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The past decade has witnessed great strides in video recovery by specialisttechnologies, like video inpainting, completion, and error concealment.However, they typically simulate the missing content by manual-designed errormasks, thus failing to fill in the realistic video loss in video communication(e.g., telepresence, live streaming, and internet video) and multimediaforensics. To address this, we introduce the bitstream-corrupted video (BSCV)benchmark, the first benchmark dataset with more than 28,000 video clips, whichcan be used for bitstream-corrupted video recovery in the real world. The BSCVis a collection of 1) a proposed three-parameter corruption model for videobitstream, 2) a large-scale dataset containing rich error patterns, multiplecorruption levels, and flexible dataset branches, and 3) a plug-and-play modulein video recovery framework that serves as a benchmark. We evaluatestate-of-the-art video inpainting methods on the BSCV dataset, demonstratingexisting approaches' limitations and our framework's advantages in solving thebitstream-corrupted video recovery problem. The benchmark and dataset arereleased at https://github.com/LIUTIGHE/BSCV-Dataset.

Bitstream-Corrupted Video Recovery: A Novel Benchmark Dataset and Method