Efficient end-to-end multispectral image compression

Abstract

Multispectral imagery is instrumental across diverse domains, including remote sensing, environmental monitoring, agriculture, and healthcare, as it offers a treasure trove of data over various spectral bands, enabling profound insights into our environment. However, with the ever-expanding volume of multispectral data, the need for efficient compression methods is becoming increasingly critical. Enhanced compression not only conserves precious storage space, but also facilitates rapid data transmission and analysis, ensuring the accessibility of vital information. In particular, in applications such as satellite imaging, where bandwidth constraints and storage limitations are prevalent, superior compression techniques are essential to minimize costs and maximize resource utilization.

Neural network-based compression methods are emerging as a solution to address this escalating challenge. While autoencoders have become a common neural network approach to image compression, they face limitations in generating customized quantization maps for training images, relying on feature extraction. However, the integration of bespoke quantization maps alongside feature extraction can elevate compression performance to levels previously considered unattainable. The concept of end-to-end image compression, encompassing both quantization maps and feature extraction, offers a comprehensive approach to represent an image in its simplest form.

The proposed method considers not only the compression ratio and image quality but also the substantial computational costs associated with current approaches. Designed to capitalize on similarities within and across spectral channels, it ensures accurate reproduction of the original source information, promising a more efficient and effective solution for multispectral image compression.