PCA-Based Fast Search Method Using PCA-LBG-Based VQ Codebook for Codebook Search

• Main Authors: Po-Yuan Yang, Jinn-Tsong Tsai, Jyh-Horng Chou
• Format: Article
• Language: English
• Published: IEEE 2016-01-01
• Series: IEEE Access
• Subjects: Vector quantization; codebook; principle component analysis; fast search
• Online Access: https://ieeexplore.ieee.org/document/7444129/

A fast search method based on principle component analysis (PCA) is proposed to search codewords using vector quantization (VQ) codebooks obtained by PCA with Linde-Buzo-Gray (LBG) algorithms. The PCA sorts vectors of a test image and codewords of a PCA-LBG-based VQ codebook. The first search starts from the first codeword in the sorted codebook, and the next search starts from the previous best-matching codeword position in the sorted codebook. Both forward and backward searches are performed within the set search range until the best-matching codewords for all vectors of the test image are found in a sorted codebook. Because PCA efficiently distinguishes both test image vectors and codebook codewords, the proposed PCA-based fast search method outperforms the conventional algorithms in a codebook search. In particular, the experimental results show that, by using PCA-LBG-based VQ codebooks, the proposed PCA-based fast search method outperforms other methods in terms of peak signal-to-noise ratio for the compressed image, number of codewords searched, and runtime.