On Frequency Sensitive Competitive Learning for VQ Codebook Design

Vector quantization (VQ) plays an important role in many image coding systems. The present paper examines the application of frequency sensitive competitive learning for VQ codebook design. Simulation results are presented for: the influence of the initialization on codebook design; the normalized entropy of the codevectors; the number of multiplications spent in the codebook training; the "evolution" of the codebook by the end of each iteration of the algorithm; the quality of the designed codebooks in terms of the peak signal to noise ratio of the reconstructed images. A comparison with both LBG (Linde-Buzo-Gray) and non frequency sensitive competitive learning is presented.