Perceptual evaluation of color gamut mapping algorithms

Dugay, Fabienne; Farup, Ivar; Hardeberg, Jon Yngve

doi:10.1002/col.20443

Cited by 36 publications

(28 citation statements)

References 13 publications

(5 reference statements)

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“…However, in this paper we focus on the short-term memory effects. Work has also shown that there is a difference between expert and non-expert observers in psychometric experiments [14,15]. Le Moan et al [16] compared two different setups for paired comparison experiments, showing that the results between the two setups were significantly different.…”

Section: Still Imagesmentioning

confidence: 99%

Memory Effects in Subjective Quality Assessment of X-Ray Images

2017

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Abstract. Experiments with human observers is considered as the most precise way for the assessment of image quality. Although widely used, such experiments have its pitfalls and hazards. In this work we investigate if the quality rating of previously viewed images influence the rating given to the current image, which we refer to as the rating memory effect. A subjective experiment with a group of observers rating x-ray images of different radiation dose was used for the basis of the analysis. The results indicate a memory effect, meaning that the rating of an image can be influenced by the ratings given in previously judged images.

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Section: Still Imagesmentioning

confidence: 99%

Memory Effects in Subjective Quality Assessment of X-Ray Images

2017

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“…This analysis lead to the proposal of a new spatial GMA based on a multi-scale image representation, which performs gamut mapping of the scaled images at increasing dimensions. In accordance with 13 we will refer to this as the Gatta algorithm.…”

Section: Farup Et Almentioning

confidence: 99%

“…An experiment had previously been performed by Fabienne Dugay, 13,14 in which several point based and spatial GMAs had been compared. Three spatial and two point-based GMAs were used on 20 different images.…”

Section: Motivationmentioning

confidence: 99%

“…This is one of the few psychophysical experiments that have been performed for gamut mapped images that involve relatively many images and observers, since this is a very time-consuming procedure. One of the conclusions 14 was that the choice of images had a great impact on the result, since the algorithms showed significantly different performance on the images used in the experiment. Figure 1 illustrates the performance of the GMAs on each separate image.…”

Section: Motivationmentioning

confidence: 99%

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Predicting the performance of a spatial gamut mapping algorithm

2009

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Gamut mapping algorithms are currently being developed to take advantage of the spatial information in an image to improve the utilization of the destination gamut. These algorithms try to preserve the spatial information between neighboring pixels in the image, such as edges and gradients, without sacrificing global contrast. Experiments have shown that such algorithms can result in significantly improved reproduction of some images compared with non-spatial methods. However, due to the spatial processing of images, they introduce unwanted artifacts when used on certain types of images. In this paper we perform basic image analysis to predict whether a spatial algorithm is likely to perform better or worse than a good, non-spatial algorithm. Our approach starts by detecting the relative amount of areas in the image that are made up of uniformly colored pixels, as well as the amount of areas that contain details in out-of-gamut areas. A weighted difference is computed from these numbers, and we show that the result has a high correlation with the observed performance of the spatial algorithm in a previously conducted psychophysical experiment.

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“…It is an established fact that such spatial colour gamut mapping algorithms can be superior to the conventional algorithms for many types of images [10]. However, the behaviour of the algorithms is still somewhat unpredictable in that they can produce unwanted artefacts such as hue shifts and halos for certain images.…”

Section: Introductionmentioning

confidence: 99%

Gamut Mapping in RGB Colour Spaces with the Iterative Ratios Diffusion Algorithm

Gatta¹,

Farup²

2017

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Different reproduction devices can have different sets of reproducible colours. These sets are called gamuts. The process of transforming colours from one device (or image) gamut to another is called gamut mapping. Gamut mapping has many technical issues to be considered: the used colour space, direction and magnitude of the mapping and whether and to which extent ingamut colours should be altered. Spatially invariant algorithms treat all the pixels independently on their position in the image. Spatially variant (local) algorithms allows a better rendition but introduces the problem of artefacts and/or haloing in the resulting image. In this paper we propose a spatially variant gamut mapping algorithm that creates virtually no artefacts nor haloing in the resulting image. We start from an analysis of the Retinex algorithm and devise proper functionals to build an algorithm which tries to maintain spatial ratios in the image while mapping it into the gamut and, at the same time, avoids all drawbacks of Retinex approaches. We suggest to perform the mapping in an RGB colour space rather than one of the perceptually more homogeneous ones. Although less homogeneous, we experimentally show that RGB colour spaces actually have better hue constancy according to a certain criterion.

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Perceptual evaluation of color gamut mapping algorithms

Cited by 36 publications

References 13 publications

Memory Effects in Subjective Quality Assessment of X-Ray Images

Memory Effects in Subjective Quality Assessment of X-Ray Images

Predicting the performance of a spatial gamut mapping algorithm

Gamut Mapping in RGB Colour Spaces with the Iterative Ratios Diffusion Algorithm

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