A vector quantization neural network to compress still monochromatic images

A self-organizing neural network performing learning vector quantization (LVQ) is proposed to compress image data from still pictures. The advantages of the authors´ model are its low training time complexity, high utilization of neurons, robust clustering capability, and simple computation; therefore, a VLSI implementation is highly feasible. By learning with self-supervision, the authors´ LVQ neural model finds near-optimal clustering from image data and builds a compression codebook in the weight connections. The compression result is competitive comparing with JPEG and a wavelet method which has previously been developed as a fingerprint image compression standard. In addition to implementing LVQ into effective learning rules, the authors also introduce a neuron replenishment technique and a centroid adaptation at class stabilization method to enhance the codebook construction and to yield high picture fidelity. The authors also experiment on the filtering effect of a signal-to-noise ratio weight adaptation and the convolution effect of training with intersectedly subdivided images