A Neuro-Wavelet Model Using Fuzzy Vector Quantization for Efficient Image Compression

Images have large data quantity. For storage and transmission of images, high efficiency image compression methods are under wide attention. In this paper, we propose a neuro- wavelet based model for image compression, which combines the advantages of wavelet transform and neural network and uses fuzzy vector quantization on hidden layer coefficients. Images are decomposed using wavelet filters into a set of sub bands with different resolution corresponding to different frequency bands. Different quantization and coding schemes are used for different sub bands based on their statistical properties. The coefficients in the lowest frequency band are compressed by differential pulse code modulation (DPCM) and the coefficients in higher frequency bands are compressed using neural network. The coefficients of the hidden layer of the neural network are further fuzzy vector quantized, which increases the compression ratio. The visual quality of the image has been increased by introducing fuzziness to vector quantization algorithm. Satisfactory reconstructed images with large compression ratios have been achieved using this scheme.