发表机构
National Yang Ming Chiao Tung University; VNU-University of Engineering and Technology; Vietnam National University（国立阳明交通大学; 越南国家大学工程技术大学; 越南国家大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对H.266/VVC压缩视频的质量增强问题，提出含FPFT模块的MDFI方法，结合多域特征融合策略，在客观指标与视觉质量上优于现有最优方法，代码公开。
AI 中文摘要
最新视频编码标准H.266/VVC相比H.265/HEVC在压缩效率上有显著提升，但仍面临难以满足日益增长的更高感知质量与增强压缩性能需求的挑战。为解决这些局限，我们提出MDFI（Multi-Domain Features Integration，多域特征集成），一种压缩视频质量增强方法，其包含新颖的帧预测特征变换（FPFT）模块以处理预测信息。此外，MDFI集成多域特征融合策略，有效结合时空特性、跨频表示及压缩域预测信息，提升解码视频质量。我们还引入综合数据集，包含未压缩视频序列、对应多个QP等级的重构版本，以及从H.266/VVC压缩码流生成的预测帧，为开发和基准测试视频增强方法提供必要资源。大量实验表明，MDFI方法在客观指标和视觉质量上均优于现有最优方法，可有效缓解视频压缩伪影，代码可在该httpsURL获取。
英文摘要
The latest video coding standard, H.266/VVC, has demonstrated significant improvements in compression efficiency compared to H.265/HEVC. Despite its advanced coding techniques, H.266/VVC still faces challenges in meeting the increasing demand for higher perceptual quality and enhanced compression performance. To address these limitations, we propose MDFI (Multi-Domain Features Integration), a compressed video quality enhancement approach that features a novel Frame-Prediction Feature Transform (FPFT) module to process prediction information. Moreover, MDFI integrates a multi-domain feature fusion strategy that effectively combines spatiotemporal characteristics, cross-frequency representations, and compressed-domain prediction information to enhance decoded video quality. Additionally, we introduce a comprehensive dataset that encompasses uncompressed video sequences, corresponding reconstructed versions at multiple QP levels, and predicted frames generated from H.266/VVC compressed bitstreams, providing essential resources for developing and benchmarking video enhancement approaches. Extensive experiments demonstrate that our MDFI approach achieves superior performance to state-of-the-art methods in both objective metrics and visual quality, effectively mitigating video compression artifacts. The code is available at: https://github.com/dangdinh17/MDFI.git.
