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. Our code is made available at https://github.com/acnagle/neural-dsc.

翻译：分布式信源编码（DSC）的任务是在编码器无法获取相关边信息（该信息仅对解码器可用）的情况下对输入进行编码。值得注意的是，Slepian和Wolf在1973年证明，无法访问边信息的编码器可以渐近地达到与能够访问边信息时相同的压缩率。尽管该主题已有大量前期研究，但实用的DSC一直局限于合成数据集和特定的相关结构。本文提出了一种对相关结构不可知且能扩展到高维的有损DSC框架。我们的方法不依赖于手工设计的信源建模，而是利用条件向量量化变分自编码器（VQ-VAE）来学习分布式编码器和解码器。我们在多个数据集上评估了我们的方法，结果表明该方法能够处理复杂的相关性，并取得了最先进的峰值信噪比（PSNR）。我们的代码发布于 https://github.com/acnagle/neural-dsc。