Fractal Lossy Hyperspectral Image Coding Algorithm Based on Prediction

Abstract

At present, a hyperspectral image has a significant advantage in the aspect of application because of its high spectral resolution. However, owing to the limit of communication capacity, a hyperspectral image must be compressed. In this paper, we develop and propose a fractal lossy hyperspectral image compression method based on prediction. First, we exploit spatial correlation by applying predictive lossy compression to obtain a reference band of high quality. Then, the local similarity between the two adjacent bands is used through fractal encoding using a local search algorithm. Next, we encode the fractal parameters and the error and fractal residual is transformed by discrete cosine transform, quantized, and entropy encoded to improve the decoded quality. Through experiments, we demonstrate that the proposed algorithm leads to considerably improved performance in compression compared with the other well-known methods. Finally, we validate whether the compression affects the data in the hyperspectral images through classification. The results indicate that the accuracy of classification obtained for the reconstructed image is marginally less than the accuracy reported for the original data set; however, the loss in accuracy is less than 1% and thus acceptable.