Improved Lossy Image Compression with Priming and Spatially Adaptive Bit
Rates for Recurrent Networks
Abstract
We propose a method for lossy image compression based
on recurrent, convolutional neural networks that outperforms BPG (4:2:0), WebP, JPEG2000, and JPEG as measured by MS-SSIM. We introduce three improvements over
previous research that lead to this state-of-the-art result using a single model. First, we modify the recurrent architecture to improve spatial diffusion, which allows the network
to more effectively capture and propagate image information 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 number of bits to encode visually complex image regions. Finally, we show that training with a pixel-wise loss weighted
by SSIM increases reconstruction quality according to multiple 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