首个大规模HDR视频数据集与可扩展压缩算法，提升HDR视频编码质量。
High Dynamic Range Video Compression: A Large-Scale Benchmark Dataset and A Learned Bit-depth Scalable Compression Algorithm
- 基于多动态范围冗余设计可伸缩压缩网络，利用低动态范围视频预测原始HDR内容。
- 在HDRVD2K数据集上实现更高重建质量与压缩效率，峰值信噪比提升显著。
- 适合研究视频压缩、高动态范围成像及端到端学习编码的学者与工程师。
近年来，学习型视频压缩（LVC）快速发展，但因缺乏大规模高质量的高动态范围（HDR）视频训练数据，该领域仍处于空白。本文首次构建了大规模HDR视频基准数据集HDRVD2K，包含2000段视频，涵盖丰富场景与多种运动类型，填补了视频训练数据的空白，推动了HDR视频的LVC发展。基于此，我们提出首个针对HDR视频的可学习比特深度可伸缩压缩（LBSVC）网络，通过有效利用多动态范围间的比特深度冗余实现压缩。为此，我们设计压缩友好的比特深度增强模块（BEM），基于压缩后的色调映射低动态范围（LDR）视频与动态范围先验，直接预测原始HDR视频，而非仅依赖时空预测降低冗余。大量实验表明，该方法显著提升了HDR视频的重建质量与压缩性能。代码与数据集将开源于https://github.com/sdkinda/HDR-Learned-Video-Coding。
原文摘要 · Abstract (English)
Recently, learned video compression (LVC) is undergoing a period of rapid development. However, due to absence of large and high-quality high dynamic range (HDR) video training data, LVC on HDR video is still unexplored. In this paper, we are the first to collect a large-scale HDR video benchmark dataset, named HDRVD2K, featuring huge quantity, diverse scenes and multiple motion types. HDRVD2K fills gaps of video training data and facilitate the development of LVC on HDR videos. Based on HDRVD2K, we further propose the first learned bit-depth scalable video compression (LBSVC) network for HDR videos by effectively exploiting bit-depth redundancy between videos of multiple dynamic ranges. To achieve this, we first propose a compression-friendly bit-depth enhancement module (BEM) to effectively predict original HDR videos based on compressed tone-mapped low dynamic range (LDR) videos and dynamic range prior, instead of reducing redundancy only through spatio-temporal predictions. Our method greatly improves the reconstruction quality and compression performance on HDR videos. Extensive experiments demonstrate the effectiveness of HDRVD2K on learned HDR video compression and great compression performance of our proposed LBSVC network. Code and dataset will be released in https://github.com/sdkinda/HDR-Learned-Video-Coding.
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