This paper presents FCM-ULB, a feature coding framework designed for machine vision in extremely low-bitrate scenarios. It introduces a spatial-channel attention (SC-Attn) block to better capture global feature dependencies, enabling aggressive downsampling while preserving task-relevant information. In addition, a three-step training strategy—warm-up of vision backbone, rate-distortion optimization of feature compression, and joint fine-tuning—further improves compression and maintains task accuracy. Experiments on COCO and OpenImages show that FCM-ULB achieves much higher accuracy than existing standards like FCTMv4 at compression ratios up to$10,000 \times$, demonstrating its effectiveness for bandwidth-limited vision applications.

With advancement of deep space exploration, vast amounts of image data must be transmitted back to Earth for scientific research. Current deep-space image codecs rely on conventional progressive coding algorithms, but offer limited compression performance. Despite great success on natural images achieved by learning-based compression methods, the high computational complexity restrains their application in the deep space missions and they are unable to cope with packet loss caused by severe noise interference during transmission. Motivated by the urgent need and technical challenges, we take Mars as a representative case and propose a novel image compression and transmission framework that innovatively incorporates learning-based strategies to deliver both low-complexity and error-resilient source coding. To adapt learning-based methods to the stringent constraints and high packet loss of deep space environment, we first establish a new Martian image dataset with high resolution and diversity, and analyze its characteristics to guide the network design. With heterogeneous textures yet synergistic structures as well as higher inter-channel similarity in the feature domain revealed for the Martian images, we develop a Martian Vision Adaptive Transformation Module (MVATM) with efficient low-complexity compression. Furthermore, unlike conventional one-stage training, a novel two-stage training strategy with Joint Channel Training (JCT) is proposed to enhance error resilience. Experimental results and hardware deployment strongly validate that our method achieves a better rate-distortion-complexity (RDC) trade-offs than other advanced learning-based models and significantly outperforms conventional methods in deep space simulation test. Also, the technical strategies proposed herein can offer methodological insights for deep space and other resource-constrained fields.