Adaptive MAP error concealment for dispersively packetized images

In this paper we present an adaptive maximum a posteriori (MAP) error concealment algorithm for dispersively packetized wavelet-coded images. We model the subbands of a wavelet-coded image as Markov random fields, and use the characteristics of subband/wavelet samples to adapt the potential functions locally. The resulting MAP estimation gives PSNR advantages of up to 0.7 dB compared to the competing algorithms. Visual quality of the reconstructed images is also improved.