C. Bovik and Zhengzhong Tu},
year={2024},
eprint={2412.04508},
archivePrefix={arXiv},
primaryClass={eess.IV},
url={https://arxiv.org/abs/2412.04508},
}
- Video-Quality-Assessment-A-Comprehensive-Survey
- Table of Contents
- Taxonomy of Subjective and Objective Video Quality Assessment
- Classification and Evolution of Objective Quality Assessment Models
- Application Overview of Video Quality Assessment
- Video Quality Assessment Datasets
- Objective Video Quality Assessment Models
LIVE-VQA: Study of Subjective and Objective Quality Assessment of Video
CVD2014: CVD2014—A Database for Evaluating No-Reference Video Quality Assessment Algorithms
MCL-V: MCL-V: A streaming video quality assessment database
BVI-HFR: A Study of High Frame Rate Video Formats
LIVE-YT-HFR: Subjective and objective quality assessment of high frame rate videos
BVI-VFI: BVI-VFI: A Video Quality Database for Video Frame Interpolation
KoNViD-1k: The Konstanz natural video database (KoNViD-1k)
LIVE-VQC: Large-Scale Study of Perceptual Video Quality
YouTube-UGC: YouTube UGC Dataset for Video Compression Research
FlickrVid-150k: No-Reference Video Quality Assessment using Multi-Level Spatially Pooled Features
LSVQ: Patch-VQ: ‘Patching Up’ the Video Quality Problem
Youku-V1K: Perceptual quality assessment of internet videos
PUGCQ: PUGCQ: A Large Scale Dataset for Quality Assessment of Professional User-Generated Content
YT-UGC+: Rich features for perceptual quality assessment of UGC videos
LIVE-YT-Gaming: Subjective and Objective Analysis of Streamed Gaming Videos
Tele-VQA: Telepresence Video Quality Assessment
TaoLive: MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos
KVQ: KVQ: Kwai video quality assessment for short-form videos
Chivileva et al.: Measuring the Quality of Text-to-Video Model Outputs: Metrics and Dataset
EvalCrafter: EvalCrafter: Benchmarking and Evaluating Large Video Generation Models
FETV: FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation
VBench: VBench: Comprehensive Benchmark Suite for Video Generative Models
T2VQA-DB: Subjective-Aligned Dataset and Metric for Text-to-Video Quality Assessment
GAIA: GAIA: Rethinking Action Quality Assessment for AI-Generated Videos
LGVQ: Benchmarking AIGC Video Quality Assessment: A Dataset and Unified Model
MSE
PSNR
SSIM: Image quality assessment: from error visibility to structural similarity
MS-SSIM: Multiscale structural similarity for image quality assessment
CW-SSIM: Complex Wavelet Structural Similarity: A New Image Similarity Index
IW-SSIM: Information Content Weighting for Perceptual Image Quality Assessment
FSIM: FSIM: A Feature Similarity Index for Image Quality Assessment
ESSIM: Edge Strength Similarity for Image Quality Assessment
GMSD: Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
Liu et al.: Image Quality Assessment Based on Gradient Similarity
VSI: VSI: A Visual Saliency-Induced Index for Perceptual Image Quality Assessment
Video SSIM: Video quality assessment based on structural distortion measurement
Wang and Li: Video quality assessment using a statistical model of human visual speed perception
V-SSIM: A structural similarity metric for video based on motion models
MC-SSIM: Efficient Video Quality Assessment Along Temporal Trajectories
Manasa and Channappayya: An Optical Flow-Based Full Reference Video Quality Assessment Algorithm
3D-SSIM: 3D-SSIM for video quality assessment
VIF: Image information and visual quality
MAD: Most apparent distortion: full-reference image quality assessment and the role of strategy
ST-RRED: Video Quality Assessment by Reduced Reference Spatio-Temporal Entropic Differencing
VMAF: Toward a practical perceptual video quality metric
ST-VMAF: Spatiotemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment
E-VMAF: Spatiotemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment
FUNQUE: Funque: Fusion of Unified Quality Evaluators
FUNQUE+: One Transform to Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment
MOVIE: Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos
ST-MAD: A spatiotemporal most-apparent-distortion model for video quality assessment
AFViQ: Attention Driven Foveated Video Quality Assessment
VQM-VFD: Temporal Video Quality Model Accounting for Variable Frame Delay Distortions
PVM: A Perception-Based Hybrid Model for Video Quality Assessment
Bosse et al.: Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
DeepQA: Deep Learning of Human Visual Sensitivity in Image Quality Assessment Framework
Ahn et al.: Deep Learning-Based Distortion Sensitivity Prediction for Full-Reference Image Quality Assessment
SAMScore: SAMScore: A Semantic Structural Similarity Metric for Image Translation Evaluation
SAM-IQA: SAM-IQA: Can Segment Anything Boost Image Quality Assessment?
LPIPS: The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
E-LPIPS: E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles
DeepWSD: DeepWSD: Projecting Degradations in Perceptual Space to Wasserstein Distance in Deep Feature Space
Tariq et al.: Why Are Deep Representations Good Perceptual Quality Features?
DISTS: Image Quality Assessment: Unifying Structure and Texture Similarity
A-DISTS: Locally Adaptive Structure and Texture Similarity for Image Quality Assessment
TOPIQ: TOPIQ: A Top-Down Approach From Semantics to Distortions for Image Quality Assessment
FloLPIPS: FloLPIPS: A Bespoke Video Quality Metric for Frame Interpolation
C3DVQA: C3DVQA: Full-Reference Video Quality Assessment with 3D Convolutional Neural Network
Sun et al.: Deep Learning Based Full-Reference and No-Reference Quality Assessment Models for Compressed UGC Videos
Chen et al.: Deep Neural Networks for End-to-End Spatiotemporal Video Quality Prediction and Aggregation
DVQM-HT: Deep VQA based on a Novel Hybrid Training Methodology
STRA-VQA: Video Quality Assessment for Spatio-Temporal Resolution Adaptive Coding
CPBDM: A No-Reference Image Blur Metric Based on the Cumulative Probability of Blur Detection (CPBD)
LPCM: Image Sharpness Assessment Based on Local Phase Coherence
NJQA: No-Reference Quality Assessment of JPEG Images via a Quality Relevance Map
JPEG-NR: No-reference perceptual quality assessment of JPEG compressed images
TLVQM: Two-Level Approach for No-Reference Consumer Video Quality Assessment
CORNIA: Unsupervised feature learning framework for no-reference image quality assessment
HOSA: Blind Image Quality Assessment Based on High Order Statistics Aggregation
BRISQUE: No-Reference Image Quality Assessment in the Spatial Domain
GM-LOG: Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features
HIGRADE: No-Reference Quality Assessment of Tone-Mapped HDR Pictures
FRIQUEE: Perceptual quality prediction on authentically distorted images using a bag of features approach
V-BLIINDS: Blind Prediction of Natural Video Quality
Li et al.: Spatiotemporal Statistics for Video Quality Assessment
VIDEVAL: UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
STS-QA: Blind Video Quality Assessment via Space-Time Slice Statistics
FAVER: FAVER: Blind quality prediction of variable frame rate videos
NIQE: Making a “Completely Blind” Image Quality Analyzer
IL-NIQE: A Feature-Enriched Completely Blind Image Quality Evaluator
NPQI: Blind Image Quality Assessment by Natural Scene Statistics and Perceptual Characteristics
VIIDEO: A Completely Blind Video Integrity Oracle
STEM: Completely Blind Quality Assessment of User Generated Video Content
TPQI: Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment
SLEEQ: A no-reference video quality predictor for compression and scaling artifacts
BPRI: Blind Quality Assessment Based on Pseudo-Reference Image
VIQE: A Completely Blind Video Quality Evaluator
Kang et al.: Convolutional Neural Networks for No-Reference Image Quality Assessment
Bosse et al.: A deep neural network for image quality assessment
MEON: End-to-End Blind Image Quality Assessment Using Deep Neural Networks
NIMA: NIMA: Neural Image Assessment
PQR: A Probabilistic Quality Representation Approach to Deep Blind Image Quality Prediction
DB-CNN: Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network
PaQ-2-PiQ: From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality
QCN: Blind Image Quality Assessment Based on Geometric Order Learning
GraphIQA: GraphIQA: Learning Distortion Graph Representations for Blind Image Quality Assessment
Gao et al.: Image Quality Assessment: From Mean Opinion Score to Opinion Score Distribution
FPR: Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning
HIQA: No-Reference Image Quality Assessment by Hallucinating Pristine Features
BIECON: Fully Deep Blind Image Quality Predictor
MUSIQ: MUSIQ: Multi-Scale Image Quality Transformer
TRIQ: Transformer For Image Quality Assessment
DEIQT: Data-Efficient Image Quality Assessment with Attention-Panel Decoder
TReS: No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency
MANIQA: MANIQA: Multi-Dimension Attention Network for No-Reference Image Quality Assessment
Zhang et al.: Continual Learning for Blind Image Quality Assessment
MetaIQA: MetaIQA: Deep Meta-Learning for No-Reference Image Quality Assessment
Li et al.: Continual Learning of Blind Image Quality Assessment with Channel Modulation Kernel
Wang et al.: Deep Blind Image Quality Assessment Powered by Online Hard Example Mining
Zhang et al.: Task-Specific Normalization for Continual Learning of Blind Image Quality Models
CONTRIQUE: Image Quality Assessment Using Contrastive Learning
Shukla et al.: Opinion Unaware Image Quality Assessment via Adversarial Convolutional Variational Autoencoder
Re-IQA: Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild
Babu et al.: No Reference Opinion Unaware Quality Assessment of Authentically Distorted Images
ARNIQA: ARNIQA: Learning Distortion Manifold for Image Quality Assessment
Zhao et al: Quality-Aware Pre-Trained Models for Blind Image Quality Assessment
CLIP-IQA: Exploring CLIP for Assessing the Look and Feel of Images
CLIP-IQA+: Exploring CLIP for Assessing the Look and Feel of Images
LIQE: Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective
TCDs: Towards transparent deep image aesthetics assessment with tag-based content descriptors
Q-Bench: Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision
Q-Instruct: Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models
Q-Align: Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
Co-Instruct: Towards Open-Ended Visual Quality Comparison
Domonkos Varga: No-Reference Video Quality Assessment Based on the Temporal Pooling of Deep Features
NAVE: A No-Reference Autoencoder Video Quality Metric
CNN-TLVQM: Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features
SIONR: Semantic Information Oriented No-Reference Video Quality Assessment
CenseoQoE: A strong baseline for image and video quality assessment
RAPIQUE: RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content
SWDF-DF-VQA: No-Reference Video Quality Assessment Using Multi-Pooled, Saliency Weighted Deep Features and Decision Fusion
NR-VMAF: No-Reference VMAF: A Deep Neural Network-Based Approach to Blind Video Quality Assessment
MLSP-VQA: No-Reference Video Quality Assessment using Multi-Level Spatially Pooled Features
Varga et al.: No-reference video quality assessment via pretrained CNN and LSTM networks
VSFA: Quality Assessment of In-the-Wild Videos
MGQA: Video Quality Assessment for Online Processing: From Spatial to Temporal Sampling
RIRNet: RIRNet: Recurrent-In-Recurrent Network for Video Quality Assessment
STDAM: Perceptual Quality Assessment of Internet Videos
MDTVSFA: Unified Quality Assessment of in-the-Wild Videos with Mixed Datasets Training
AB-VQA: Attention Based Network For No-Reference UGC Video Quality Assessment
Li et al.: Study on no-reference video quality assessment method incorporating dual deep learning networks
2BiVQA: 2BiVQA: Double Bi-LSTM-based Video Quality Assessment of UGC Videos
SACONVA: No-Reference Video Quality Assessment With 3D Shearlet Transform and Convolutional Neural Networks
V-MEON: End-to-End Blind Quality Assessment of Compressed Videos Using Deep Neural Networks
You et al.: Deep Neural Networks for No-Reference Video Quality Assessment
Hou et al.: No-reference video quality evaluation by a deep transfer CNN architecture
Patch-VQ: Patch-VQ: 'Patching Up' the Video Quality Problem
CoINVQ: Rich Features for Perceptual Quality Assessment of UGC Videos
Sun et al.: A Deep Learning based No-reference Quality Assessment Model for UGC Videos
Li et al.: Blindly Assess Quality of In-the-Wild Videos via Quality-Aware Pre-Training and Motion Perception
MD-VQA: MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos
UCDA: Unsupervised Curriculum Domain Adaptation for No-Reference Video Quality Assessment
Shen et al.: A Blind Video Quality Assessment Method via Spatiotemporal Pyramid Attention
StarVQA: Starvqa: Space-Time Attention for Video Quality Assessment
PHIQNet: Long Short-term Convolutional Transformer for No-Reference Video Quality Assessment
DisCoVQA: DisCoVQA: Temporal Distortion-Content Transformers for Video Quality Assessment
FAST-VQA: FAST-VQA: Efficient End-to-End Video Quality Assessment with Fragment Sampling
SAMA: Scaling and Masking: A New Paradigm of Data Sampling for Image and Video Quality Assessment
SSL-VQA: Knowledge Guided Semi-supervised Learning for Quality Assessment of User Generated Videos
PTM-VQA: PTM-VQA: Efficient Video Quality Assessment Leveraging Diverse PreTrained Models from the Wild
COVER: COVER: A Comprehensive Video Quality Evaluator
KSVQE: KVQ: Kwai Video Quality Assessment for Short-form Videos
Wen et al.: Modular Blind Video Quality Assessment
BUONA-VISTA: Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterion
Q-Align: Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
LMM-VQA: LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models