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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.
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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 • Output the pixel coordinates that corresponds to given significance levels • Repeat in decrementing significance levels • Much resources spend on sorting pass
Set Partitioning in Hierarchical Trees • Elements are tagged significant or insignificant with respect to a given threshold • Parse through the image in a tree format • 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 • JPEG2000 • Questions?