Novel Convergence Model for Efficient Error Concealment using Information Hiding in Multimedia Streams

Abstract

Error concealment using information hiding has been an efficient tool to combat channel impairments that degrade the transmitted data quality by introducing channel errors/packet losses. The proposed model takes a stream of multimedia content and the binarised stream is subjected to bit level enhanced mapping procedure (PRASAN - Enhanced NFD approach) accompanied with a set of convergence models that ensure a high degree of convergence for a given error norm. The mapping is performed between the current frames with respect to the previous frame in case of video data. This approach often referred to as the correlation generation is followed by convergence mathematical function generation. This function is derived based on trying out the various convergence methodologies in a weighted round robin environment and choosing the best matching function by computing the mean square error. This error is termed map-fault and is kept a minimum. The test data taken are subjected to noisy channel environments and the power signal to noise ratios obtained experimentally support firmly the advantage of the proposed methodology in comparison to existing approaches