Compression of Multispectral Images

This paper is an overview of research contributions by the authors to the use of compression techniques to handle high resolution, multi-spectral images. Originally developed in the remote sensing context, the same techniques are here applied to food and medical images. The objective is to point out the potential of this kind of processing in different contexts such as remote sensing, food monitoring, and medical imaging and to stimulate new research exploitations. Compression is based on the simple assumption that it is possible to find out a relationship between pixels close one each other in multi-spectral images it translates to the possibility to say that there is a certain degree of correlation within pixels belonging to the same band in a close neighbourhood. Once found a correlation based on certain coefficient on one band, the coefficients of this relationship are, in turn, quite probably, similar to the ones calculated in one of the other bands. Based upon this second observation, an algorithm was developed, able to reduce the number of bit/pixel from 16 to 4 in satellite remote sensed multi-spectral images. A comparison is carried out between different methods about their speed and compression ratio. As reference it was taken the behaviour of three common algorithms, LZW (Lempel-Ziv-Welch), Huffman and RLE (Run Length Encoding), as they are used in common graphic format such as GIF, JPEG and PCX. The Presented methods have similar results in both speed and compression ratio to the commonly used programs and are to be preferred when the decompression must be carried out on line, inside a main program or when there is the need of a custom made compression algorithm.