Nonlinear prediction in image coding with DPCM

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Abstract
In contrast to the traditional linear differential pulse code modulation (DPCM) design for the encoding of images, a new, nonlinear, neural network-based, DPCM technique has been devised. The predictor is designed by supervised training, based on a typical sequence of pixel values in an image. A function link neural network architecture has been used to design the predictor for one dimensional (1-D) DPCM. Computer simulation experiments in still image coding have shown that the resulting encoders work very well. At a transmission rate of 1 bit/pixel, for the image LENA, the 1-D neural network DPCM provides a 4.2 dB improvement in SNR over the standard linear DPCM system.

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