# A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment

arXiv: https://arxiv.org/abs/2002.10651 · HTML: https://mlsanity.com/paper/2002.10651/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

## Built on by (most cited first, 22 of 22)

- [A Deep Learning based No-reference Quality Assessment Model for UGC Videos](https://mlsanity.com/paper/2204.14047/) (2022)
- [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)
- [Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models](https://mlsanity.com/paper/2307.13981/) (2023)
- [Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC Videos](https://mlsanity.com/paper/2106.01111/) (2021)
- [Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment](https://mlsanity.com/paper/2207.03723/) (2022)
- [ChipQA: No-Reference Video Quality Prediction via Space-Time Chips](https://mlsanity.com/paper/2109.08726/) (2021)
- [Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment](https://mlsanity.com/paper/2304.06440/) (2023)
- [CONVIQT: Contrastive Video Quality Estimator](https://mlsanity.com/paper/2206.14713/) (2022)
- [Video Quality Assessment: A Comprehensive Survey](https://mlsanity.com/paper/2412.04508/) (2024)
- [Light-VQA: A Multi-Dimensional Quality Assessment Model for Low-Light Video Enhancement](https://mlsanity.com/paper/2305.09512/) (2023)
- [Regression or Classification? New Methods to Evaluate No-Reference Picture and Video Quality Models](https://mlsanity.com/paper/2102.00155/) (2021)
- [HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos](https://mlsanity.com/paper/2304.13156/) (2023)
- [A Comparative Study of Perceptual Quality Metrics for Audio-driven Talking Head Videos](https://mlsanity.com/paper/2403.06421/) (2024)
- [Perceptual Quality Assessment of UGC Gaming Videos](https://mlsanity.com/paper/2204.00128/) (2022)
- [A Neural Network based Framework for Effective Laparoscopic Video Quality Assessment](https://mlsanity.com/paper/2202.04517/) (2022)
- [Perceptual Quality Assessment of Virtual Reality Videos in the Wild](https://mlsanity.com/paper/2206.08751/) (2022)
- [DCVQE: A Hierarchical Transformer for Video Quality Assessment](https://mlsanity.com/paper/2210.04377/) (2022)
- [SB-VQA: A Stack-Based Video Quality Assessment Framework for Video Enhancement](https://mlsanity.com/paper/2305.08408/) (2023)
- [Deep Priors for Video Quality Prediction](https://mlsanity.com/paper/2410.22566/) (2024)
- [Exploring Simple Siamese Network for High-Resolution Video Quality Assessment](https://mlsanity.com/paper/2503.02330/) (2025)
- [MotionInsight: Diagnosing Object Motion Deficiencies in Generated Videos](https://mlsanity.com/paper/2609.37030/) (2026)

## Abstract (arXiv)

Many objective video quality assessment (VQA) algorithms include a key step of temporal pooling of frame-level quality scores. However, less attention has been paid to studying the relative efficiencies of different pooling methods on no-reference (blind) VQA. Here we conduct a large-scale comparative evaluation to assess the capabilities and limitations of multiple temporal pooling strategies on blind VQA of user-generated videos. The study yields insights and general guidance regarding the application and selection of temporal pooling models. In addition, we also propose an ensemble pooling model built on top of high-performing temporal pooling models. Our experimental results demonstrate the relative efficacies of the evaluated temporal pooling models, using several popular VQA algorithms, and evaluated on two recent large-scale natural video quality databases. In addition to the new ensemble model, we provide a general recipe for applying temporal pooling of frame-based quality predictions.