NDLI: A two-phase hybrid codebook generation technique for vector quantization

Content Provider	IEEE Xplore Digital Library
Author	Chia-Chen Yen Chih-Ya Shen Ming-Syan Chen
Copyright Year	2010
Description	Author affiliation: Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan (Chia-Chen Yen; Chih-Ya Shen; Ming-Syan Chen)
Abstract	In general, the codebook generation for vector quantization emphasizes two major topics: minimizing the distortion error to improve the quality of the reconstructed image and reducing the time cost to enhance the efficiency. LBG is one of the famous codebook generation techniques proposed in recent decades. LBG was widely utilized due to its simplicity. However, it only guarantees a local optimum. To be an alternative to LBG, the pair-wise nearest neighbor (PNN) algorithm was devised to obtain better results. By the fully searching operation, PNN needs a large amount of calculations. In this paper, a Two-Phase Codebook Generation (TPCG) technique based on the relative pixel magnitudes is presented. With the pre-process of image decomposition, TPCG applies a simple quantifier to divide low frequency blocks in linear time, and then employs a new proposed k-nearest neighbor graph construction approach with Double Linked Algorithm instead of PNN for high frequency blocks. The experiments reveal that TPCG has accuracy approximates to that of PNN while keeping a low time cost.
Starting Page	4309
Ending Page	4312
File Size	304566
Page Count	4
File Format	PDF
ISBN	9781424479924
ISSN	15224880
e-ISBN	9781424479948
e-ISBN	9781424479931
DOI	10.1109/ICIP.2010.5652473
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-26
Publisher Place	Hong Kong
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pixel Nearest neighbor searches Clustering algorithms Vector quantization Image coding Decoding Merging K-d tree k-nearest neighbors k-nearest neighbor graph
Content Type	Text
Resource Type	Article

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