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.

翻译：为加速火星探索进程，亟需开发一种高效的火星图像压缩方法，以通过受限的火星-地球通信信道传输图像。尽管现有学习型压缩方法在地球自然图像上已取得良好效果，但其应用于火星图像压缩仍存在两个关键问题：1）未充分考虑火星上高度受限的计算资源；2）未利用火星图像间强烈的图像间相似性来提升压缩性能。基于对火星图像在纹理、色彩和语义层面呈现的显著图像内与图像间相似性的实证分析，本文提出一种基于参考的火星非对称图像压缩（REMAC）方法，该方法将计算复杂度从编码器转移至资源充裕的解码器，同时提升压缩性能。为利用图像间相似性，我们提出参考引导的熵模块与参考解码器，通过利用参考图像中的有效信息，减少编码器的冗余操作并获得更优的压缩性能。为挖掘图像内相似性，参考解码器采用具有扩大感受野的深度多尺度架构，以建模长程空间依赖关系。此外，我们开发了潜在特征循环机制，以进一步缓解火星端的极端计算约束。实验结果表明，与现有最优方法相比，REMAC在编码器复杂度降低43.51%的同时，实现了0.2664 dB的BD-PSNR增益。