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231225s2019 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2018.2867943
|2 doi
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|a eng
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| 100 |
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|a Young, Sean I
|e verfasserin
|4 aut
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| 245 |
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|a COGL
|b Coefficient Graph Laplacians for Optimized JPEG Image Decoding
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|c 2019
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 24.09.2018
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|a Date Revised 24.09.2018
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a We address the problem of decoding joint photographic experts group (JPEG)-encoded images with less visual artifacts. We view the decoding task as an ill-posed inverse problem and find a regularized solution using a convex, graph Laplacian-regularized model. Since the resulting problem is non-smooth and entails non-local regularization, we use fast high-dimensional Gaussian filtering techniques with the proximal gradient descent method to solve our convex problem efficiently. Our patch-based "coefficient graph" is better suited than the traditional pixel-based ones for regularizing smooth non-stationary signals such as natural images and relates directly to classic non-local means de-noising of images. We also extend our graph along the temporal dimension to handle the decoding of M-JPEG-encoded video. Despite the minimalistic nature of our convex problem, it produces decoded images with similar quality to other more complex, state-of-the-art methods while being up to five times faster. We also expound on the relationship between our method and the classic ANCE method, reinterpreting ANCE from a graph-based regularization perspective
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|a Journal Article
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1 |
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|a Naman, Aous T
|e verfasserin
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|a Taubman, David
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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|g 28(2019), 1 vom: 01. Jan., Seite 343-355
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|u http://dx.doi.org/10.1109/TIP.2018.2867943
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