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

A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail.

/pdf/a-model-of-saliency-based-visual-attention-for-rapid-scene-wv0n3yqxix.pdf

A model of saliency-based visual attention for rapid scene analysis

https://surfer.nmr.mgh.harvard.edu/ftp/articles/dale99-recon1.pdf

Cortical surface-based analysis. I. Segmentation and surface reconstruction

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

A method of using functional magnetic resonance imaging (fMRI) to measure retinotopic organization within human cortex is described. The method is based on a visual stimulus that creates a traveling wave of neural activity within retinotopically organized visual areas. We measured the fMRI signal caused by this stimulus in visual cortex and represented the results on images of the flattened cortical sheet. We used the method to locate visual areas and to evaluate the spatial precision of fMRI. Specifically, we: (i) identified the borders between several retinotopically organized visual areas in the posterior occipital lobe; (ii) measured the function relating cortical position to visual field eccentricity within area V1; (iii) localized activity to within 1.1 mm of visual cortex; and (iv) estimated the spatial resolution of the fMRI signal and found that signal amplitude falls to 60% at a spatial frequency of 1 cycle per 9 mm of visual cortex. This spatial resolution is consistent with a linespread whose full width at half maximum spreads across 3.5 mm of visual cortex. In a series of experiments, we measured the retinotopic organization of human cortical area V1 and identified the locations of other nearby retinotopically organized visual areas. We also used the retinotopic organization of human primary visual cortex to measure the spatial localization and spatial resolution that can be obtained from functional magnetic resonance imaging (fMRI) of human visual cortex. Human primary visual cortex (area V1) is located in the

Retinotopic organization in human visual cortex and the spatial precision of functional MRI.

The development of functional magnetic resonance imaging (fMRI) has brought together a broad community of scientists interested in measuring the neural basis of the human mind. Because fMRI signals are an indirect measure of neural activity, interpreting these signals to make deductions about the nervous system requires some understanding of the signaling mechanisms. We describe our current understanding of the causal relationships between neural activity and the blood-oxygen-level-dependent (BOLD) signal, and we review how these analyses have challenged some basic assumptions that have guided neuroscience. We conclude with a discussion of how to use the BOLD signal to make inferences about the neural signal.

Interpreting the BOLD signal.

Originally published in Contemporary Psychology: APA Review of Books, 1997, Vol 42(7), 649-649. In Foundations of Vision (see record 1995-98050-000), Brian Wandell divides the study of vision into three parts: encoding, representation, and interpretation. Each part is designed to inform students on

Foundations of vision

https://www.cns.nyu.edu/~david/courses/sm12/Readings/Wandell-Neuron2007.pdf

Brian A. Wandell

Papers

Retinotopic organization in human visual cortex and the spatial precision of functional MRI.

Interpreting the BOLD signal.

Foundations of vision

Visual Field Maps in Human Cortex

Population receptive field estimates in human visual cortex