An Algorithmic Approach for Efficient Image Compression using Neuro-Wavelet Model and Fuzzy Vector Quantization Technique

Applications, which need to store large database and/or transmit digital images requiring high bit-rates over channels with limited bandwidth, have demanded improved image compression techniques. This paper describes practical and effective image compression system based on neuro-fuzzy model which combines the advantages of fuzzy vector quantization with neural network and wavelet transform. The emphasis here is on the usefulness of fuzzy vector quantization when it is combined with conventional image coding techniques. The implementation consists of three steps. First, the image is decomposed at different scales using wavelet transform to obtain an orthogonal wavelet representation of the image Each band can be subsequently processed in parallel. Thus, the processing speed can be much faster than otherwise. Different quantization and coding schemes are used for different sub bands based on their statistical properties. At the second step, wavelet coefficients corresponding to lowest frequency band are compressed using differential pulse code modulation. Neural network is used to extract the principal components of the higher frequency band wavelet coefficients. Finally, results of the second step are used as input to the fuzzy vector quantization algorithm. Our simulation results show encouraging results and superior reconstructed images are achieved. The effect of noise on the compression performance is also studied.