The relative impact of ghosting and noise on the perceived quality of MR images

Magnetic resonance (MR) imaging is vulnerable to a variety of artifacts, which potentially degrade the perceived quality of MR images and, consequently, may cause inefficient and/or inaccurate diagnosis. In general, these artifacts can be classified as structured or unstructured depending on the correlation of the artifact with the original content. In addition, the artifact can be white or colored depending on the flatness of the frequency spectrum of the artifact. In current MR imaging applications, design choices allow one type of artifact to be traded off with another type of artifact. Hence, to support these design choices, the relative impact of structured versus unstructured or colored versus white artifacts on perceived image quality needs to be known. To this end, we conducted two subjective experiments. Clinical application specialists rated the quality of MR images, distorted with different types of artifacts at various levels of degradation. The results demonstrate that unstructured artifacts deteriorate quality less than structured artifacts, while colored artifacts preserve quality better than white artifacts

Inducing sadness and anxiousness through visual media: measurement techniques and persistence

The persistence of negative moods (sadness and anxiousness) induced by three visual Mood Induction

AN EFFICIENT NO-REFERENCE METRIC FOR PERCEIVED BLUR

International audienceThis paper presents an efficient no-reference metric that quantifies perceived image quality induced by blur. Instead of explicitly simulating the human visual perception of blur, it calculates the local edge blur in a cost-effective way, and applies an adaptive neural network to empirically learn the highly nonlinear relationship between the local values and the overall image quality. Evaluation of the proposed metric using the LIVE blur database shows its high prediction accuracy at a largely reduced computational cost. To further validate the performance of the blur metric on its robustness against different image content, two additional quality perception experiments were conducted: one with highly textured natural images and one with images with an intentionally blurred background . Experimental results demonstrate that the proposed blur metric is promising for real-world applications both in terms of computational efficiency and practical reliability

Road marking BRDF model applicable for a wide range of incident illumination conditions

To drive safely and comfortably, an adequate contrast between the road surface and road markings is needed. This contrast can be improved by using optimized road illumination designs and luminaires with dedicated luminous intensity distributions, taking advantage of the (retro)reflective characteristics of the road surface and road markings. Since little is known about road markings’ (retro)reflective characteristics for the incident and viewing angles relevant for street luminaires, bidirectional reflectance distribution function (BRDF)-values of some retroreflective materials are measured for a wide range of illumination and viewing angles using a luminance camera in a commercial near-field goniophotometer setup. The experimental data are fitted to a new and optimized “RetroPhong” model, which shows good agreement with the data [root mean squared error (RMSE) &lt; 0.13, normalized root mean squared error (NRMSE) &lt; 0.04, and the normalized cross correlation ratio (NCC) &gt; 0.8]. The RetroPhong model is benchmarked with other relevant (retro)reflective BRDF models, and the results suggest that the RetroPhong model is most suitable for the current set of samples and measurement conditions.</p

A Comparative Study of Fixation Density Maps

International audienceFixation density maps (FDM) created from eye tracking experiments are widely used in image processing applications. The FDM are assumed to be reliable ground truths of human visual attention and as such one expects high similarity between FDM created in different laboratories. So far, no studies have analysed the degree of similarity between FDM from independent laboratories and the related impact on the applications. In this paper, we perform a thorough comparison of FDM from three independently conducted eye tracking experiments. We focus on the effect of presentation time and image content and evaluate the impact of the FDM differences on three applications: visual saliency modelling, image quality assessment, and image retargeting. It is shown that the FDM are very similar and that their impact on the applications is low. The individual experiment comparisons, however, are found to be significantly different, showing that inter-laboratory differences strongly depend on the experimental conditions of the laboratories. The FDM are publicly available to the research community

Performance evaluation of 2D and 3D-TV systems

\u3cp\u3eThe Image Quality Circle, introduced by P. Engeldrum [1,2], is a useful framework for modeling image quality of TV-systems. Line-up experiments with high-end 2D TVs show that for naïve viewers the most important attributes in the assessment of overall image quality are brightness, contrast, color rendering and sharpness. When evaluating 3D TV-systems, however, recent research showed that the added value of displaying stereoscopic depth is hardly accounted for in the assessment of image quality. Hence, the Image Quality Circle model needs expansion to cover the full visual experience of 3D-TV. Alternative concepts, such as naturalness and viewing experience, are evaluated on their ability to include both image quality and perceived depth.\u3c/p\u3

STUDYING THE ADDED VALUE OF VISUAL ATTENTION IN OBJECTIVE IMAGE QUALITY METRICS BASED ON EYE MOVEMENT DATA

Current research on image quality assessment tends to include visual attention in objective metrics to further enhance their performance. A variety of computational models of visual attention are implemented in different metrics, but their accuracy in representing human visual attention is not fully proved yet. Thus, to provide more accurate evidence on whether and to what extent visual attention can be beneficial for objective quality prediction, the use of “ground truth ” visual attention data is highly desired. In this paper, the data of an eye-tracking experiment are integrated in two objective metrics well-known in literature. Experimental results demonstrate that there is indeed a gain in performance including visual attention in objective metrics. The amount of gain in performance tends to depend on the type of objective metric and image distortion. Index Terms — Visual attention, eye tracking, natural scene saliency, distortion metric, image quality assessment 1