Distributed source coding (DSC) is the task of encoding an input in the absence of correlated side information that is only available to the decoder. Remarkably, Slepian and Wolf showed in 1973 that an encoder without access to the side information can asymptotically achieve the same compression rate as when the side information is available to it. While there is vast prior work on this topic, practical DSC has been limited to synthetic datasets and specific correlation structures. Here we present a framework for lossy DSC that is agnostic to the correlation structure and can scale to high dimensions. Rather than relying on hand-crafted source modeling, our method utilizes a conditional Vector-Quantized Variational Autoencoder (VQ-VAE) to learn the distributed encoder and decoder. We evaluate our method on multiple datasets and show that our method can handle complex correlations and achieves state-of-the-art PSNR.

翻译：分布式信源编码（DSC）是指在解码器端拥有相关边信息而编码器端缺失该信息的编码任务。值得注意的是，Slepian和Wolf于1973年证明，当编码器无法获取边信息时，仍能渐进达到与编码器掌握边信息时相同的压缩率。尽管已有大量相关研究，实际DSC仍局限于合成数据集和特定相关结构。本文提出一种与相关结构无关且可扩展至高维的有损DSC框架。该方法无需手工构建信源模型，而是利用条件向量量化变分自编码器（VQ-VAE）学习分布式编码器与解码器。我们在多个数据集上评估该方法，结果表明其能处理复杂相关性，并达到当前最优的峰值信噪比（PSNR）。