Although image compression has been actively studied for decades, there has been relatively little research on learning to compress images with modern neural networks. Standard approaches, such as those employing patch-based autoencoders, have shown a great deal of promise but cannot compete with popular image codecs because they fail to address three questions: 1) how to effectively binarize activations: in the absence of binarization, a bottleneck layer alone tends not to lead to efficient compression; 2) how to achieve variable-rate encoding: a standard autoencoder generates a fixed-length code for each fixed-resolution input patch, resulting in the same cost for low- and high-entropy patches, and requiring the network to be completely retrained to achieve different compression rates; and 3) how to avoid block artifacts: patch-based approaches are prone to block discontinuities. We propose a general framework for variable-rate image compression and a novel architecture based on convolutional and deconvolutional recurrent networks, including LSTMs, that address these issues and report promising results compared to existing baseline codecs. We evaluate the proposed methods on a large-scale benchmark consisting of tiny images (32$\times$32), which proves to be very challenging for all the methods.

本文提出了一种基于卷积LSTM神经网络的可变比率图像压缩框架和架构，可以用于压缩缩略图以改善移动设备用户的体验，经测试在32×32的缩略图上，与JPEG、JPEG2000和WebP相比，我们的方法在相同存储大小下提供更好的视觉质量。

利用循环神经网络进行可变率图像压缩