Performances of linear tools and models for error detection and concealment in subband image transmission.

In this paper, we investigate the performances of Gaussian modeling and linear prediction tools for error detection and concealment in the transmission of still images. We consider the transmission of subband encoded images through two types of channels. We model the residual correlation between subband coefficients by considering them as jointly Gaussian variables. The first transmission medium considered is a packet-oriented channel, where some packets are lost during transmission. The problem is to estimate the values of missing coefficients. In this case, particular care must be taken while evaluating correlation matrices from incomplete data. The other system considered is based on a discrete memoryless noisy channel affecting the data being transmitted. The challenge is here first to determine the locations of the errors--which is done through hypotheses tests--and then to replace them by estimates based on their neighbors. The reconstruction via linear prediction is shown to give better results than median filtering based reconstruction. Error detection through this Gaussian model also shows promising results, in particular when channel statistics are taken into account in a joint source-channel decoding framework.