Image Compression Based on Hierarchical Clustering Vector Quantization

Vector quantization (VQ) is an efficient tool for lossy compression due to its simple decoding algorithm and high compression rate. The key technique of VQ is the codebook design. In this paper, based on fuzzy c-means clustering algorithm, we firstly generate the initial classified codebooks according to the image features of different blocks. And then the proper codebooks are selected by adjusting the PSNR thresholds which are based on the quality of the reconstructed image. Since the proposed hierarchical clustering VQ framework is more adaptable to the specific regions of an image, we can reconstruct the different regions of the image hierarchically. Experimental results show that the proposed coding framework can achieve satisfactory quality measured by PSNR while reducing the codebook size significantly.