An adaptive Markov random field based error concealment method for video communication in an error prone environment

TitleAn adaptive Markov random field based error concealment method for video communication in an error prone environment
Publication TypeConference Paper
Year of Publication1999
AuthorsShirani, S., F. Kossentini, and R. Ward
Conference NameAcoustics, Speech, and Signal Processing, 1999. ICASSP '99. Proceedings., 1999 IEEE International Conference on
Pagination3117 -3120 vol.6
Date Publishedmar.
Keywordsadaptive Markov random field, computational complexity, damaged area reconstruction, data loss, decoded video sequence, error concealment method, error prone environment, image reconstruction, image sequences, Markov processes, missing edges reconstruction, MRF models, neighborhood pixels, video coding, video communication, visual communication

Loss of coded data during its transmission can affect a decoded video sequence to a large extent, making concealment of errors caused by data loss a serious issue. Previous work in spatial error concealment exploiting MRF models used a single pixel wide region around the erroneous area to achieve a reconstruction based on an optimality measure. This practically restricts the amount of available information that is used in a concealment procedure to a small region around the missing area. Incorporating more pixels usually means a higher order model and this is expensive as the complexity grows exponentially with the order of the MRF model. Using previously proposed approaches, the damaged area is reconstructed fairly well in very low frequency portions of the image. However, the reconstruction process yields blurry results with a significant loss of details in high frequency, or edge portions of the image. In our proposed approach, a MRF is used as the image a priori model. More available information is incorporated in the reconstruction procedure not by increasing the order of the model but instead by adaptively adjusting the model parameters. Adaptation is done based on the image characteristics determined in a large region around the damaged area. Thus, the reconstruction procedure can make use of information embedded in not only immediate neighborhood pixels but also in a wider neighborhood without a dramatic increase in computational complexity. The proposed method outperforms the previous methods in the reconstruction of missing edges


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