The past decade has witnessed great strides in video recovery by specialist technologies, like video inpainting, completion, and error concealment. However, they typically simulate the missing content by manual-designed error masks, thus failing to fill in the realistic video loss in video communication (e.g., telepresence, live streaming, and internet video) and multimedia forensics. To address this, we introduce the bitstream-corrupted video (BSCV) benchmark, the first benchmark dataset with more than 28,000 video clips, which can be used for bitstream-corrupted video recovery in the real world. The BSCV is a collection of 1) a proposed three-parameter corruption model for video bitstream, 2) a large-scale dataset containing rich error patterns, multiple corruption levels, and flexible dataset branches, and 3) a plug-and-play module in video recovery framework that serves as a benchmark. We evaluate state-of-the-art video inpainting methods on the BSCV dataset, demonstrating existing approaches' limitations and our framework's advantages in solving the bitstream-corrupted video recovery problem. The benchmark and dataset are released at https://github.com/LIUTIGHE/BSCV-Dataset.

翻译：过去十年间，专业视频恢复技术（如视频修复、补全及错误掩盖）取得了显著进展。然而，这些技术通常通过人工设计的误差掩码模拟缺失内容，因此难以应对视频通信（如远程呈现、直播流媒体、互联网视频）及多媒体取证中实际发生的视频丢帧问题。为此，我们提出比特流损坏视频（BSCV）基准数据集——首个包含超过28,000个视频片段的基准数据集，可用于真实场景下的比特流损坏视频恢复。BSCV集合了以下内容：1）提出的视频比特流三参数损坏模型；2）包含丰富错误模式、多级损坏程度及灵活数据集分支的大规模数据集；3）视频恢复框架中的即插即用模块，可作为基准测试工具。我们在BSCV数据集上评估了当前最先进的视频修复方法，揭示了现有方法的局限性，并展示了本框架在解决比特流损坏视频恢复问题上的优势。基准测试及数据集已发布于https://github.com/LIUTIGHE/BSCV-Dataset。