Evaluating the visual quality of watermarked images

Aleksandr Shnayderman; Ahmet M. Eskicioglu

doi:10.1117/12.641972

17 February 2006 Evaluating the visual quality of watermarked images

Aleksandr Shnayderman, Ahmet M. Eskicioglu

Proceedings Volume 6072, Security, Steganography, and Watermarking of Multimedia Contents VIII; 607222 (2006) https://doi.org/10.1117/12.641972
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

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 A = USV^T, where U and V are orthogonal matrices, U^TU = I, V^TV = I and S = diag (s₁, s₂, ...). The diagonal entries of S are called the singular values of A, the columns of U are called the left singular vectors of A, and the columns of V are called the right singular vectors of A. 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 n x n is the block size. If the image size is k x k, we have (k/n) x (k/n) 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.

Citation Download Citation

Aleksandr Shnayderman and Ahmet M. Eskicioglu "Evaluating the visual quality of watermarked images", Proc. SPIE 6072, Security, Steganography, and Watermarking of Multimedia Contents VIII, 607222 (17 February 2006); https://doi.org/10.1117/12.641972

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