Compressed-domain classification of texture images

Traditional decompress-process methods for texture feature extraction consume valuable time and memory resources. This paper proposes a method for calculating wavelet energy texture features directly from a wavelet-compressed symbol stream. The proposed method requires little decompression and results in a technique that is efficient and requires less memory than traditional approaches. This reduction is accomplished through the elimination of both multiplication operations and the storage of zero-valued coefficients, which have no effect on these features. The developed algorithm has been implemented at various compression ratios, and in each case, the classification results are nearly identical to those obtained with the traditional method.