A large fraction of Internet traffic is now driven by requests from mobile
devices with relatively small screens and often stringent bandwidth
requirements. Due to these factors, it has become the norm for modern
graphics-heavy websites to transmit low-resolution, low-bytecount image
previews (thumbnails) as part of the initial page load process to improve
apparent page responsiveness. Increasing thumbnail compression beyond the
capabilities of existing codecs is therefore a current research focus, as any
byte savings will significantly enhance the experience of mobile device users.
Toward this end, we propose a general framework for variable-rate image
compression and a novel architecture based on convolutional and deconvolutional
LSTM recurrent networks. Our models address the main issues that have prevented
autoencoder neural networks from competing with existing image compression
algorithms: (1) our networks only need to be trained once (not per-image),
regardless of input image dimensions and the desired compression rate; (2) our
networks are progressive, meaning that the more bits are sent, the more
accurate the image reconstruction; and (3) the proposed architecture is at
least as efficient as a standard purpose-trained autoencoder for a given number
of bits. On a large-scale benchmark of 32$\times$32 thumbnails, our LSTM-based
approaches provide better visual quality than (headerless) JPEG, JPEG2000 and
WebP, with a storage size that is reduced by 10% or more.

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。