Low bit rate image compression with orthogonal projection pursuit neural networks
S.R. Safavian, Hamid Reza Rabiee, M. Fardanesh, R.L. Kashyap · Proceedings of International Conference on Neural Networks (ICNN'97) · 2002
A new multiresolution algorithm for image compression based on projection pursuit neural networks is presented. High quality low bit-rate image compression is achieved first by segmenting an image into regions of different sizes based on perceptual variation in each region and then constructing a distinct code for each block by using the orthogonal projection pursuit neural networks. This algorithm allows one to adaptively construct a better approximation for each block by optimally selecting the basis functions from a universal set. The convergence is guaranteed by orthogonalizing the selected bases at each iteration. The coefficients of the approximations are obtained by back-projection with convex combinations. Our experimental results shows that at rates below 0.5 bits/pixel, this algorithm shows excellent performance both in terms of peak S/N ratio and subjective image quality.