An EZW compressed image technique for calculating texture signatures

Abstract

Traditional techniques for texture feature extraction needlessly consume valuable system resources by decompressing and storing the image data prior to processing. This paper presents a new technique for calculating mean deviation texture signatures directly from wavelet-compressed images. The technique reduces the overhead while maintaining accuracy. It offers a reduction in memory and computational costs by reducing and simplifying the calculations performed and by eliminating the storage of the reconstructed image.