Improved Lossy Image Compression With Priming and Spatially Adaptive Bit Rates for Recurrent Networks

We propose a method for lossy image compression based on recurrent, convolutional neural networks that outper- forms BPG (4:2:0), WebP, JPEG2000, and JPEG as mea- sured by MS-SSIM. We introduce three improvements over previous research that lead to this state-of-the-art result us- ing a single model. First, we modify the recurrent architec- ture to improve spatial diffusion, which allows the network to more effectively capture and propagate image informa- tion through the network’s hidden state. Second, in addition to lossless entropy coding, we use a spatially adaptive bit allocation algorithm to more efficiently use the limited num- ber of bits to encode visually complex image regions. Fi- nally, we show that training with a pixel-wise loss weighted by SSIM increases reconstruction quality according to sev- eral metrics. We evaluate our method on the Kodak and Tecnick image sets and compare against standard codecs as well as recently published methods based on deep neural networks.