Image compression using fast transformed vector quantization

Digital image compression is an important technique in digital image processing. To improve its performance, we attempt to speed up the design process and achieve the highest compression ratio where possible. For speed improvement, we used a fast Kohonen self-organizing neural network algorithm to achieve big saving in codebook construction time. For compression purpose, we propose a new approach, called fast transformed vector quantization (FTVQ), by combining together the features of speed improvement, transform coding and vector quantization. We use several experiments to demonstrate the feasibility of this FTVQ approach.