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Memory Efficient Set Partitioning in Hierarchical Tree (MESH) for Wavelet Image Compression

Authors: Farid Ghani; Md. Abdul Kader; Ekram Khan; R. Badlishah Ahmad;

Memory Efficient Set Partitioning in Hierarchical Tree (MESH) for Wavelet Image Compression

Abstract

This paper presents a memory efficient version of set partitioning in hierarchical tree (SPIHT). The proposed coder called Memory Efficient SPIHT (MESH) uses a single re-usable list instead of three continuously growing linked lists as in conventional SPIHT. The list is re-initialized at the beginning of each bit-plane (coding pass) and is exhausted within the same bit-plane. Another feature of the proposed coder is that it uses a single pass for each bit-plane by merging the sorting and refinement passes of the conventional version of SPIHT. The performance of the proposed coder is measured in terms of coding efficiency, and the worst case dynamic memory requirements due to the list entries in each bit-plane. Performance comparison with SPIHT shows that the proposed coder reduces the dynamic memory requirement by about 50―70% compared to the SPIHT while retaining its coding efficiency.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
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