Lossy Compression of Remote Sensing Images by Atomic Functions: DAC Improvements

In this chapter, the lossy compression of three-channel remote sensing (RS) digital images is considered. It is proposed to apply a discrete atomic compression (DAC) method and algorithm that are based on a special class of atomic functions. The DAC combines image compression with built-in data encryption. Since RS data volumes continue to increase rapidly, developing effective compression solutions for RS images has become essential. In this context, the use of DAC presents a promising approach. This chapter concentrates mainly on the spatial complexity of the DAC algorithm. It considers several approaches, including block-splitting and chroma-subsampling, and compares their performance using lossy image compression and algorithm analysis indicators. It is shown that spatial complexity can be significantly reduced without degradation of performance measured by such measures as maximum absolute deviation, root mean square error, peak signal-to-noise ratio, and compression ratio. Also, a set of DAC modifications, which involve operating data workflow improvements, are proposed and analyzed. Their deployment may improve the performance of autonomous systems and devices such as unmanned aerial vehicles. In addition, a comprehensive analysis of the DAC features and their use in various systems, which process RS images, are given.