Improving Image Quality in Visual Cryptography

This book comprehensively covers the important efforts in improving the quality of images in visual cryptography (VC), with a focus on cases with gray scale images. It not only covers schemes in traditional VC and extended VC for binary secret images, but also the latest development in the analysis-by-synthesis approach.

This book distinguishes itself from the existing literature in three ways. First, it not only reviews traditional VC for binary secret images, but also covers recent efforts in improving visual quality for gray scale secret images. Second, not only traditional quality measures are reviewed, but also measures that were not used for measuring perceptual quality of decrypted secret images, such as Radially Averaged Power Spectrum Density (RAPSD) and residual variance, are employed for evaluating and guiding the design of VC algorithms. Third, unlike most VC books following a mathematical formal style, this book tries to make a balance between engineeringintuition and mathematical reasoning. All the targeted problems and corresponding solutions are fully motivated by practical applications and evaluated by experimental tests, while important security issues are presented as mathematical proof. Furthermore, important algorithms are summarized as pseudocodes, thus enabling the readers to reproduce the results in the book. Therefore, this book serves as a tutorial for readers with an engineering background as well as for experts in related areas to understand the basics and research frontiers in visual cryptography.

Focuses on the topic of perceptual quality in visual cryptography Comprehensively reviews and discusses perceptual quality improvement of decrypted gray scale secret images Employs quality measures such as RAPSD for halftone image, to guide gray scale visual cryptography design

Inhalt
Introduction.- Basic visual cryptography algorithms.- Improving the visual quality for binary secret images.- Digital Halftoning.- Improving visual quality for share images.- Improving visual quality for probabilistic and random grid schemes.- Improving visual quality for vector schemes.- Conclusion.