19 January 2009 Local structure learning and prediction for efficient image compression
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One major challenge in image compression is to efficiently represent and encode high-frequency structural components in images, such as edges, contours, and texture regions. To address this issue, we propose a scheme to learn local image structures and efficiently predict image data based on this structure information. We prove that, using singular value decomposition, we can find a small number of basis vectors whose linear combinations are able to closely approximate local image patches. By extrapolating these linear combination coefficients, we can efficiently predict neighboring pixels of the local image patch. We find that this structure learning and prediction procedure is very efficient for image regions with significant structural components. However, because of its large overhead and high computational complexity, its performance degrades in other image regions, such as smooth areas. Therefore, we propose a classification scheme to partition an image into three types of regions: structure regions, non-structure regions, and transition regions. Structure regions are encoded with structure prediction. With image in-painting, the non-structure and transmission regions are extended into a maximally smoothed image which can be efficiently encoded with conventional image compression methods, such as JPEG2000. Our experimental results demonstrate that the proposed method outperform the state-of-the-art JPEG2000 image compression.
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Xiwen Zhao, Xiwen Zhao, Zhihai He, Zhihai He, } "Local structure learning and prediction for efficient image compression", Proc. SPIE 7257, Visual Communications and Image Processing 2009, 72570Y (19 January 2009); doi: 10.1117/12.805953; https://doi.org/10.1117/12.805953

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