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A new, non-iterative post-processing approach is proposed for real-time reduction of blocking effects. The proposed approach has the merits of being fully compatible with the JPEG standard and requiring no additional transmission overhead. This is achieved by training feed-forward single-layer neural networks to restore classified block boundaries of JPEG-encoded images. Classification is based on...
In this paper we derive a log-likelihood function-based classification algorithm for classifying quadrature amplitude modulation (QAM) signals buried in additive white Gaussian noise. We derive the amplitude density functions of received QAM signals first, then develop the required statistics for signal classification based on the maximum a posteriori probability criterion and demonstrate a schematic...
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