MMIC: A Remote Sensing Image Compression Algorithm

Unlike natural images, remote sensing images have unique characteristics such as high spatial resolution, complex textures, and strong directional features. Their content often contains many man-made targets with clear directional structures, such as buildings, roads, and bridges. It also contains complex ground object boundaries. However, most existing image compression methods are designed for natural images. They typically use square convolution kernels and local receptive fields. As a result, they struggle to effectively capture the rich directional structures in remote sensing images and model global context information. This limits compression efficiency and the fidelity of key information. To address this challenge, this paper proposes a novel remote sensing image compression algorithm. The algorithm uses a multi-scale asymmetric convolution block that combines sampling convolution, parallel one-dimensional horizontal and vertical convolutions, and two-dimensional square convolution. This helps the model better capture directional objects and multi-scale features. In addition, we also propose a multi-scale non-local attention module. It models global dependencies with a linear computational complexity. This helps improve the ability to retain key information. The experimental results demonstrate that compared with the baseline model, the proposed algorithm achieves a 0.40 dB improvement in BD-PSNR and a 10.27% reduction in BD-Rate, while also delivering superior subjective visual quality. These results confirm the effectiveness of our approach for remote sensing image compression.