Summary of Robust Multiple Description Neural Video Codec with Masked Transformer For Dynamic and Noisy Networks, by Xinyue Hu et al.
Robust Multiple Description Neural Video Codec with Masked Transformer for Dynamic and Noisy Networks
by Xinyue Hu, Wei Ye, Jiaxiang Tang, Eman Ramadan, Zhi-Li Zhang
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel Multiple Description Coding (MDC) video codec, called NeuralMDC, which leverages bidirectional transformers to simplify the design of MDC video codecs. The proposed method tokenizes each frame into its latent representation and splits it into multiple descriptions containing correlated information. Instead of using motion prediction and warping operations, NeuralMDC trains a bidirectional masked transformer to model spatial-temporal dependencies and predict the distribution of the current representation based on past representations. This predicted distribution is used for entropy coding and inferring lost tokens. Experimental results show that NeuralMDC achieves state-of-the-art loss resilience with minimal compression efficiency sacrifices, outperforming existing residual-coding-based error-resilient neural video codecs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make a type of video compression method called Multiple Description Coding (MDC) better. MDC is useful for sending videos through networks that can get broken or lost data. The current way of doing MDC is complicated and not very good. This paper suggests a new way, called NeuralMDC, which uses special computer algorithms to make the process simpler and more efficient. They tested it and found that it works really well at protecting against lost or corrupted video frames while still keeping the file size small. |
Keywords
» Artificial intelligence » Transformer
