# SB-VQA: A Stack-Based Video Quality Assessment Framework for Video Enhancement

arXiv: https://arxiv.org/abs/2305.08408 · HTML: https://mlsanity.com/paper/2305.08408/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 53 references that are in the map.
- First posted on arXiv 15 May 2023.

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

- [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)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2204.08958/) (2022)
- [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019)
- [BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment](https://mlsanity.com/paper/2104.13371/) (2021)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019)
- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [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)
- [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)
- [A Deep Learning based No-reference Quality Assessment Model for UGC Videos](https://mlsanity.com/paper/2204.14047/) (2022)
- [Large-Scale Study of Perceptual Video Quality](https://mlsanity.com/paper/1803.01761/) (2018)
- [YouTube UGC Dataset for Video Compression Research](https://mlsanity.com/paper/1904.06457/) (2019)
- [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)
- [Learned Video Compression](https://mlsanity.com/paper/1811.06981/) (2018)
- [Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training](https://mlsanity.com/paper/2011.04263/) (2020)
- [Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment](https://mlsanity.com/paper/2210.05357/) (2022)
- [Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment](https://mlsanity.com/paper/2207.03723/) (2022)
- [Subjective and Objective Quality Assessment of High Frame Rate Videos](https://mlsanity.com/paper/2007.11634/) (2020)
- [A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment](https://mlsanity.com/paper/2002.10651/) (2020)
- [VDPVE: VQA Dataset for Perceptual Video Enhancement](https://mlsanity.com/paper/2303.09290/) (2023)

## Built on by (most cited first, 2 of 2)

- [NTIRE 2023 Quality Assessment of Video Enhancement Challenge](https://mlsanity.com/paper/2307.09729/) (2023)
- [RMT-BVQA: Recurrent Memory Transformer-based Blind Video Quality Assessment for Enhanced Video Content](https://mlsanity.com/paper/2405.08621/) (2024)

## Abstract (arXiv)

In recent years, several video quality assessment (VQA) methods have been developed, achieving high performance. However, these methods were not specifically trained for enhanced videos, which limits their ability to predict video quality accurately based on human subjective perception. To address this issue, we propose a stack-based framework for VQA that outperforms existing state-of-the-art methods on VDPVE, a dataset consisting of enhanced videos. In addition to proposing the VQA framework for enhanced videos, we also investigate its application on professionally generated content (PGC). To address copyright issues with premium content, we create the PGCVQ dataset, which consists of videos from YouTube. We evaluate our proposed approach and state-of-the-art methods on PGCVQ, and provide new insights on the results. Our experiments demonstrate that existing VQA algorithms can be applied to PGC videos, and we find that VQA performance for PGC videos can be improved by considering the plot of a play, which highlights the importance of video semantic understanding.