@inproceedings{
zhang2024spatialtemporal,
title={Spatial-Temporal Context Model for Remote Sensing Imagery Compression},
author={Jinxiao Zhang and Runmin Dong and Juepeng Zheng and Mengxuan Chen and Lixian Zhang and Yi Zhao and Haohuan Fu},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=YTNN0mOPQN}
}
With the increasing spatial and temporal resolutions of obtained remote sensing (RS) images, effective compression becomes critical for storage, transmission, and large-scale in-memory processing. Although image compression methods achieve a series of breakthroughs for daily images, a straightforward application of these methods to RS domain underutilizes the properties of the RS images, such as content duplication, homogeneity, and temporal redundancy. This paper proposes a Spatial-Temporal Context model (STCM) for RS image compression, jointly leveraging context from a broader spatial scope and across different temporal images. Specifically, we propose a stacked diagonal masked module to expand the contextual reference scope, which is stackable and maintains its parallel capability. Furthermore, we propose spatial-temporal contextual adaptive coding to enable the entropy estimation to reference context across different temporal RS images at the same geographic location. Experiments show that our method outperforms previous state-of-the-art compression methods on rate-distortion (RD) performance. For downstream tasks validation, our method reduces the bitrate by 52 times for single temporal images in the scene classification task while maintaining accuracy.
Spatial-Temporal Context Model for Remote Sensing Imagery Compression
Jinxiao Zhang Runmin Dong Juepeng Zheng Mengxuan Chen Lixian Zhang Yi Zhao Haohuan Fu