# XGC-VQA: A unified video quality assessment model for User, Professionally, and Occupationally-Generated Content

arXiv: https://arxiv.org/abs/2303.13859 · HTML: https://mlsanity.com/paper/2303.13859/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2023.

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

- [AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2404.03407/) (2024)
- [Q-Refine: A Perceptual Quality Refiner for AI-Generated Image](https://mlsanity.com/paper/2401.01117/) (2024)

## Abstract (arXiv)

With the rapid growth of Internet video data amounts and types, a unified Video Quality Assessment (VQA) is needed to inspire video communication with perceptual quality. To meet the real-time and universal requirements in providing such inspiration, this study proposes a VQA model from a classification of User Generated Content (UGC), Professionally Generated Content (PGC), and Occupationally Generated Content (OGC). In the time domain, this study utilizes non-uniform sampling, as each content type has varying temporal importance based on its perceptual quality. In the spatial domain, centralized downsampling is performed before the VQA process by utilizing a patch splicing/sampling mechanism to lower complexity for real-time assessment. The experimental results demonstrate that the proposed method achieves a median correlation of 0.7 while limiting the computation time below 5s for three content types, which ensures that the communication experience of UGC, PGC, and OGC can be optimized altogether.