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In image communication over lossy packet networks (e.g., cell phone communication), packet loss errors lead to damaged images. Damaged images can be repaired with passive error concealment methods, which use neighboring coefficient or pixel values to estimate the missing ones. Neighboring image data should, thus, be spread over different packets. This paper presents a novel robust packetization method for the transmission of image content in lossy packet networks. We first define novel criteria for a good packetization. Based on these properties, we propose a cost function for packetization masks. We then use stochastic optimization to calculate optimal packetization masks. We test our packetization technique on both wavelet coding and DCT coding. Compared to other packetization techniques, we are able to achieve the same or better mean quality of the reconstructed images but with less fluctuation in quality, which is important for the viewer experience. In this way, we significantly increase the worst case quality, especially for high packet loss rates. This leads to visually more pleasing images in case of a passive reconstruction.

In video communication over lossy packet networks (e.g., the Internet), packet loss errors can severely damage the transmitted video. The damaged video can largely be repaired with passive error concealment, where neighboring information is used to estimate missing information. We address the problem of passive error concealment for wavelet coded data with dispersive packetization. The reported techniques of this kind have many problems and usually fail in the reconstruction of high-frequency content. This paper presents a novel locally adaptive error concealment method for subband coded I-frames based on an inhomogeneous Gaussian Markov random field model. We estimate the parameters of this model from a local context of each lost coefficient, and we interpolate the lost coefficients accordingly. The results demonstrate a significant improvement over the reported related methods both in terms of objective performance measures and visually. The biggest improvement of the proposed method compared to the state-of-the-art in the field is the correct reconstruction of high-frequency information such as textures and edges.

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