FineVQ: Fine-Grained User Generated Content Video Quality Assessment

TL;DR

This paper introduces FineVQ, a model for fine-grained quality assessment of user-generated videos, supported by a large-scale database, enabling detailed quality analysis beyond overall ratings.

Contribution

The paper presents the first large-scale fine-grained UGC video quality database and a novel model capable of detailed quality scoring and attribution.

Findings

FineVQ achieves state-of-the-art performance on multiple datasets.

The database FineVD contains 6104 videos with detailed quality annotations.

FineVQ provides fine-grained quality results for UGC videos.

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.