Image Quality Features Research Articles

Steganalysis using image quality metrics

We present techniques for steganalysis of images that have been potentially subjected to steganographic algorithms, both within the passive warden and active warden frameworks. Our hypothesis is that steganographic schemes leave statistical evidence that can be exploited for detection with the aid of image quality features and multivariate regression analysis. To this effect image quality metrics have been identified based on the analysis of variance (ANOVA) technique as feature sets to distinguish between cover-images and stego-images. The classifier between cover and stego-images is built using multivariate regression on the selected quality metrics and is trained based on an estimate of the original image. Simulation results with the chosen feature set and well-known watermarking and steganographic techniques indicate that our approach is able with reasonable accuracy to distinguish between cover and stego images.

New Closed‐Circuit Television Magnifier for the Low Vision Patient

Although several types of closed-circuit video-magnifiers are available commercially in Australia, they suffer the various disadvantages of being either expensive, bulky, or sometimes poorly designed from an ergonomic point of view. In this report a new system is described. It has been designed to optimise the important features of versatility, portability and image quality, without being prohibitively expensive. In addition, it presents several new features, not seen previously on other video-magnifiers.

Magnetic contrast in the scanning electron microscope

The scanning electron microscope has been used to study the domain contrast in ferrimagnetic oxides including garnets. A systematic attempt has been made to investigate the parameters which influence magnetic contrast, particularly those concerned with the electron collecting system. In addition a phenomenological model has been developed which explains most of the observed contrast and image quality features. As a result it is possible to assess quantitatively the image quality as a function of microscope parameters and hence to choose operating conditions which maximize it.