Contrast sensitivity and discrimination of complex scenes

Triantaphillidou, Sophie; Jarvis, John R.; Gupta, Gaurav

doi:10.1117/12.2006076

Cited by 3 publications

(9 citation statements)

References 35 publications

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“…These functions expand upon Barten's mechanistic model, and have been verified with extensive 2AFC pair-comparisons of band-altered images (12) . We expect them to be more applicable than 'traditional' CSFs, when modeling observations of the real-world or natural images, since these conditions expose the HVS to a plethora of complex frequencies.…”

Section: ) Icsf Describes the Hvs' Contrast Detection Threshold Of Amentioning

confidence: 99%

“…Contrast spectra used in the modeling of the cCSF and cVPF did not account for the display MTF, although this would have had a maximum effect of approximately 15%, at 32 cycles/degree ( Figure 5). cCSF and cVPF models also required K values (12) . K values of between 0.1 and 0.07 were selected after comparison with Triantaphillidou et al observer data (12) , for images where observation data were available.…”

Section: Modeling Of Sensitivity and Discrimination Functionsmentioning

confidence: 99%

“…cCSF and cVPF models also required K values (12) . K values of between 0.1 and 0.07 were selected after comparison with Triantaphillidou et al observer data (12) , for images where observation data were available. A mean K value was selected for images with no observation data.…”

Section: Modeling Of Sensitivity and Discrimination Functionsmentioning

confidence: 99%

“…iCSFs, cCSFs and cVPFs were calculated according to Triantaphillidou et al (12) . This required mean overall display luminances to be calculated for each image, accounting for the effect of the neutral background of pixel value 180 across the R, G and B channels, which the images would be displayed upon.…”

Section: Modeling Of Sensitivity and Discrimination Functionsmentioning

confidence: 99%

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Image quality optimization, via application of contextual contrast sensitivity and discrimination functions

et al. 2015

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What is the best luminance contrast weighting-function for image quality optimization? 'Traditional' contrast sensitivity functions (CSFs) have been applied as weighting-functions in image difference metrics. This weighting also resulted in increased sharpness and color-preference, according to previous psychophysical research. We suggest 'contextual' CSFs (cCSFs) and 'contextual' discrimination functions (cVPFs) should provide bases for further improvement; these functions are directly measured from pictorial scenes, modeling threshold and suprathreshold sensitivities within the context of complex masking information. Image quality assessment is understood to require detection/discrimination of masked signals, making 'contextual' CSFs directly relevant.In this investigation, images are weighted with a 'traditional' CSF, cCSF, cVPF and a 'constant' function. Controlled mutations of these functions are also applied as weighting-functions, seeking the optimal band weighting for quality optimization. Image quality, sharpness and naturalness are then assessed in two-alternative forced-choice psychophysical tests. Maximal quality, results from cCSFs and cVPFs, mutated to boost contrast in the higher visible frequencies.

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Section: ) Icsf Describes the Hvs' Contrast Detection Threshold Of Amentioning

confidence: 99%

Section: Modeling Of Sensitivity and Discrimination Functionsmentioning

confidence: 99%

Section: Modeling Of Sensitivity and Discrimination Functionsmentioning

confidence: 99%

Section: Modeling Of Sensitivity and Discrimination Functionsmentioning

confidence: 99%

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Image quality optimization, via application of contextual contrast sensitivity and discrimination functions

et al. 2015

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“…Here, first we investigated the effect of bi-cubic interpolation on image contrast by measuring root-mean-square (RMS) luminance contrast [9][10][11][12] . No significant effect of bi-cubic interpolation on image contrast was evident.…”

Section: Introductionmentioning

confidence: 99%

Just noticeable differences in perceived image contrast with changes in displayed image size

2014

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An evaluation of the change in perceived image contrast with changes in displayed image size was carried out. This was achieved using data from four psychophysical investigations, which employed techniques to match the perceived contrast of displayed images of five different sizes. A total of twenty-four S-shape polynomial functions were created and applied to every original test image to produce images with different contrast levels. The objective contrast related to each function was evaluated from the gradient of the mid-section of the curve (gamma). The manipulation technique took into account published gamma differences that produced a just-noticeable-difference (JND) in perceived contrast. The filters were designed to achieve approximately half a JND, whilst keeping the mean image luminance unaltered. The processed images were then used as test series in a contrast matching experiment. Sixty-four natural scenes, with varying scene content acquired under various illumination conditions, were selected from a larger set captured for the purpose. Results showed that the degree of change in contrast between images of different sizes varied with scene content but was not as important as equivalent perceived changes in sharpness 1 .

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Spatial contrast sensitivity and discrimination in pictorial images

2014

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This paper describes continuing research concerned with the measurement and modeling of human spatial contrast sensitivity and discrimination functions, using complex pictorial stimuli. The relevance of such functions in image quality modeling is also reviewed. Previously 1,2 we presented the choice of suitable contrast metrics, apparatus and laboratory setup , the stimuli acquisition and manipulation, the methodology employed in the subjective tests and initial findings. Here we present our experimental paradigm, the measurement and modeling of the following visual response functions: i) Isolated Contrast Sensitivity Function (iCSF); Contextual Contrast Sensitivity Function (cCSF); Isolated Visual Perception Function (iVPF); Contextual Visual Perception Function (cVPF). Results indicate that the measured cCSFs are lower in magnitude than the iCSFs and flatter in profile. Measured iVPFs, cVPFs and cCSFs are shown to have similar profiles. Barten's contrast detection model 3 was shown to successfully predict iCSF. For a given frequency band, the reduction, or masking of cCSF compared with iCSF sensitivity is predicted from the linear amplification model (LAM) 4. We also show that our extension of Barten's contrast discrimination model 1,5 is capable of describing iVPFs and cVPFs. We finally reflect on the possible implications of the measured and modeled profiles of cCSF and cVPF to image quality modeling.

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Contrast sensitivity and discrimination of complex scenes

Cited by 3 publications

References 35 publications

Image quality optimization, via application of contextual contrast sensitivity and discrimination functions

Image quality optimization, via application of contextual contrast sensitivity and discrimination functions

Just noticeable differences in perceived image contrast with changes in displayed image size

Spatial contrast sensitivity and discrimination in pictorial images

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