Efficient Progressive Image Compression with Variance-aware Masking

TL;DR

This paper introduces a variance-aware masking approach for progressive image compression that allows flexible quality reconstruction with reduced complexity, outperforming many existing methods.

Contribution

It proposes a novel masking system for element-wise importance ranking in residual latent representations, enabling efficient progressive decoding without extra parameters.

Findings

Achieves competitive compression quality with fewer parameters.

Reduces decoding time and computational complexity.

Introduces Rate Enhancement Modules for better entropy estimation.

Abstract

Learned progressive image compression is gaining momentum as it allows improved image reconstruction as more bits are decoded at the receiver. We propose a progressive image compression method in which an image is first represented as a pair of base-quality and top-quality latent representations. Next, a residual latent representation is encoded as the element-wise difference between the top and base representations. Our scheme enables progressive image compression with element-wise granularity by introducing a masking system that ranks each element of the residual latent representation from most to least important, dividing it into complementary components, which can be transmitted separately to the decoder in order to obtain different reconstruction quality. The masking system does not add further parameters nor complexity. At the receiver, any elements of the top latent representation excluded from the transmitted components can be independently replaced with the mean predicted by the hyperprior architecture, ensuring reliable reconstructions at any intermediate quality level. We also introduced Rate Enhancement Modules (REMs), which refine the estimation of entropy parameters using already decoded components. We obtain results competitive with state-of-the-art competitors, while significantly reducing computational complexity, decoding time, and number of parameters.