Low Bit Rate Image Compression with Orthogonal Projection Pursuit Neural Networks

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 PSNR and subjective image quality.