Year: 2002
Type: article
Abstract: Vector quantization is the process of encoding vector data as an index to a dictionary or codebook of representative vectors. One of the most serious problems for vector quantization is the high computational complexity involved in searching for the closest codeword through the codebook. In this paper, we describe a new method allowing significant acceleration of codebook design and encoding processes for vector quantization. This method has feature of using a suitable hyperplane to partition codebook and image data. Experimental results are presented on image block data. These results show that our method performs better than the previously known methods.
DOI: 10.3169/itej.56.1513
Authors: Ahmed Swilem, Kousuke Imamura, Hideo Hashimoto
Institution: Kanazawa University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2329426544