# VDPVE: VQA Dataset for Perceptual Video Enhancement

arXiv: https://arxiv.org/abs/2303.09290 · HTML: https://mlsanity.com/paper/2303.09290/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2023.

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

- [AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2306.04717/) (2023)
- [AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2404.03407/) (2024)
- [Advancing Zero-Shot Digital Human Quality Assessment through Text-Prompted Evaluation](https://mlsanity.com/paper/2307.02808/) (2023)
- [Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment](https://mlsanity.com/paper/2304.06440/) (2023)
- [Q-Refine: A Perceptual Quality Refiner for AI-Generated Image](https://mlsanity.com/paper/2401.01117/) (2024)
- [NTIRE 2024 Quality Assessment of AI-Generated Content Challenge](https://mlsanity.com/paper/2404.16687/) (2024)
- [Enhancing Blind Video Quality Assessment with Rich Quality-aware Features](https://mlsanity.com/paper/2405.08745/) (2024)
- [UVEB: A Large-scale Benchmark and Baseline Towards Real-World Underwater Video Enhancement](https://mlsanity.com/paper/2404.14542/) (2024)
- [StableVQA: A Deep No-Reference Quality Assessment Model for Video Stability](https://mlsanity.com/paper/2308.04904/) (2023)
- [Light-VQA: A Multi-Dimensional Quality Assessment Model for Low-Light Video Enhancement](https://mlsanity.com/paper/2305.09512/) (2023)
- [Image Quality Assessment: From Human to Machine Preference](https://mlsanity.com/paper/2503.10078/) (2025)
- [NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Results](https://mlsanity.com/paper/2505.03007/) (2025)
- [VQAThinker: Exploring Generalizable and Explainable Video Quality Assessment via Reinforcement Learning](https://mlsanity.com/paper/2508.06051/) (2025)
- [NTIRE 2023 Quality Assessment of Video Enhancement Challenge](https://mlsanity.com/paper/2307.09729/) (2023)
- [Light-VQA+: A Video Quality Assessment Model for Exposure Correction with Vision-Language Guidance](https://mlsanity.com/paper/2405.03333/) (2024)
- [DeTurb: Atmospheric Turbulence Mitigation with Deformable 3D Convolutions and 3D Swin Transformers](https://mlsanity.com/paper/2407.20855/) (2024)
- [SB-VQA: A Stack-Based Video Quality Assessment Framework for Video Enhancement](https://mlsanity.com/paper/2305.08408/) (2023)
- [RMT-BVQA: Recurrent Memory Transformer-based Blind Video Quality Assessment for Enhanced Video Content](https://mlsanity.com/paper/2405.08621/) (2024)
- [QualiRAG: Retrieval-Augmented Generation for Visual Quality Understanding](https://mlsanity.com/paper/2601.18195/) (2026)
- [Subjective and Objective Quality-of-Experience Evaluation Study for Live Video Streaming](https://mlsanity.com/paper/2409.17596/) (2024)
- [V-Trans4Style: Visual Transition Recommendation for Video Production Style Adaptation](https://mlsanity.com/paper/2501.07983/) (2025)
- [VersusQ: Pairwise Margin Reasoning for Generalizable Video Quality Assessment](https://mlsanity.com/paper/2605.21130/) (2026)

## Abstract (arXiv)

Recently, many video enhancement methods have been proposed to improve video quality from different aspects such as color, brightness, contrast, and stability. Therefore, how to evaluate the quality of the enhanced video in a way consistent with human visual perception is an important research topic. However, most video quality assessment methods mainly calculate video quality by estimating the distortion degrees of videos from an overall perspective. Few researchers have specifically proposed a video quality assessment method for video enhancement, and there is also no comprehensive video quality assessment dataset available in public. Therefore, we construct a Video quality assessment dataset for Perceptual Video Enhancement (VDPVE) in this paper. The VDPVE has 1211 videos with different enhancements, which can be divided into three sub-datasets: the first sub-dataset has 600 videos with color, brightness, and contrast enhancements; the second sub-dataset has 310 videos with deblurring; and the third sub-dataset has 301 deshaked videos. We invited 21 subjects (20 valid subjects) to rate all enhanced videos in the VDPVE. After normalizing and averaging the subjective opinion scores, the mean opinion score of each video can be obtained. Furthermore, we split the VDPVE into a training set, a validation set, and a test set, and verify the performance of several state-of-the-art video quality assessment methods on the test set of the VDPVE.