FineVQ: Fine-Grained User Generated Content Video Quality Assessment
Journal:
arXiv
Published Date:
Dec 26, 2024
Abstract
The rapid growth of user-generated content (UGC) videos has produced an
urgent need for effective video quality assessment (VQA) algorithms to monitor
video quality and guide optimization and recommendation procedures. However,
current VQA models generally only give an overall rating for a UGC video, which
lacks fine-grained labels for serving video processing and recommendation
applications. To address the challenges and promote the development of UGC
videos, we establish the first large-scale Fine-grained Video quality
assessment Database, termed FineVD, which comprises 6104 UGC videos with
fine-grained quality scores and descriptions across multiple dimensions. Based
on this database, we propose a Fine-grained Video Quality assessment (FineVQ)
model to learn the fine-grained quality of UGC videos, with the capabilities of
quality rating, quality scoring, and quality attribution. Extensive
experimental results demonstrate that our proposed FineVQ can produce
fine-grained video-quality results and achieve state-of-the-art performance on
FineVD and other commonly used UGC-VQA datasets.
