FineVQ: Fine-Grained User Generated Content Video Quality Assessment
This addresses the need for better quality monitoring and optimization in UGC video platforms, though it is incremental as it builds on existing VQA methods with a new database and model.
The authors tackled the lack of fine-grained video quality assessment (VQA) for user-generated content (UGC) videos by creating FineVD, a large-scale database with 6104 videos and fine-grained scores, and proposed the FineVQ model, which achieved state-of-the-art performance on FineVD and other datasets.
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.