A new method for image quantization based on adaptive region related heterogeneous PCNN

Abstract

Based on the different strength of synaptic connections between actual neurons, this paper proposes a novel heterogeneous PCNN (HPCNN) algorithm to quantize images. HPCNN is constructed with traditional pulse coupled neural network (PCNN) models, which has different parameters corresponding to different image regions. It puts pixels of different gray levels to be classified broadly into two categories: the background regional ones and the object regional ones. Moreover, HPCNN also satisfies human visual characteristics (HVS). The parameters of HPCNN model are calculated automatically according to these categories and quantized results will be optimal and more suitable for human to observe. At the same time, the experimental results show the validity and efficiency of our proposed quantization method.