Vector quantization and signal compression
Vector quantization and signal compression
Parallel competitive learning algorithm for fast codebook design on partitioned space
CLUSTER '04 Proceedings of the 2004 IEEE International Conference on Cluster Computing
A fast finite-state algorithm for vector quantizer design
IEEE Transactions on Signal Processing
Finite-state vector quantization for waveform coding
IEEE Transactions on Information Theory
A fast LBG codebook training algorithm for vector quantization
IEEE Transactions on Consumer Electronics
An efficient encoding algorithm for vector quantization based on subvector technique
IEEE Transactions on Image Processing
Multipurpose image watermarking algorithm based on multistage vector quantization
IEEE Transactions on Image Processing
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The codebook design in the vector quantization scheme is important because it affects the image quality of the encoded image. The Linde-Buzo-Gray (LBG) codebook generation algorithm is well known and a popular choice among codebook users. However, a heavy computational complexity is consumed for the iteratively clustering process in the LBG algorithm. In this paper, the similarity of codewords in consecutive rounds of the LBG algorithm is exploited to reduce the computational complexity. By checking the stability of codewords, the status of each codeword in the codebook can be determined. Only the unstable codewords are refined to generate the new codebook. The proposed method can be further improved by cooperating with the finite state technique. Experimental results show that the computational complexity of the proposed method is reduced to about 4% of the LBG algorithm while achieving a slightly worse image quality.