  Lossy image compression is essential for Mars exploration missions, due to
the limited bandwidth between Earth and Mars. However, the compression may
introduce visual artifacts that complicate the geological analysis of the
Martian surface. Existing quality enhancement approaches, primarily designed
for Earth images, fall short for Martian images due to a lack of consideration
for the unique Martian semantics. In response to this challenge, we conduct an
in-depth analysis of Martian images, yielding two key insights based on
semantics: the presence of texture similarities and the compact nature of
texture representations in Martian images. Inspired by these findings, we
introduce MarsQE, an innovative, semantic-informed, two-phase quality
enhancement approach specifically designed for Martian images. The first phase
involves the semantic-based matching of texture-similar reference images, and
the second phase enhances image quality by transferring texture patterns from
these reference images to the compressed image. We also develop a
post-enhancement network to further reduce compression artifacts and achieve
superior compression quality. Our extensive experiments demonstrate that MarsQE
significantly outperforms existing approaches for Earth images, establishing a
new benchmark for the quality enhancement on Martian images.
