REMAC: Reference-Based Martian Asymmetrical Image Compression

TL;DR

REMAC is a novel reference-based image compression method designed for Martian conditions, shifting computational load to the decoder and utilizing inter- and intra-image similarities to improve efficiency and performance.

Contribution

It introduces a reference-guided entropy module and a multi-scale ref-decoder, significantly reducing encoder complexity while leveraging inter- and intra-image similarities for better compression.

Findings

Reduces encoder complexity by 43.51%

Achieves a BD-PSNR gain of 0.2664 dB

Outperforms state-of-the-art methods in Martian image compression

Abstract

To expedite space exploration on Mars, it is indispensable to develop an efficient Martian image compression method for transmitting images through the constrained Mars-to-Earth communication channel. Although the existing learned compression methods have achieved promising results for natural images from earth, there remain two critical issues that hinder their effectiveness for Martian image compression: 1) They overlook the highly-limited computational resources on Mars; 2) They do not utilize the strong \textit{inter-image} similarities across Martian images to advance image compression performance. Motivated by our empirical analysis of the strong \textit{intra-} and \textit{inter-image} similarities from the perspective of texture, color, and semantics, we propose a reference-based Martian asymmetrical image compression (REMAC) approach, which shifts computational complexity from the encoder to the resource-rich decoder and simultaneously improves compression performance. To leverage \textit{inter-image} similarities, we propose a reference-guided entropy module and a ref-decoder that utilize useful information from reference images, reducing redundant operations at the encoder and achieving superior compression performance. To exploit \textit{intra-image} similarities, the ref-decoder adopts a deep, multi-scale architecture with enlarged receptive field size to model long-range spatial dependencies. Additionally, we develop a latent feature recycling mechanism to further alleviate the extreme computational constraints on Mars. Experimental results show that REMAC reduces encoder complexity by 43.51\% compared to the state-of-the-art method, while achieving a BD-PSNR gain of 0.2664 dB.