Author

Haobo Xiong

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Open access 2026

Balancing Visual Fidelity and Detection Reliability: Scalable Remote Sensing Image Compression

The surging volume of high-resolution remote sensing (RS) images and the limited transmission capacity of the satellite-to-ground link impose a pressing challenge on image compression: how to maintain higher reconstruction fidelity at lower bit rates without compromising the reliability of downstream vision tasks (e.g., object detection). To address this challenge, we propose an end-to-end scalable remote sensing image compression (SRSIC) framework. Considering that downstream tasks prioritize semantic structure while visual interpretation requires textural details, we adopt a scalable framework to decouple these features. Specifically, the compressed bitstream is divided into a base layer and an enhancement layer. The base layer is dedicated to compact semantic features optimized for object detection via a feature transfer network, bypassing the need for complete decoding. The enhancement layer supplements residual details for high-fidelity image reconstruction. Furthermore, considering the complex scale variations characteristic of RS images, we design a multiscale asymmetric codec to extract multiscale features and employ an adaptive context entropy model to minimize redundancy. Experimental results on the DIOR dataset demonstrate that SRSIC achieves significant bitrate savings, while maintaining better image reconstruction quality and higher object detection accuracy.

R. Tang, Pei-Cheng Zhou, Jia Jia et al. · IEEE Journal of Selected Top... · 0 citations

Preprint Aug 2026

CrossMambaTuning: Synergistic Spatial and Cross-Layer Adaptation for Machine Vision Compression

To reduce deployment cost and retraining overhead, adapting pretrained learned image compression (LIC) models to downstream machine vision tasks has attracted growing attention. However, existing methods typically insert fine-tuning modules independently into frozen backbones, lacking explicit mechanisms for cross-layer coordination. To address this limitation, we propose a novel framework named CrossMambaTuning, which integrates State Space Models with cross-layer interaction mechanisms for parameter-efficient fine-tuning. Specifically, we design an efficient Mamba adapter equipped with task-specific prompts and multi-scale branching to precisely capture both local features and global dependencies. Furthermore, we introduce a Scale-Invariant Cross-Layer Adapter (SICA) utilizing a parameter-sharing strategy to fuse task information across different scales and reduce redundancy. Extensive experiments demonstrate that CrossMambaTuning achieves state-of-the-art (SOTA) performance on multiple machine vision tasks, reducing parameter overhead by 72\% compared to SOTA methods. Code is available at https://github.com/rsr1123/CrossMambaTuning.

Haobo Xiong, Shaobo Liu, Kai Liu et al. · 0 citations