用AI模型提升VVC压缩视频画质，可省19.6%码率。
Enhancing Quality for VVC Compressed Videos with Omniscient Quality Enhancement Model
- 基于跨频域与时空特征的全能型增强网络
- 画质提升达0.74 dB，最高1.2 dB
- 适合追求画质效率的视频编解码研究者
最新视频编码标准H.266/VVC相比前代HEVC在压缩性能上显著提升。尽管采用了诸多先进技巧，但其仍面临解码端对更高感知质量的需求以及编码端压缩效率的挑战。人工智能技术，尤其是基于深度学习的视频质量增强方法，展现出巨大潜力。本文提出一种针对VVC压缩视频的新型全能型质量增强网络（OVQE-VVC）。该模型在原始用于HEVC的OVQE基础上进行改进，并集成至最新STD-VVC解码器架构中。在多种测试条件下评估表明，所提OVQE-VVC方案可实现显著的PSNR提升，平均约0.74 dB，最高达1.2 dB；同时，在保持相近画质的前提下，可节省约19.6%的码率。
原文摘要 · Abstract (English)
The latest video coding standard H.266/VVC has shown its great improvement in terms of compression performance when compared to its predecessor HEVC standard. Though VVC was implemented with many advanced techniques, it still met the same challenges as its predecessor due to the need for even higher perceptual quality demand at the decoder side as well as the compression performance at the encoder side. The advancement of Artificial Intelligence (AI) technology, notably the deep learning-based video quality enhancement methods, was shown to be a promising approach to improving the perceptual quality experience. In this paper, we propose a novel Omniscient video quality enhancement Network for VVC compressed Videos. The Omniscient Network for compressed video quality enhancement was originally designed for HEVC compressed videos in which not only the spatial-temporal features but also cross-frequencies information were employed to augment the visual quality. Inspired by this work, we propose a modification of the OVQE model and integrate it into the lasted STD-VVC (Standard Versatile Video Coding) decoder architecture. As assessed in a rich set of test conditions, the proposed OVQE-VVC solution is able to achieve significant PSNR improvement, notably around 0.74 dB and up to 1.2 dB with respect to the original STD-VVC codec. This also corresponds to around 19.6% of bitrate saving while keeping a similar quality observation.
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