MDFI: A Multi-Domain Features Integration for Compressed Video Quality Enhancement

• URL: http://arxiv.org/abs/2608.21495v1
• Date: Fri, 21 Aug 2026 15:04:06 GMT
• Title: MDFI: A Multi-Domain Features Integration for Compressed Video Quality Enhancement
• Abstract summary: MDFI (Multi-Domain Features Integration) is a compressed video quality enhancement approach.<n>It integrates a novel Frame-Prediction Feature Transform (FPFT) module to process prediction information.<n>Extensive experiments demonstrate that our MDFI approach achieves superior performance to state-of-the-art methods in both objective metrics and visual quality.
• Score: 0.9099663022952496
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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