Image compression: Wavelet transform using radial basis function (RBF) neural network

Abstract

Image compression is the technique of reducing the size of the image file without degrading the quality of the image. The compression in file size permits more images to be stored in the available amount of memory space. It also decreases the time needed for images to be uploaded over the Internet or downloaded from it. There are many techniques available in the lossy image compression, in which wavelet transform based image compression is the best technique. Vector Quantization (VQ) is the most powerful tool for image compression. One of the major steps in the Vector Quantization is the generation of the code book. In this proposed approach, a popular neural network technique called Radial Basis Function (RBF) approach is used to generate the code book. A combined approach of image compression based on vector quantization and wavelet transform is proposed using RBF neural network. This approach will be very helpful for medical imaging, criminal investigation where high precision reconstructed image is required. The experimental result shows that the proposed technique provides better PSNR value and also reduces the Mean Square Error value.