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- W2054345114 abstract "A recent image quality measure, M-SVD, can express the quality of distorted images either numerically or graphically. Based on the Singular Value Decomposition (SVD), it consistently measures the distortion across different distortion types and within a given distortion type at different distortion levels. The SVD decomposes every real matrix into a product of three matrices <i>A = USV<sup>T</sup></i>, where <i>U</i> and <i>V</i> are orthogonal matrices, <i>U<sup>T</sup>U = I</i>, <i>V<sup>T</sup>V = I</i> and <i>S =</i> diag (s<sub>1</sub>, s<sub>2</sub>, ...). The diagonal entries of <i>S</i> are called the singular values of <i>A</i>, the columns of <i>U</i> are called the left singular vectors of <i>A</i>, and the columns of <i>V</i> are called the right singular vectors of <i>A</i>. M-SVD, as a graphical measure, computes the distance between the singular values of the original image block and the singular values of the distorted image block, where <i>n</i> x <i>n</i> is the block size. If the image size is <i>k</i> x <i>k</i>, we have (<i>k/n</i>) x (<i>k/n</i>) blocks. The set of distances, when displayed in a graph, represents a distortion map. The numerical measure is derived from the graphical measure. It computes the global error expressed as a single numerical value. In this paper, we will extend the SVD-based image quality measure to evaluate the visual quality of watermarked images using several watermarking schemes." @default.
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- W2054345114 date "2006-02-02" @default.
- W2054345114 modified "2023-09-23" @default.
- W2054345114 title "Evaluating the visual quality of watermarked images" @default.
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- W2054345114 doi "https://doi.org/10.1117/12.641972" @default.
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