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

Nick Johnston, Damien Vincent, David Minnen, Michele Covell, Saurabh Singh, Troy Chinen, Sung Jin Hwang, Joel Shor, George Toderici; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 4385-4393

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.