Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.

/pdf/image-quality-assessment-from-error-visibility-to-structural-1rlwcqe34t.pdf

Image quality assessment: from error visibility to structural similarity

to the Human Visual System (HVS).- Visual Attention.- Neurological Substrate of the HVS.- Visual Psychophysics.- Taxonomy and Models of Eye Movements.- Eye Tracking Systems.- Eye Tracking Techniques.- Head-Mounted System Hardware Installation.- Head-Mounted System Software Development.- Head-Mounted System Calibration.- Table-Mounted System Hardware Installation.- Table-Mounted System Software Development.- Table-Mounted System Calibration.- Eye Movement Analysis.- Eye Tracking Methodology.- Experimental Design.- Suggested Empirical Guidelines.- Case Studies.- Eye Tracking Applications.- Diversity and Types of Eye Tracking Applications.- Neuroscience and Psychology.- Industrial Engineering and Human Factors.- Marketing/Advertising.- Computer Science.- Conclusion.

Eye Tracking Methodology: Theory and Practice

Modeling visual attention-particularly stimulus-driven, saliency-based attention-has been a very active research area over the past 25 years. Many different models of attention are now available which, aside from lending theoretical contributions to other fields, have demonstrated successful applications in computer vision, mobile robotics, and cognitive systems. Here we review, from a computational perspective, the basic concepts of attention implemented in these models. We present a taxonomy of nearly 65 models, which provides a critical comparison of approaches, their capabilities, and shortcomings. In particular, 13 criteria derived from behavioral and computational studies are formulated for qualitative comparison of attention models. Furthermore, we address several challenging issues with models, including biological plausibility of the computations, correlation with eye movement datasets, bottom-up and top-down dissociation, and constructing meaningful performance measures. Finally, we highlight current research trends in attention modeling and provide insights for future.

State-of-the-Art in Visual Attention Modeling

Bayesian surprise attracts human attention.

A model of bottom-up overt attention is proposed based on the principle of maximizing information sampled from a scene. The proposed operation is based on Shannon's self-information measure and is achieved in a neural circuit, which is demonstrated as having close ties with the circuitry existent in die primate visual cortex. It is further shown that the proposed salicney measure may be extended to address issues that currently elude explanation in the domain of saliency based models. Results on natural images are compared with experimental eye tracking data revealing the efficacy of the model in predicting the deployment of overt attention as compared with existing efforts.

/pdf/saliency-based-on-information-maximization-2lip2063yu.pdf

Saliency Based on Information Maximization

Many machine vision applications, such as compression, pictorial database querying, and image understanding, often need to analyze in detail only a representative subset of the image, which may be arranged into sequences of loci called regions-of-interest (ROIs). We have investigated and developed a methodology that serves to automatically identify such a subset of aROIs (algorithmically detected ROIs) using different image processing algorithms (IPAs), and appropriate clustering procedures. In human perception, an internal representation directs top-down, context-dependent sequences of eye movements to fixate on similar sequences of hROIs (human identified ROIs). In the paper, we introduce our methodology and we compare aROIs with hROIs as a criterion for evaluating and selecting bottom-up, context-free algorithms. An application is finally discussed.

/pdf/algorithms-for-defining-visual-regions-of-interest-37jh871ctn.pdf

Algorithms for defining visual regions-of-interest: comparison with eye fixations

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Algorithms for defining visual regions-of-interest: comparison with eye fixations