28 January 2008 Decision trees for denoising in H.264/AVC video sequences
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Abstract
All existing video coding standards are based on block-wise motion compensation and block-wise DCT. At high levels of quantization, block-wise motion compensation and transform produces blocking artifacts in the decoded video, a form of distortion to which the human visual system is very sensitive. The latest video coding standard, H.264/AVC, introduces a deblocking filter to reduce the blocking artifacts. However, there is still visible distortion after the filtering when compared to the original video. In this paper, we propose a non-conventional filter to further reduce the distortion and to improve the decoded picture quality. Different from conventional filters, the proposed filter is based on a machine learning algorithm (decision tree). The decision trees are used to classify the filter's inputs and select the best filter coeffcients for the inputs. Experimental results with 4 × 4 DCT indicate that using the filter holds promise in improving the quality of H.264/AVC video sequences.
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G. Huchet, J.-Y. Chouinard, D. Wang, A. Vincent, "Decision trees for denoising in H.264/AVC video sequences", Proc. SPIE 6822, Visual Communications and Image Processing 2008, 68220Y (28 January 2008); doi: 10.1117/12.766406; https://doi.org/10.1117/12.766406
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