# A Multi-annotated and Multi-modal Dataset for Wide-angle Video Quality Assessment

arXiv: https://arxiv.org/abs/2501.12082 · HTML: https://mlsanity.com/paper/2501.12082/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 21 Jan 2025.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives](https://mlsanity.com/paper/2211.04894/) (2022)
- [FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling](https://mlsanity.com/paper/2207.02595/) (2022)
- [Patch-VQ: 'Patching Up' the Video Quality Problem](https://mlsanity.com/paper/2011.13544/) (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)
- [Learning Generalized Spatial-Temporal Deep Feature Representation for No-Reference Video Quality Assessment](https://mlsanity.com/paper/2012.13936/) (2020)
- [MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos](https://mlsanity.com/paper/2303.14933/) (2023)
- [Modular Blind Video Quality Assessment](https://mlsanity.com/paper/2402.19276/) (2024)
- [One Transform To Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment](https://mlsanity.com/paper/2304.03412/) (2023)
- [Harmonized Multimodal Learning with Gaussian Process Latent Variable Models](https://mlsanity.com/paper/1908.04979/) (2019)
- [Blind Multimodal Quality Assessment of Low-light Images](https://mlsanity.com/paper/2303.10369/) (2023)

## Built on by (most cited first, 1 of 1)

- [Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality](https://mlsanity.com/paper/2505.19696/) (2025)

## Abstract (arXiv)

Wide-angle video is favored for its wide viewing angle and ability to capture a large area of scenery, making it an ideal choice for sports and adventure recording. However, wide-angle video is prone to deformation, exposure and other distortions, resulting in poor video quality and affecting the perception and experience, which may seriously hinder its application in fields such as competitive sports. Up to now, few explorations focus on the quality assessment issue of wide-angle video. This deficiency primarily stems from the absence of a specialized dataset for wide-angle videos. To bridge this gap, we construct the first Multi-annotated and multi-modal Wide-angle Video quality assessment (MWV) dataset. Then, the performances of state-of-the-art video quality methods on the MWV dataset are investigated by inter-dataset testing and intra-dataset testing. Experimental results show that these methods impose significant limitations on their applicability.