Multispectral Images Compression using PSO-based De-correlation Matrix and DWT Transform

Abstract

This paper proposes a new approach of multi-spectral image compression based on the combination of the particle swarm optimization (PSO) and the discrete wavelet transforms (DWT). In the first stage, the PSO is used to reduce the redundancies in the spectral domain. In fact, the PSO transforms a given multispectral image to optimize the energy in the first band. Despite to the complexity of this kind of approach, the transformed multispectral image is easily computed by multiplying a de-correlation matrix and the input multispectral image. The de-correlation matrix is estimated via a PSO evolution derived by a defined fitness function. In the second stage, the compressed data, related to the input multispectral image, is computed from the transformed multispectral image using an efficient 2D-DWT based algorithm. In addition to this compression approach, the original multispectral image can be recovered using a decompression algorithm. Experimental results show the validity of our proposed approach. These significant results are evaluated according to Peak signal-to-noise ratio (PSNR), compression ratio (CR) and bits per pixel (bpp) metrics.