Empirical formula for creating error bars for the method of paired comparison

Montag, Ethan D.

doi:10.1117/1.2181547

Cited by 52 publications

(35 citation statements)

References 5 publications

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“…Treating the rankings as paired-comparison data and applying Montag's method [11] resulted in z-scores of 0, 1.5, and 0.98, for laptop, TV, and projector, respectively, with all pairwise differences significant at the 95% level. For format, the paired-comparison result was 15 of 24 in favor of the static format, which is just not significant.…”

Section: Resultsmentioning

confidence: 99%

Effects of image size and interactivity in lighting visualization

Murdoch

Stokkermans

2014

SPIE Proceedings

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Rendered images of varied lighting conditions in a virtual environment have been shown to provide a perceptually accurate visual impression of those in a real environment, providing a valuable tool set for the development and communication of new lighting solutions. In order to further improve this tool set, an experiment was conducted to assess the impact of image size and viewing interactivity on perceptual accuracy. It was found that a high-quality TVsized display outperforms a smaller laptop screen and a larger projected image on most measures, and that the expected value of the interactive panoramic format was masked by the fatigue of using it repeatedly.

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Section: Resultsmentioning

confidence: 99%

Effects of image size and interactivity in lighting visualization

Murdoch

Stokkermans

2014

SPIE Proceedings

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“…For the pair comparison, software developed locally gathers the results in frequency matrices that are then processed as the other experiment to obtain the z-scores. The 95 per cent confidence intervals are determined by using the empirical formula by Montag 16 .…”

Section: Viewing Conditionsmentioning

confidence: 99%

Perceptual evaluation of color gamut mapping algorithms

Dugay¹,

Farup²,

Hardeberg³

2008

Color Research & Application

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Abstract:The recommendation of the CIE has been followed as close as possible to evaluate the accuracy of five color gamut mapping algorithms (GMAs), two non-spatial and three spatial algorithms, by psychophysical experiments with 20 test images, 20 observers, a test done on paper and a second one on display. Even though the results do not show any overall "winner", one GMA is definitely not perceived as accurate. The importance of a high number of test images to obtain robust evaluation is underlined by the high variability of the results depending on the test images. Correlations between the percentage of out-of-gamut pixels, the number of distinguishable pairs of GMAs and the perceived difficulty to distinguish them have been found. The type of observers is also of importance. The experts, who prefer a spatial GMA, show a stronger consensus and look especially for a good rendering of details, whereas the non-experts hardly make a difference between the GMAs.

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“…Since these results should be evaluated on the differences in perceptual score, we set the perceptual score for CG to 0. The 95% confidence intervals were calculated by the empirical formula of [41]. Both figures show that face compensation leads to a significant improvement in the overall perceptual score, and the perceptual score for each test image independently.…”

Section: Paired Comparisonmentioning

confidence: 99%

“…We compare the performance of CG, CG with facial blur compensation, and Sweeper with facial blur compensation in a paired comparison [41] on the 10 low-depth-of-field test images of Fig. 5.…”

Section: Paired Comparisonmentioning

confidence: 99%

Efficient dense blur map estimation for automatic 2D-to-3D conversion

Vosters

Haan

2012

SPIE Proceedings

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Focus is an important depth cue for 2D-to-3D conversion of low depth-of-field images and video. However, focus can be only reliably estimated on edges. Therefore, Bea et al. [1] first proposed an optimization based approach to propagate focus to non-edge image portions, for single image focus editing. While their approach produces accurate dense blur maps, the computational complexity and memory requirements for solving the resulting sparse linear system with standard multigrid or (multilevel) preconditioning techniques, are infeasible within the stringent requirements of the consumer electronics and broadcast industry. In this paper we propose fast, efficient, low latency, line scanning based focus propagation, which mitigates the need for complex multigrid or (multilevel) preconditioning techniques. In addition we propose facial blur compensation to compensate for false shading edges that cause incorrect blur estimates in people's faces. In general shading leads to incorrect focus estimates, which may lead to unnatural 3D and visual discomfort. Since visual attention mostly tends to faces, our solution solves the most distracting errors. A subjective assessment by paired comparison on a set of challenging low-depth-of-field images shows that the proposed approach achieves equal 3D image quality as optimization based approaches, and that facial blur compensation results in a significant improvement.

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Empirical formula for creating error bars for the method of paired comparison

Abstract: Abstract. The method of paired comparison based on

Cited by 52 publications

References 5 publications

Effects of image size and interactivity in lighting visualization

Effects of image size and interactivity in lighting visualization

Perceptual evaluation of color gamut mapping algorithms

Efficient dense blur map estimation for automatic 2D-to-3D conversion

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