Using Set Partitioning in Hierarchical Trees in the EZW Algorithm Yeon Hyang Kim William L’Huillier

Introduction to wavelet transform May contain multiple levels Pixel magnitude levels decrease in 3 quadrants Total size remains constant

Original idea of encoding Use sorting passes to characterize significance of information in bit depth Use sorting passes to characterize significance of information in bit depth Output the pixel coordinates that corresponds to given significance levels Output the pixel coordinates that corresponds to given significance levels Repeat in decrementing significance levels Repeat in decrementing significance levels Much resources spend on sorting pass Much resources spend on sorting pass

Set Partitioning in Hierarchical Trees Elements are tagged significant or insignificant with respect to a given threshold Elements are tagged significant or insignificant with respect to a given threshold Parse through the image in a tree format Parse through the image in a tree format Assumed information: root of tree has greatest information Assumed information: root of tree has greatest information

Original image Reconstruction from compression level 4 Rates:.2288 and.2177 Reconstruction from compression level 6 Rates:.1283 and.1332

implications This algorithm is able to send the most significant information first before ending the transmition This algorithm is able to send the most significant information first before ending the transmition JPEG2000 JPEG2000 Questions? Questions?