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A new, fast, and efficient image codec based on set partitioning in hierarchical trees

Published: 01 June 1996 Publication History

Abstract

Embedded zerotree wavelet (EZW) coding, introduced by Shapiro (see IEEE Trans. Signal Processing, vol.41, no.12, p.3445, 1993), is a very effective and computationally simple technique for image compression. We offer an alternative explanation of the principles of its operation, so that the reasons for its excellent performance can be better understood. These principles are partial ordering by magnitude with a set partitioning sorting algorithm, ordered bit plane transmission, and exploitation of self-similarity across different scales of an image wavelet transform. Moreover, we present a new and different implementation based on set partitioning in hierarchical trees (SPIHT), which provides even better performance than our previously reported extension of EZW that surpassed the performance of the original EZW. The image coding results, calculated from actual file sizes and images reconstructed by the decoding algorithm, are either comparable to or surpass previous results obtained through much more sophisticated and computationally complex methods. In addition, the new coding and decoding procedures are extremely fast, and they can be made even faster, with only small loss in performance, by omitting entropy coding of the bit stream by the arithmetic code

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  1. A new, fast, and efficient image codec based on set partitioning in hierarchical trees

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    cover image IEEE Transactions on Circuits and Systems for Video Technology
    IEEE Transactions on Circuits and Systems for Video Technology  Volume 6, Issue 3
    June 1996
    86 pages

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    IEEE Press

    Publication History

    Published: 01 June 1996

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    • (2024)LimitNet: Progressive, Content-Aware Image Offloading for Extremely Weak Devices & NetworksProceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services10.1145/3643832.3661856(519-533)Online publication date: 3-Jun-2024
    • (2024)A General Framework for Progressive Data Compression and RetrievalIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332718630:1(1358-1368)Online publication date: 1-Jan-2024
    • (2024)MECOM: A Meta-Completion Network for Fine-Grained Recognition With Incomplete Multi-ModalitiesIEEE Transactions on Image Processing10.1109/TIP.2024.340305133(3456-3469)Online publication date: 24-May-2024
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