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

TL;DR

This paper introduces the BSCV benchmark dataset and a recovery method for realistic bitstream-corrupted videos, addressing limitations of prior approaches that used artificial error masks.

Contribution

It presents the first large-scale dataset and a flexible recovery framework specifically designed for real-world bitstream corruption in videos.

Findings

State-of-the-art methods perform poorly on BSCV.

The proposed framework outperforms existing approaches.

BSCV captures diverse real-world error patterns.

Abstract

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.