Visual sensitivity to color errors in images of natural scenes

Aldaba, Mikel; Linhares, João M. M.; Pinto, Paulo Daniel Araújo; Nascimento, Sérgio M. C.; Amano, Kinjiro; Foster, David H.

doi:10.1017/s0952523806233467

Cited by 35 publications

(25 citation statements)

References 15 publications

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“…The worst case fitted 91% and the best 100%. For the original images of the paintings, the average chromatic error for the pixels out of gamut expressed in CIELAB color space was E* ab = 2.4, i.e., near threshold for complex images (Aldaba, Linhares, Pinto, Nascimento, Amano & Foster, 2006, CIE, 2011, Liu, Huang, Cui, Luo & Melgosa, 2013. For rotations of the color gamut, gamut compression occurred as each color out of gamut 8 was projected to the closest one displayable.…”

Section: Stimuli and Experimental Set-upmentioning

confidence: 99%

The colors of paintings and viewers’ preferences

et al. 2017

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Abstract. One hypothesis to explain the aesthetics of paintings is that it depends on the extent to which they mimic natural image statistics. In fact, paintings and natural scenes share several statistical image regularities but the colors of paintings seem generally more biased towards red than natural scenes. Is the particular option for colors in each painting, even if less naturalistic, critical for perceived beauty? Here we show that it is. In the experiments, 50 naïve observers, unfamiliar with the 10 paintings tested, could rotate the color gamut of the paintings and select the one producing the best subjective impression. The distributions of angles obtained are described by normal distributions with maxima deviating, on average, only 7 degrees from the original gamut orientation and full width at half maximum just above the threshold to perceive a chromatic change in the paintings. Crucially, for data pooled across observers and abstract paintings the maximum of the distribution was at zero degrees, i.e., the same as the original. This demonstrates that artists know what chromatic compositions match viewers' preferences and that the option for less naturalistic colors does not constraint the aesthetic value of paintings.3

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Section: Stimuli and Experimental Set-upmentioning

confidence: 99%

The colors of paintings and viewers’ preferences

et al. 2017

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“…But the visual significance of colorimetric errors in complex images is still an open question that has not been completely resolved. Previous experiments 19 have found that the observers' ability to discriminate between images needed on average a CIELab color difference of about 2.2, although large variations around this mean for a variety of images were also found. Because the illuminant-free images were obtained here by just using a three-channel RGB digital camera and not a spectral device, the results could be biased by these psychophysical and physical constraints.…”

Section: Discussionmentioning

confidence: 82%

“…Because the average color difference was around 7 ΔE Lab units our results confirm that so far there is no easy way to assess colorimetric accuracy in the reproduction of color images. 19 Color accuracy can only be assessed on the basis of a pixel-by-pixel calculation, which merely gives approximate overall perceptible differences after averaging out the results for the whole image. …”

Section: Color Albedo Recovery Outdoorsmentioning

confidence: 99%

Photometric-based recovery of illuminant-free color images using a red-green-blue digital camera

et al. 2012

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Albedo estimation has traditionally been used to make computational simulations of real objects under different conditions, but as yet no device is capable of measuring albedo directly. The aim of this work is to introduce a photometric-based color imaging framework that can estimate albedo and can reproduce the appearance both indoors and outdoors of images under different lights and illumination geometry. Using a calibration sample set composed of chips made of the same material but different colors and textures, we compare two photometric-stereo techniques, one of them avoiding the effect of shadows and highlights in the image and the other ignoring this constraint. We combined a photometric-stereo technique and a color-estimation algorithm that directly relates the camera sensor outputs with the albedo values. The proposed method can produce illuminant-free images with good color accuracy when a three-channel red-green-blue (RGB) digital camera is used, even outdoors under solar illumination.1 Introduction Our visual system allows us to distinguish elements such as color, texture, and surface shape, and our brains use this information to recognize objects under different lighting and observation conditions. Computational vision uses imaging devices to simulate the way the human visual system analyzes color images. Reproducing color appearance in color-based image displays depends on several factors such as the imaging device itself, the geometry of the lighting conditions, and the surface properties of the objects in question. When dealing with textured objects these factors become more evident; for example, the position of the camera and the object must be kept fixed because the color appearance of the object can change with the direction of illumination, which can cause a problem for object characterization. Thus, extracting information from textured images independently of lighting and imaging geometries poses a challenge for many computational vision systems. The use of the albedo rather than raw sensor response data can help to solve this issue. Albedo is usually defined as the ratio of scattered flux to that scattered and absorbed by a monodisperse or relatively uniform suspension, i.e., the ratio of flux scattered to the quantity of incident flux. 1 As opposed to intensity, albedo does not depend on the illumination conditions, and has been used in computer graphics to derive illuminantinvariant properties. 2 But the light reflected from a surface depends on the surface's spectral characteristics, the illumination geometry, the spectral content of the incident light, the viewing direction, and the roughness of the surface. This means that albedo recovery should not only be insensitive to the intensity of illumination but also to spectral changes in the reflected light.

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“…In a previous study, Aldaba et al [9] segmented the image in the CIELAB space into cubes. Each cube then suffer the application of a ∆E * ab error and it was concluded the existence of an error perception for ∆E * ab larger than 5 units.…”

Section: Database Manipulationmentioning

confidence: 99%

Objective evaluation of chromatic quality assessment

Bernardo

Pinheiro

Pereira

et al. 2013

2013 IEEE International Conference on Multimedia and Expo (ICME)

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This paper provides a study on the ability of Full-Reference quality metrics to measure the Quality of Experience that results on images that suffer chromatic errors. Considering this, the well known PSNR, MSSIM and VIFP will be compared with the MOS results. It is important to highlight that these measures are usually applied to the image luminance channel. However, in this study only the chromatic components of the image represented in the CIELAB color space have been changed resulting in similar values of luminosity. Moreover, the CIEDE2000 was also considered, because the knowledge of the scene illuminant allows a reliable computation. Using correlation estimates it was concluded that the CIEDE2000 and the MSSIM provides the best MOS estimation. The MSSIM results in a slightly better Pearson correlation value while the CIEDE2000 results in a better RMSE and Outlier Ratio. The VIFP also provides a good correlation, while the PSNR shows a poor correlation with the MOS values.

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Visual sensitivity to color errors in images of natural scenes

Cited by 35 publications

References 15 publications

The colors of paintings and viewers’ preferences

The colors of paintings and viewers’ preferences

Photometric-based recovery of illuminant-free color images using a red-green-blue digital camera

Objective evaluation of chromatic quality assessment

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