A G2LA vector quantization for image data coding

  • Authors:

  • Jerome Yeh;Yen-Tseng Hsu

  • Affiliations:

  • Department of Computer Information and Science Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC;Department of Computer Information and Science Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

Quantified Score

Hi-index 12.05

Visualization

Abstract

In this paper, based on the Grey theory, a novel measurement method in a large volume and high dimension of information system is proposed for vector quantization (VQ) design and applied to image data coding. In the VQ coding procedure, it is often needs several epochs of clustering and always fails to obtain a better codebook; for instance, the well-known generalized Lloyd algorithm (GLA) easily traps into suboptimal codebook and does not have the ability to locate an optimal codebook during any clustering iteration with a random initial codebook. Hence, we propose a G^2LA design to solve heavy times of clustering procedure and at least to gain the best suboptimal codebook. In order to avoid edge degradation, firstly, the new selection of initial codevectors is adopted as the fast grey vector quantization (FGVQ) procedure which chooses nonhomogeneous vectors from a large volume image data. Then extending the GLA to G^2LA method by utilizing the measurement of grey relational analysis (GRA) which depends on the effect of relative objective and initial codevectors of FGVQ to obtain a better representative codebook. Experiment results show that at the same bit rate the G^2LA has not only the quickly convergence time but also high quality reconstructed image than traditional GLA technique with Euclidean distance measure, especially in high dimension and a large volume data system.