Fast codebook searching in a SOM-based vector quantizer for image compression View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2008-01

AUTHORS

Arijit Laha, Bhabatosh Chanda, Nikhil R. Pal

ABSTRACT

We propose a novel method for fast codebook searching in self-organizing map (SOM)-generated codebooks. This method performs a non-exhaustive search of the codebook to find a good match for an input vector. While performing an exhaustive search in a large codebook with high dimensional vectors, the encoder faces a significant computational barrier. Due to its topology preservation property, SOM holds a good promise of being utilized for fast codebook searching. This aspect of SOM remained largely unexploited till date. In this paper we first develop two separate strategies for fast codebook searching by exploiting the properties of SOM and then combine these strategies to develop the proposed method for improved overall performance. Though the method is general enough to be applied for any kind of signal domain, in the present paper we demonstrate its efficacy with spatial vector quantization of gray-scale images. More... »

PAGES

39-49

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11760-007-0034-3

DOI

http://dx.doi.org/10.1007/s11760-007-0034-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1020517493


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