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Journal ArticleDOI

Contrast in complex images.

Eli Peli
- 01 Oct 1990 - 
- Vol. 7, Iss: 10, pp 2032-2040
TLDR
A definition of local band-limited contrast in images is proposed that assigns a contrast value to every point in the image as a function of the spatial frequency band and is helpful in understanding the effects of image-processing algorithms on the perceived contrast.
Abstract
The physical contrast of simple images such as sinusoidal gratings or a single patch of light on a uniform background is well defined and agrees with the perceived contrast, but this is not so for complex images. Most definitions assign a single contrast value to the whole image, but perceived contrast may vary greatly across the image. Human contrast sensitivity is a function of spatial frequency; therefore the spatial frequency content of an image should be considered in the definition of contrast. In this paper a definition of local band-limited contrast in images is proposed that assigns a contrast value to every point in the image as a function of the spatial frequency band. For each frequency band, the contrast is defined as the ratio of the bandpass-filtered image at the frequency to the low-pass image filtered to an octave below the same frequency (local luminance mean). This definition raises important implications regarding the perception of contrast in complex images and is helpful in understanding the effects of image-processing algorithms on the perceived contrast. A pyramidal image-contrast structure based on this definition is useful in simulating nonlinear, threshold characteristics of spatial vision in both normal observers and the visually impaired.

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Citations
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Dissertation

Perceptually Modulated Level of Detail for Virtual Environments

Martin Reddy
TL;DR: This thesis presents a generic and principled solution for optimising the visual complexity of any arbitrary computer-generated virtual environment (VE) with the ultimate goal of reducing the inherent latencies of current virtual reality (VR) technology.
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Image-Difference Prediction: From Grayscale to Color

TL;DR: An image-difference framework that comprises image normalization, feature extraction, and feature combination is presented that shows significantly higher prediction accuracy on a gamut-mapping dataset than all other evaluated measures.
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Image enhancement in the JPEG domain for people with vision impairment

TL;DR: Experiments with visually impaired patients show improved perceived image quality at moderate levels of enhancement but rejection of artifacts caused by higher levels of Enhancement, suggesting a need for further research into this area.
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A Visual Detection System for Rail Surface Defects

TL;DR: An intelligent vision detection system (VDS) for discrete surface defects and the local Michelson-like contrast (MLC) measure to enhance rail images and the new automatic thresholding method-proportion emphasized maximum entropy (PEME) thresholding algorithm are presented.
Journal ArticleDOI

Image dissimilarity

TL;DR: A number of representative instrumental models for image Dissimilarity with respect to their ability to predict both image dissimilarity and image quality, as perceived by human subjects are compared.
References
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Journal ArticleDOI

Relations between the statistics of natural images and the response properties of cortical cells.

TL;DR: The results obtained with six natural images suggest that the orientation and the spatial-frequency tuning of mammalian simple cells are well suited for coding the information in such images if the goal of the code is to convert higher-order redundancy into first- order redundancy.
Journal ArticleDOI

Spatial frequency selectivity of cells in macaque visual cortex

TL;DR: Among other things, it is shown that many stirate cells have quite narrow spatial bandwidths and at a given retinal eccentricity, the distribution of peak frequency covers a wide range of frequencies; these findings support the basic multiple channel notion.
Journal ArticleDOI

Feature Detection in Human Vision: A Phase-Dependent Energy Model

TL;DR: A simple and biologically plausible model of how mammalian visual systems could detect and identify features in an image is presented and it is suggested that the points in a waveform that have unique perceptual significance as ‘lines’ and ‘edges’ are the points where the Fourier components of the waveform come into phase with each other.
Journal ArticleDOI

Contrast constancy: deblurring in human vision by spatial frequency channels.

TL;DR: It is argued that spatial frequency channels in the visual cortex are organized to compensate for earlier attenuation, and achieves a dramatic 'deblurring' of the image, and optimizes the clarity of vision.
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