Polynomial Weighted Median Predictors for Image Sequences

Image sequence prediction is widely used in image compression and transmission schemes such as differential pulse code modulation (DPCM). In traditional predictive coding, linear predictors are usually adopted to exploit the inherent redundancy and correlation between neighboring pixels. However, due to the nonstationary and non-Gaussian nature of image sequences, linear predictors are not often very effective. As an alternative, Volterra predictor is able to compensate for the smoothing effects introduced by linear predictor. However, it suffers from noise that may be attributed to quantization errors or image acquisition devices. In this paper, we propose a novel nonlinear polynomial weighted median (PWM) predictor for image sequence prediction. The proposed PWM predictor is more robust to noise, while still retaining the information of higher-order statistics of pixel values. Experimental results illustrate that the PWM predictor yields better results than other predictors especially in noisy case. The proposed scheme can be incorporated in new predictive coding systems.