  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.
