Interframe DPCM with robust median-based predictors for transmission of image sequences over noisy channels

Abstract

A new image sequence coding technique based on robust median-based predictors is presented for the transmission of image sequences over noisy channels. We analyze the robustness of median-based predictors against channel errors. A heuristic algorithm for the design of a robust predictor from a given median-based predictor is presented. It is shown that with small modifications in terms of a necessary requirement for a median-based predictor to be robust against channel errors, the robustness of a given median-based predictor can be considerably improved. Simulations on a real image sequence show significant improvement over the conventional differential pulse code modulation (DPCM) at high bit error rate (BER) using this new technique. The technique does not increase the transmission rate. It is shown that the quality of reconstructed images obtained by robust median-based predictors can be further improved by postprocessing the image using a nonlinear detail-preserving noise-smoothing filter.

Polynomial Weighted Median Image Sequence Prediction

“…Nonlinear predictors can also be used to address robustness issues, for instance, in the case of median-based predictors [6], [7]. While such predictors are more robust than Volterra predictors, they do not exploit the higher order statistics of observation samples.…”

“…Other nonlinear predictors such as Volterra predictors [4], [5] and median predictors [6], [7] are also proposed to overcome the limitations of linear predictors. Motion compensated prediction (MCP) has been widely used since it exploits temporal redundancy by estimating the motion vector field [8], [9].…”

Robustness of the Prediction Filter in Differential Pulse Code Modulation System

“…Some robustness analyses of predictors in regard to transmission error have already done in [16]. In this paper we will perform parametric robustness in regard to predictor coefficients.…”