An Adaptive Hybrid Classified Vector Quantisation and its Application to Image Compression

A novel adaptive image compression technique using Classified Vector Quantiser and Discrete Cosine Transform is presented for the efficient representation of still images. The proposed method is called Adaptive Hybrid Classified Vector Quantisation. It involves a simple, but efficient, classifier based gradient method in the spatial domain without using any threshold to determine the class of the input image block, and uses three AC coefficients of the Discrete Cosine Transform coefficients to determine the orientation of the block without employing any threshold. K-means algorithm was used to generate the classified codebooks. The proposed technique was benchmarked with the standard vector quantiser generated using the k-means algorithm, and JPEG-2000. Simulation results indicated that the proposed approach alleviates edge degradation and can reconstruct good visual quality images with higher PeakSignal-to Noise-Ratio than the benchmarked techniques.