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

Further Research on the Sensor Correlation Method for Scene Illuminant Classification.

The sections in this article are:


1
Scope and Organization

2
Physiological Explanation in Visual Science: Three Classes of Perceptual Phenomena

3
Visual Acuity
3.1
Overview

3.2
The Retinal Image

3.3
Spatial Modulation Detection

3.4
Neural Limits of Visual Acuity




4
Color Vision
4.1
Scotopic Spectral Sensitivity

4.2
Monochromacy

4.3
Dichromacy

4.4
Trichromacy

4.5
Color Blindness

4.6
Seeing Colors




5
Visual Adaptation
5.1
Overview

5.2
Experimental Paradigms

5.3
Identifying Neural Substrates

5.4
Single- Variable Theories

5.5
Concluding Comment




6
Note Added in Proof

The Beginnings of Visual Perception: The Retinal Image and its Initial Encoding. Appendix: Fourier Transforms and Shift‐Invariant Linear Operators

We compare the major white matter tracts in human and macaque occipital lobe. The comparative anatomy of human and macaque occipital white matter tracts reveals both similarities and significant differences in spatial arrangement and relative sizes of the tracts. There are several apparently homologous tracts in the two species, including the vertical occipital fasciculus (VOF), optic radiation, forceps major, and inferior longitudinal fasciculus. There is one large human tract, the inferior occipital-frontal fasciculus, with no corresponding fasciculus in macaque. The macaque VOF is compact and its fibers intertwine with the dorsal segment of the ILF, but the human VOF is much more elongated in the anterior-posterior direction and its fibers do not intertwine with the ILF. These similarities and differences will be useful in establishing which circuitry in the macaque can serve as an accurate model for human visual cortex.

https://www.biorxiv.org/content/biorxiv/early/2016/08/27/069443.full.pdf

Occipital white matter tracts in human and macaque

Humans resolve the spatial alignment between two visual stimuli at a resolution that is substantially finer than the spacing between the foveal cones. In this paper, we analyze the factors that limit the information at the cone photoreceptors that is available to make these acuity judgments (Vernier acuity). We use open-source software, ISETBIO1 to quantify the stimulus and encoding stages in the front-end of the human visual system, starting with a description of the stimulus spectral radiance and a computational model that includes the physiological optics, inert ocular pigments, eye movements, photoreceptor sampling and absorptions. The simulations suggest that the visual system extracts the information available within the spatiotemporal pattern of photoreceptor absorptions within a small spatial (0.12 deg) and temporal (200 ms) regime. At typical display luminance levels, the variance arising from the Poisson absorptions and small eye movements (tremors and microsaccades) both appear to be critical limiting factors for Vernier acuity.

/pdf/simulating-retinal-encoding-factors-influencing-vernier-3nrlzk1zp7.pdf

Simulating retinal encoding: factors influencing Vernier acuity

There are many scientific, medical and industrial imaging applications where users have full control of the scene illumination and color reproduction is not the primary objective For example, it is possible to co-design sensors and spectral illumination in order to classify and detect changes in biological tissues, organic and inorganic materials, and object surface properties. In this paper, we propose two different approaches to illuminant spectrum selection for surface classification. In the supervised framework we formulate a biconvex optimization problem where we alternate between optimizing support vector classifier weights and optimal illuminants. We also describe a sparse Principal Component Analysis (PCA) dimensionality reduction approach that can be used with unlabeled data. We efficiently solve the non-convex PCA problem using a convex relaxation and Alternating Direction Method of Multipliers (ADMM). We compare the classification accuracy of a monochrome imaging sensor with optimized illuminants to the classification accuracy of conventional RGB cameras with natural broadband illumination.

/pdf/designing-illuminant-spectral-power-distributions-for-3ij5nxs365.pdf

Brian A. Wandell

Papers

Further Research on the Sensor Correlation Method for Scene Illuminant Classification.

The Beginnings of Visual Perception: The Retinal Image and its Initial Encoding. Appendix: Fourier Transforms and Shift‐Invariant Linear Operators

Occipital white matter tracts in human and macaque

Simulating retinal encoding: factors influencing Vernier acuity

Designing Illuminant Spectral Power Distributions for Surface Classification