4 research outputs found

    Error Concealment using Neural Networks for Block-Based Image Coding

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    In this paper, a novel adaptive error concealment (EC) algorithm, which lowers the requirements for channel coding, is proposed. It conceals errors in block-based image coding systems by using neural network. In this proposed algorithm, only the intra-frame information is used for reconstruction of the image with separated damaged blocks. The information of pixels surrounding a damaged block is used to recover the errors using the neural network models. Computer simulation results show that the visual quality and the MSE evaluation of a reconstructed image are significantly improved using the proposed EC algorithm. We propose also a simple non-neural approach for comparison

    <title>Finite-state residual vector quantization</title>

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    <title>Finite state residual vector quantization with neural network state prediction</title>

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