We present a novel nonlinear predictive image coding scheme in which a relative prediction error is first generated from the current pixel value and its predicted value. It is next mapped, quantized, coded and transmitted. Consequently, a weighting function is introduced into the coding algorithm such that the coding error is adapted by the pixel intensity and its relative prediction error. Meanwhile, the resulting quantization step size is smaller in lower
contrast areas and larger in higher contrast areas so that the granular noise and the slope overload distortion can be efficiently reduced. Our simulation results show that on an average, with the proposed scheme, the bit rate is about 0.23 bits less than that obtained with differential pulse-code modulation (DPCM), while the peak SNR (PSNR) is about 2.9 dB higher than that with DPCM. On the other hand, more coding errors are allocated in less visible areas where the image intensity and/or contrast are higher.