系统分类压缩视频增强方法，建立统一评估框架。
Compressed Video Quality Enhancement: Classifying and Benchmarking over Standards
- 按架构、标准、特征使用方式分类方法
- 构建整合多编码标准的统一评测平台
- 分析性能与计算复杂度权衡，指导未来研究
压缩视频质量增强（CVQE）对提升H.264/AVC、H.265/HEVC和H.266/VVC等有损编码器的用户体验至关重要。尽管基于深度学习的CVQE已取得显著进展，现有综述仍存在局限：缺乏将方法与具体编码标准及失真特征关联的系统性分类，跨编码类型架构范式的比较分析不足，基准测试实践不完善。本文提出三项关键贡献：首先，构建新颖分类体系，涵盖架构范式、编码标准与压缩域特征利用方式；其次，设计统一基准框架，集成现代压缩协议与标准测试序列，实现公平的多指标评估；第三，系统分析前沿方法在重建性能与计算复杂度间的权衡，并指出未来研究的可行方向。本综述旨在为CVQE研究与部署建立一致评估基础，支持模型合理选型。
原文摘要 · Abstract (English)
Compressed video quality enhancement (CVQE) is crucial for improving user experience with lossy video codecs like H.264/AVC, H.265/HEVC, and H.266/VVC. While deep learning based CVQE has driven significant progress, existing surveys still suffer from limitations: lack of systematic classification linking methods to specific standards and artifacts, insufficient comparative analysis of architectural paradigms across coding types, and underdeveloped benchmarking practices. To address these gaps, this paper presents three key contributions. First, it introduces a novel taxonomy classifying CVQE methods across architectural paradigms, coding standards, and compressed-domain feature utilization. Second, it proposes a unified benchmarking framework integrating modern compression protocols and standard test sequences for fair multi-criteria evaluation. Third, it provides a systematic analysis of the critical trade-offs between reconstruction performance and computational complexity observed in state-of-the-art methods and highlighting promising directions for future research. This comprehensive review aims to establish a foundation for consistent assessment and informed model selection in CVQE research and deployment.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。