A comparative study of LBG and SOA codebooks concerning the computational complexity of the minimum distortion encoding for VQ

Summary form only given. Vector quantization (VQ),is a well-known compression technique which has been widely used in many speech and image coding systems. Techniques for codebook design attempt to produce a codebook that is optimum for a given source. To date, the most widely used technique for VQ codebook design is the LBG (Linde-Buzo-Gray) algorithm. Madeim et al. (1999) show that an unsupervised neural network algorithm, referred to as SOA (self-organizing algorithm), provides good VQ codebooks, leading to reconstructed signals with better quality when compared to the ones obtained by using LBG codebooks. In this paper, an investigation is carried out to evaluate the "inherent" quality of SOA and LBG codebooks regarding the computational complexity of medium distortion encoding. The present work shows that the SOA codebooks overperforms the LBG codebooks in the sense that they yield a smaller average number of multiplications per sample for image VQ.