Fast codebook generation for Vector Quantization using Ordered Pairwise Nearest Neighbor with Multiple Merging

In this paper, we propose two fast codebook generation techniques with iterative clustering for Vector Quantization (VQ). The techniques proposed in this paper are, Ordered Pairwise Nearest Neighbor (OPNN) and Ordered Pairwise Nearest Neighbor with Multiple Merging (OPNNMM). The conventional PNN technique has been improved using the proposed techniques to reduce the time taken in searching the nearest neighbors. Codebooks of various sizes for images of size 256 × 256 pixels are created. We also introduce codebook optimization, using clustering, for both the methods. Experimental results show that the time taken to generate the codebooks is reduced and the quality quality of the reconstructed images, in terms of PSNR, is better when compared to the results of earlier PNN based methods. OPNNMM with codebook optimization improves the quality of images and is fat better than OPNN method.