The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images in a perceptually consistent manner. Image QA algorithms generally interpret image quality as fidelity or similarity with a “reference” or “perfect” image in some perceptual space. In order to improve the assessment accuracy of white noise, Gauss blur, JPEG2000 compression an...

Natural images are meaningful to humans and used for a variety of tasks. The specific task to be performed dictates the assessment of an image’s suitability to the task. This work considers image assessment (IA) as a task-specific evaluation and examines the performance of a variety of objective IA algorithms for three different assessment tasks: fidelity assessment, quality assessment, and uti...

The emerging generation of radio interferometric (RI) telescopes, such as the Square Kilometre Array (SKA), will acquire massive volumes of data and transition radio astronomy to a big-data era. The ill-posed inverse problem of imaging the raw visibilities acquired by RI telescopes will become significantly more computationally challenging, particularly in terms of data storage and computationa...

In this Keynote Address paper, we review early work on Image and Video Quality Assessment against the backdrop of an interpretation of image perception as a visual communication problem. As a way of explaining our recent work on Video Quality Assessment, we first describe our recent successful advances on QA algorithms for still images, specifically, the Structural SIMilarity (SSIM) Index and t...

We introduce two filtering methods for near-field diffuse light diffraction tomography based on the angular spectrum representation. We then combine these filtering techniques with a new method to find the approximate depth of the image heterogeneities. Taken together these ideas improve the fidelity of our projection image reconstructions, provide an interesting three dimensional rendering of ...

We have developed a method for the linear reconstruction of an image from undersampled, dithered data. The algorithm, known as Variable-Pixel Linear Reconstruction (or informally as “drizzling”), preserves photometry and resolution, can weight input images according to the statistical significance of each pixel, and removes the effects of geometric distortion both on image shape and photometry....

This paper presents a new method for characterizing information of a compressed image relative to the original one. We show how the Kullback-Leibler information gain is based on three basic postulates which are natural for image processing and thus desirable. As an example of the proposed measure, we analyze the effects of lossy compression on the identification of breast cancer microcalcificat...

We investigate the nanosecond pulse noise-hidden image reconstruction and visualization using stochastic resonance implemented by modulation instability. In particular, this dynamical stochastic resonance holds with coupling between the pulse incoherent noise and pulse coherent signal, and provides a substantial enhancement of the signal-to-noise ratio and cross-correlation. This means that the...

The problem inherent with any digital image (or digital video) system is the large amount of bandwidth required for transmission or storage. This has driven the research area of image compression to develop more complex algorithms that compress images to lower data rates with better fidelity. One approach that can be used to increase the execution speed of these complex algorithms is through th...

The paper deals with strategies for optimizing the transmission of JPEG2000 coded images over a MIMO quasi-static Rayleigh fading channel, by exploiting different available MIMO STBC (Space Time Block Codes), as well as the image content to be transmitted. The aim is to propose a link adaptation scheme based on the variable radio channel’s conditions, by establishing an UEP (Unequal Error Prote...