Mammalian visual characteristics inspired perceptual image quantization using pulse-coupled neural networks

As a matter of fact, mammalian visual system do not pay an equivalent attention to different regions in an image, the visual cortex is less sensitive to textures than non-textures. Therefore, to obtain the optimal visual quality and the perfect compression ratio simultaneously in image quantization, textures should be quantized coarsely, and non-textures should be quantized finely. The pulse-coupled neural networks (PCNN) is a model of synchronous pulse bursts in mammalian visual cortex, which has been proved to be extremely effective in image processing because of its biological background. In this work, a mammalian visual characteristics inspired perceptual image quantization strategy is proposed. It employs PCNN to extract textures from original image. Then, pixels in textures are quantized into less gray scale layers than pixels in non-textures. After that, quantized textures and quantized non-textures are consolidated. Experimental results prove validity and efficiency of the proposed method.