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Brightness judgments are a key part of the primate brain's visual analysis of the environment. There is general consensus that the perceived brightness of an image region is based not only on its actual luminance, but also on the photometric structure of its neighborhood. However, it is unclear precisely how a region's context influences its perceived brightness. Recent research has suggested that brightness estimation may be based on a sophisticated analysis of scene layout in terms of transparency, illumination and shadows. This work has called into question the role of low-level mechanisms, such as lateral inhibition, as explanations for brightness phenomena. Here we describe experiments with displays for which low-level and high-level analyses make qualitatively different predictions, and with which we can quantitatively assess the trade-offs between low-level and high-level factors. We find that brightness percepts in these displays are governed by low-level stimulus properties, even when these percepts are inconsistent with higher-level interpretations of scene layout. These results point to the important role of low-level mechanisms in determining brightness percepts.

Thesis (Ph. D.)--University of California, San Diego, 2009. / Title from first page of PDF file (viewed Oct. 7, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 57-58).

It has long been held that luminance acts as a cue for depth perception. But varying the luminance of a stimulus inevitably alters its contrast with its background. Recent research shows that contrast is a depth cue. I have distinguished two kinds of contrast, external contrast, the contrast of a stimulus with its background, and internal contrast, the contrast within the stimulus. I compared the relative apparent depth of two stimuli (both directly and indirectly; stimuli were either sine-wave filled hemifields, sine-wave filled squares, or plain squares), as their luminances and internal contrasts were varied along with the luminance of their background. I found internal and external contrast to be additive effects, whereby the stimulus with either a higher internal or external contrast appeared nearer. When the internal and external contrasts of the stimuli were equated, luminance acted as an ambiguous cue, with the lighter square appearing nearer for the majority of observers, and farther for a minority. Luminance may act as a depth cues from our experience with artificial lighting (artificial light varies ambiguously with depth). Contrast may act as a depth cue from its usual association with the reduction of contrast of objects with distance through the atmosphere. I conclude that luminance and contrast are independent depth-cues that are caused by two different mechanisms.

Contrast sensitivity to vertically oriented grating patterns with a sinusoidal luminance profile were examined between groups of observers varying either in gender or age. For each observer at each of the seven spatial frequencies tested (.75, 1.5, 3, 6, 7.5, 10, 15 cyc/deg) threshold values were calculated for either ascending or descending trials as well as a combination of both. These threshold values were numerically transformed into sensitivity values and contributed to a group mean contrast sensitivity score for each spatial frequency. No significant effect of gender was found but younger observers (mean age=22.6 yrs.) exhibited significantly better contrast sensitivity than the older aged group (mean age=66.2 yrs.) for ascending trials at 3, 1.5 and .75 cyc/deg--the lowest spatial frequencies tested. Contrast sensitivity was also correlated with various measures. These findings were discussed in relation to the existing literature on age and spatial contrast sensitivity and since the machine used to examine the contrast sensitivity function (CSF) in this study utilized a laser interferometric method of stimulus generation, possible neurological changes with aging to explain this noted loss were also considered. Also discussed were various parameters that effect the CSF with a view toward explaining the disparate findings of various existing studies of age and the CSF. / Arts, Faculty of / Psychology, Department of / Graduate

Thesis (M.S.)--Ohio University, June, 2003. / Title from PDF t.p. Includes bibliographical references (leaves 102-108).

Image quality measures are used to optimize image processing algorithms and evaluate their performances. The only reliable way to assess image quality is subjective evaluation by human observers, where the mean value of their scores is used as the quality measure. This is known as mean opinion score (MOS). In addition to this measure there are various objective (quantitative) measures. Most widely used quantitative measures are: mean squared error (MSE), peak signal to noise ratio (PSNR) and signal to noise ratio (SNR). Since these simple measures do not always produce results that are in agreement with subjective evaluation, many other quality measures have been proposed. They are mostly various modifications of MSE, which try to take into account some properties of human visual system (HVS) such as nonlinear character of brightness perception, contrast sensitivity function (CSF) and texture masking. In these approaches quality measure is computed as MSE of input image intensities or frequency domain coefficients obtained after some transform (DFT, DCT etc.), weighted by some coefficients which account for the mentioned properties of HVS. These measures have some advantages over MSE, but their ability to predict image quality is still limited. A different approach is presented here. Quality measure proposed here uses simple model of HVS, which has one user-defined parameter, whose value depends on the reference image. This quality measure is based on the average value of locally computed correlation coefficients. This takes into account structural similarity between original and distorted images, which cannot be measured by MSE or any kind of weighted MSE. The proposed measure also differentiates between random and signal dependant distortion, because these two have different effect on human observer. This is achieved by computing the average correlation coefficient between reference image and error image. Performance of the proposed quality measure is illustrated by examples involving images with different types of degradation.

Title from PDF of title page. / Document formatted into pages; contains 82 pages. / Thesis (M.S.E.E.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: Image quality measures are used to optimize image processing algorithms and evaluate their performances. The only reliable way to assess image quality is subjective evaluation by human observers, where the mean value of their scores is used as the quality measure. This is known as mean opinion score (MOS). In addition to this measure there are various objective (quantitative) measures. Most widely used quantitative measures are: mean squared error (MSE), peak signal to noise ratio (PSNR) and signal to noise ratio (SNR). Since these simple measures do not always produce results that are in agreement with subjective evaluation, many other quality measures have been proposed. They are mostly various modifications of MSE, which try to take into account some properties of human visual system (HVS) such as nonlinear character of brightness perception, contrast sensitivity function (CSF) and texture masking. / ABSTRACT: In these approaches quality measure is computed as MSE of input image intensities or frequency domain coefficients obtained after some transform (DFT, DCT etc.), weighted by some coefficients which account for the mentioned properties of HVS. These measures have some advantages over MSE, but their ability to predict image quality is still limited. A different approach is presented here. Quality measure proposed here uses simple model of HVS, which has one user-defined parameter, whose value depends on the reference image. This quality measure is based on the average value of locally computed correlation coefficients. This takes into account structural similarity between original and distorted images, which cannot be measured by MSE or any kind of weighted MSE. The proposed measure also differentiates between random and signal dependant distortion, because these two have different effect on human observer. / ABSTRACT: This is achieved by computing the average correlation coefficient between reference image and error image. Performance of the proposed quality measure is illustrated by examples involving images with different types of degradation. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

A síntese de imagens com alta faixa dinâmica é uma prática cada vez mais comum em computação gráfica. O desafio consiste em relacionar o grande conjunto de intensidades da imagem sintetizada com um sub-conjunto muito inferior suportado por um dispositivo de exibição, evitando a perda de detalhes contrastivos. Os operadores locais de reprodução de tons (local tone-mapping operators) são capazes de realizar tal compressão, adaptando o nível de luminância de cada pixel com respeito à sua vizinhança. Embora produzam resultados significativamente superiores aos operadores globais, o custo computacional é consideravelmente maior, o que vem impedindo sua utilização em aplicações em tempo real. Este trabalho apresenta uma técnica para aproximar o operador fotográfico local de reprodução de tons. Todas as etapas da técnica são implementadas em GPU, adequando-se ao cenário de aplicações em tempo real, sendo significativamente mais rápida que implementações existentes e produzindo resultados semelhantes. A abordagem é baseada no uso de tabelas de áreas acumuladas (summed-area tables) para acelerar a convolução das vizinhanças, usando filtros da média (box-filter), proporcionando uma solução elegante para aplicações que utilizam imagens em alta faixa dinâmica e que necessitam de performance sem comprometer a qualidade da imagem sintetizada. Uma investigação sobre algoritmos para a geração de somatórios pré-fixados (prefix sum) e uma possível melhoria para um deles também são apresentada. / High dynamic range (HDR) rendering is becoming an increasingly popular technique in computer graphics. Its challenge consists on mapping the resulting images’ large range of intensities to the much narrower ones of the display devices in a way that preserves contrastive details. Local tone-mapping operators effectively perform the required compression by adapting the luminance level of each pixel with respect to its neighborhood. While they generate significantly better results when compared to global operators, their computational costs are considerably higher, thus preventing their use in real-time applications. This work presents a real-time technique for approximating the photographic local tone reproduction that runs entirely on the GPU and is significantly faster than existing implementations that produce similar results. Our approach is based on the use of summed-area tables for accelerating the convolution of the local neighborhoods with a box filter and provides an attractive solution for HDR rendering applications that require high performance without compromising image quality. A survey of prefix sum algorithms and possible improvements are also presented.