Compressed domain texture classification from a modified EZW symbol stream

Summary form only given. Researchers have demonstrated the effectiveness of wavelet subband energy values as a feature for representing and classifying texture images. However, the extraction of these texture features from compressed data can be cumbersome using traditional decompress-process approaches. A method has been developed for calculating wavelet energy features directly from a compressed embedded zerotree wavelet (EZW) symbol stream. The resulting technique is efficient and requires less memory than traditional approaches. In order to simplify the detection of subbands within the compressed data stream, end-of-subband markers have been inserted during the dominant pass of the EZW coding process. After compressing the test image, the reconstruction values described in Shapiro (1993) are used to calculate the energy of each subband. Following this technique, the memory requirements are reduced since the image is no longer reconstructed prior to the energy calculations. Additionally, the potentially large reconstruction matrix is no longer traversed which also reduces the time complexity