论文标题
使用解码器侧信息进行深度图像压缩
Deep Image Compression using Decoder Side Information
论文作者
论文摘要
我们提出了一个深层图像压缩神经网络，该神经网络依赖于侧面信息，该网络仅适用于解码器。我们基于算法的基础，假设可用于编码器的图像以及解码器可用的图像是相关的，并且我们让网络在训练阶段学习了这些相关性。 然后，在运行时，编码器侧编码输入图像，而无需了解解码器侧图像并将其发送到解码器。然后，解码器使用编码的输入图像和侧面信息图像来重建原始图像。 此问题被称为信息理论中的分布式源编码，我们讨论了该技术的几种用例。我们将算法与几种图像压缩算法进行比较，并表明添加仅解码器的侧面信息确实可以改善结果。我们的代码可在https://github.com/ayziksha/dsin上公开获取。
We present a Deep Image Compression neural network that relies on side information, which is only available to the decoder. We base our algorithm on the assumption that the image available to the encoder and the image available to the decoder are correlated, and we let the network learn these correlations in the training phase. Then, at run time, the encoder side encodes the input image without knowing anything about the decoder side image and sends it to the decoder. The decoder then uses the encoded input image and the side information image to reconstruct the original image. This problem is known as Distributed Source Coding in Information Theory, and we discuss several use cases for this technology. We compare our algorithm to several image compression algorithms and show that adding decoder-only side information does indeed improve results. Our code is publicly available at https://github.com/ayziksha/DSIN.
