Minimizing a weighted error criterion for spatial error concealment of missing image data

In this contribution we present an algorithm for spatial error concealment of lost image data caused by transmission of images in error prone environments. The surrounding correctly received image signal is approximated by a weighted linear combination of basis functions and the missing image data is obtained by a frequency selective extrapolation. During the approximation a novel weighted error criterion is minimized. We use an isotropic correlation model for the weighting function taking the correlation among pixels into account and emphasizing pixels which are closer to the missing area. 2D DFT basis functions are especially suited for the signal extrapolation in order to be able to reconstruct monotone areas, edges and noisy regions and allow an efficient realization of the algorithm in the frequency domain. Due to the weighting function we could improve the concealing performance of the algorithm considerably while halving the required FFT size.