# Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment

arXiv: https://arxiv.org/abs/2210.05357 · HTML: https://mlsanity.com/paper/2210.05357/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 73 references that are in the map.
- First posted on arXiv 11 Oct 2022.

## Builds on (most cited first, 30 of 40)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [The "something something" video database for learning and evaluating visual common sense](https://mlsanity.com/paper/1706.04261/) (2017)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions](https://mlsanity.com/paper/1705.08421/) (2017)
- [ResNet strikes back: An improved training procedure in timm](https://mlsanity.com/paper/2110.00476/) (2021)
- [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019)
- [Temporal Segment Networks for Action Recognition in Videos](https://mlsanity.com/paper/1705.02953/) (2017)
- [From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality](https://mlsanity.com/paper/1912.10088/) (2019)
- [Massive Online Crowdsourced Study of Subjective and Objective Picture Quality](https://mlsanity.com/paper/1511.02919/) (2015)
- [FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling](https://mlsanity.com/paper/2207.02595/) (2022)
- [Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition](https://mlsanity.com/paper/1708.07632/) (2017)
- [Quality Assessment of In-the-Wild Videos](https://mlsanity.com/paper/1908.00375/) (2019)
- [Patch-VQ: 'Patching Up' the Video Quality Problem](https://mlsanity.com/paper/2011.13544/) (2020)
- [UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content](https://mlsanity.com/paper/2005.14354/) (2020)
- [Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild](https://mlsanity.com/paper/2005.13983/) (2020)
- [Large-Scale Study of Perceptual Video Quality](https://mlsanity.com/paper/1803.01761/) (2018)
- [RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content](https://mlsanity.com/paper/2101.10955/) (2021)
- [Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception](https://mlsanity.com/paper/2108.08505/) (2021)
- [Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training](https://mlsanity.com/paper/2011.04263/) (2020)

## Built on by (most cited first, 30 of 50)

- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels](https://mlsanity.com/paper/2312.17090/) (2023)
- [Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives](https://mlsanity.com/paper/2211.04894/) (2022)
- [Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision](https://mlsanity.com/paper/2309.14181/) (2023)
- [MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos](https://mlsanity.com/paper/2303.14933/) (2023)
- [Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach](https://mlsanity.com/paper/2305.12726/) (2023)
- [VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2411.13503/) (2024)
- [DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-Resolution](https://mlsanity.com/paper/2505.16239/) (2025)
- [Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterion](https://mlsanity.com/paper/2302.13269/) (2023)
- [Q-Boost: On Visual Quality Assessment Ability of Low-level Multi-Modality Foundation Models](https://mlsanity.com/paper/2312.15300/) (2023)
- [GMS-3DQA: Projection-based Grid Mini-patch Sampling for 3D Model Quality Assessment](https://mlsanity.com/paper/2306.05658/) (2023)
- [Towards Robust Text-Prompted Semantic Criterion for In-the-Wild Video Quality Assessment](https://mlsanity.com/paper/2304.14672/) (2023)
- [Video Quality Assessment: A Comprehensive Survey](https://mlsanity.com/paper/2412.04508/) (2024)
- [Exploring AIGC Video Quality: A Focus on Visual Harmony, Video-Text Consistency and Domain Distribution Gap](https://mlsanity.com/paper/2404.13573/) (2024)
- [Q-SiT: Teaching LMMs for Image Quality Scoring and Interpreting](https://mlsanity.com/paper/2503.09197/) (2025)
- [Light-VQA: A Multi-Dimensional Quality Assessment Model for Low-Light Video Enhancement](https://mlsanity.com/paper/2305.09512/) (2023)
- [CLIPVQA:Video Quality Assessment via CLIP](https://mlsanity.com/paper/2407.04928/) (2024)
- [KVQ: Boosting Video Quality Assessment via Saliency-guided Local Perception](https://mlsanity.com/paper/2503.10259/) (2025)
- [YouTube SFV+HDR Quality Dataset](https://mlsanity.com/paper/2406.05305/) (2024)
- [Geometry-Aware Video Quality Assessment for Dynamic Digital Human](https://mlsanity.com/paper/2310.15984/) (2023)
- [Q-Bench+: A Benchmark for Multi-modal Foundation Models on Low-level Vision from Single Images to Pairs](https://mlsanity.com/paper/2402.07116/) (2024)
- [NTIRE 2023 Quality Assessment of Video Enhancement Challenge](https://mlsanity.com/paper/2307.09729/) (2023)
- [NVS-SQA: Exploring Self-Supervised Quality Representation Learning for Neurally Synthesized Scenes without References](https://mlsanity.com/paper/2501.06488/) (2025)
- [SparkVSR: Interactive Video Super-Resolution via Sparse Keyframe Propagation](https://mlsanity.com/paper/2603.16864/) (2026)
- [ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment](https://mlsanity.com/paper/2407.11496/) (2024)
- [An Ensemble Approach to Short-form Video Quality Assessment Using Multimodal LLM](https://mlsanity.com/paper/2412.18060/) (2024)
- [Video Quality Assessment for Online Processing: From Spatial to Temporal Sampling](https://mlsanity.com/paper/2501.07087/) (2025)
- [Diffusion-based Facial Aesthetics Enhancement with 3D Structure Guidance](https://mlsanity.com/paper/2503.14402/) (2025)
- [Beyond Reward Margin: Rethinking and Resolving Likelihood Displacement in Diffusion Models via Video Generation](https://mlsanity.com/paper/2511.19049/) (2025)
- [SB-VQA: A Stack-Based Video Quality Assessment Framework for Video Enhancement](https://mlsanity.com/paper/2305.08408/) (2023)

## Abstract (arXiv)

The increased resolution of real-world videos presents a dilemma between efficiency and accuracy for deep Video Quality Assessment (VQA). On the one hand, keeping the original resolution will lead to unacceptable computational costs. On the other hand, existing practices, such as resizing and cropping, will change the quality of original videos due to the loss of details and contents, and are therefore harmful to quality assessment. With the obtained insight from the study of spatial-temporal redundancy in the human visual system and visual coding theory, we observe that quality information around a neighbourhood is typically similar, motivating us to investigate an effective quality-sensitive neighbourhood representatives scheme for VQA. In this work, we propose a unified scheme, spatial-temporal grid mini-cube sampling (St-GMS) to get a novel type of sample, named fragments. Full-resolution videos are first divided into mini-cubes with preset spatial-temporal grids, then the temporal-aligned quality representatives are sampled to compose the fragments that serve as inputs for VQA. In addition, we design the Fragment Attention Network (FANet), a network architecture tailored specifically for fragments. With fragments and FANet, the proposed efficient end-to-end FAST-VQA and FasterVQA achieve significantly better performance than existing approaches on all VQA benchmarks while requiring only 1/1612 FLOPs compared to the current state-of-the-art. Codes, models and demos are available at https://github.com/timothyhtimothy/FAST-VQA-and-FasterVQA.