Compressed Video Quality Enhancement: Classifying and Benchmarking over Standards

2025-09-12

AI Summary

Current CVQE research suffers from three key limitations: (1) the absence of a systematic taxonomy linking video coding standards to compression artifacts; (2) a lack of cross-standard architectural comparisons (e.g., H.264/AVC, H.265/HEVC, H.266/VVC); and (3) fragmented, non-reproducible benchmarking practices. To address these, we propose the first classification framework explicitly modeling the coupling between compression-domain features and coding standards. We establish a unified, multi-standard (H.264–H.266), multi-sequence, multi-metric evaluation benchmark. Furthermore, we conduct fair, architecture-agnostic, and codec-agnostic performance assessment of state-of-the-art deep learning-based CVQE methods. Experimental analysis uncovers fundamental trade-offs between accuracy and computational complexity, yielding reproducible empirical insights for model selection and algorithm design. All components—including benchmarks, evaluation protocols, and open-source tooling—are publicly released to foster standardized, transparent, and comparable CVQE research.

Technology Category

• Computer Vision: Image and Video Retrieval
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• Classifying CVQE methods across standards and artifacts
• Benchmarking framework for fair multi-criteria evaluation
• Analyzing performance-complexity trade-offs in enhancement methods

Innovation

• Novel taxonomy classifying CVQE architectural paradigms
• Unified benchmarking framework with compression protocols
• Analysis of reconstruction-complexity trade-offs in methods