AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results
This addresses video quality assessment for user-generated content, but it is incremental as it surveys existing methods in a challenge setting.
The paper reviews the AIS 2024 Video Quality Assessment Challenge, which tackled the problem of estimating perceptual quality for user-generated videos, resulting in 15 submissions from 102 participants with top-5 methods analyzed.
This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based methods capable of estimating the perceptual quality of UGC videos. The user-generated videos from the YouTube UGC Dataset include diverse content (sports, games, lyrics, anime, etc.), quality and resolutions. The proposed methods must process 30 FHD frames under 1 second. In the challenge, a total of 102 participants registered, and 15 submitted code and models. The performance of the top-5 submissions is reviewed and provided here as a survey of diverse deep models for efficient video quality assessment of user-generated content.