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R W G Hunt - One of the best experts on this subject based on the ideXlab platform.
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application of a model of Color Appearance to practical problems in imaging
Proceedings of the IEEE, 2002Co-Authors: R W G HuntAbstract:Some of the reasons for needing a Color Appearance model in imaging are given and the use of such a model in deriving a Color reproduction index is described. There are three reasons why a model of Color Appearance is a necessary tool in Color imaging systems. First, because the viewing conditions for images are often different at different stages of reproduction systems, it is necessary to allow for their visual effects; conventional Colorimetry does not do this, but a model of Color Appearance can supply the necessary adjustments. Second, different display media typically have different Color gamuts and a regime of gamut mapping is therefore often required; it is more appropriate to conduct this mapping in an Appearance space. Third, it is desirable to be able to assess the quality of Color reproductions in terms of a Color reproduction index and a model of Color Appearance can provide a metric that is suitable as the basis of such an index.
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the ciecam02 Color Appearance model
Color Imaging Conference, 2002Co-Authors: Nathan Moroney, Mark D Fairchild, R W G Hunt, Ronnier M Luo, Todd NewmanAbstract:The CIE Technical Committee 8-01, Color Appearance models for Color management applications, has recently proposed a single set of revisions to the CIECAM97s Color Appearance model. This new model, called CIECAM02, is based on CIECAM97s but includes many revisions1-4 and some simplifications. A partial list of revisions includes a linear chromatic adaptation transform, a new non-linear response compression function and modifications to the calculations for the perceptual attribute correlates. The format of this paper is an annotated description of the forward equations for the model. Introduction The CIECAM02 Color Appearance model builds upon the basic structure and form of the CIECAM97s5,6 Color Appearance model. This document describes the single set of revisions to the CIECAM97s model that make up the CIECAM02 Color Appearance model. There were many, often conflicting, considerations such as compatibility with CIECAM97s, prediction performance, computational complexity, invertibility and other factors. The format for this paper will differ from previous papers introducing a Color Appearance model. Often a general description of the model is provided, then discussion about its performance and finally the forward and inverse equations are listed separately in an appendix. Performance of the CIECAM02 model will be described elsewhere7 and for the purposes of brevity this paper will focus on the forward model. Specifically, this paper will attempt to document the decisions that went into the design of CIECAM02. For a complete description of the forward and inverse equations, as well as usage guidelines, interested readers are urged to refer to the TC 8-01 web site8 or to the CIE for the latest draft or final copy of the technical report. This paper is not intended to provide a definitive reference for implementing CIECAM02 but as an introduction to the model and a summary of its structure. Data Sets The CIECAM02 model, like CIECAM97s, is based primarily on a set corresponding Colors experiments and a collection of Color Appearance experiments. The corresponding Color data sets9,10 were used for the optimization of the chromatic adaptation transform and the D factor. The LUTCHI Color Appearance data11,12 was the basis for optimization of the perceptual attribute correlates. Other data sets and spaces were also considered. The NCS system was a reference for the e and hue fitting. The chroma scaling was also compared to the Munsell Book of Color. Finally, the saturation equation was based heavily on recent experimental data.13 Summary of Forward Model A Color Appearance model14,15 provides a viewing condition specific means for transforming tristimulus values to or from perceptual attribute correlates. The two major pieces of this model are a chromatic adaptation transform and equations for computing correlates of perceptual attributes, such as brightness, lightness, chroma, saturation, Colorfulness and hue. The chromatic adaptation transform takes into account changes in the chromaticity of the adopted white point. In addition, the luminance of the adopted white point can influence the degree to which an observer adapts to that white point. The degree of adaptation or D factor is therefore another aspect of the chromatic adaptation transform. Generally, between the chromatic adaptation transform and computing perceptual attributes correlates there is also a non-linear response compression. The chromatic adaptation transform and D factor was derived based on experimental data from corresponding Colors data sets. The non-linear response compression was derived based on physiological data and other considerations. The perceptual attribute correlates was derived by comparing predictions to magnitude estimation experiments, such as various phases of the LUTCHI data, and other data sets, such as the Munsell Book of Color. Finally the entire structure of the model is generally constrained to be invertible in closed form and to take into account a sub-set of Color Appearance phenomena. Viewing Condition Parameters It is convenient to begin by computing viewing condition dependent constants. First the surround is selected and then values for F, c and Nc can be read from Table 1. For intermediate surrounds these values can be linearly interpolated.2 Table. 1. Viewing condition parameters for different surrounds. Surround F c Nc Average 1.0 0.69 1.0 Dim 0.9 0.59 0.95 Dark 0.8 0.525 0.8 The value of FL can be computed using equations 1 and 2, where LA is the luminance of the adapting field in cd/m2. Note that this two piece formula quickly goes to very small values for mesopic and scotopic levels and while it may resemble a cube-root function there are considerable differences between this two-piece function and a cube-root as the luminance of the adapting field gets very small. ! k =1/ 5L A +1 ( ) (1) ! F L = 0.2k 4 5L A ( ) + 0.1 1" k4 ( ) 2 5L A ( ) 1/ 3 (2) The value n is a function of the luminance factor of the background and provides a very limited model of spatial Color Appearance. The value of n ranges from 0 for a background luminance factor of zero to 1 for a background luminance factor equal to the luminance factor of the adopted white point. The n value can then be used to compute Nbb, Ncb and z, which are then used during the computation of several of the perceptual attribute correlates. These calculations can be performed once for a given viewing condition.
Jan Kautz - One of the best experts on this subject based on the ideXlab platform.
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Modeling human Color perception under extended luminance levels
ACM Transactions on Graphics, 2009Co-Authors: Min H Kim, Tim Weyrich, Jan KautzAbstract:Display technology is advancing quickly with peak luminance increasing significantly, enabling high-dynamic-range displays. However, perceptual Color Appearance under extended luminance levels has not been studied, mainly due to the unavailability of psychophysical data. Therefore, we conduct a psychophysical study in order to acquire Appearance data for many different luminance levels (up to 16,860 cd/m(2)) covering most of the dynamic range of the human visual system. These experimental data allow us to quantify human Color perception under extended luminance levels, yielding a generalized Color Appearance model. Our proposed Appearance model is efficient, accurate and invertible. It can be used to adapt the tone and Color of images to different dynamic ranges for cross-media reproduction while maintaining Appearance that is close to human perception.
Dosik Hwang - One of the best experts on this subject based on the ideXlab platform.
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inverse tone mapping operator using sequential deep neural networks based on the human visual system
IEEE Access, 2018Co-Authors: Hanbyol Jang, Kihun Bang, Jinseong Jang, Dosik HwangAbstract:Conventional digital displays show images with much less dynamic range than that of human visual perception. High-dynamic range (HDR) displays are being developed for viewing images with higher dynamic range than that of conventional low-dynamic range (LDR) images. However, to view existing LDR images on HDR displays, an inverse tone mapping operator (ITMO), which is a process to extend the dynamic range of LDR images, is required. In this paper, we propose an adaptive ITMO by effectively learning the differences between LDR and HDR images using a sequential learning process: dynamic range learning followed by Color difference learning. Our proposed method enables visualization of Colors similar to real-world Colors better than conventional ITMOs by learning Color differences based on the human visual system properties. For the objective comparison, seven different evaluation metrics optimized for HDR image evaluation were used. Our method resulted in 10%–25% improved HDR-visual difference predictor-2.2 values over those of other ITMOs. Other metrics also demonstrated the superior performance of our method. For the subjective comparison, eight human observers evaluated the estimated HDR images in terms of Color Appearance and overall preferences. Our method received an average 4.7 out of 5 score, whereas other ITMOs received below 3.5 scores in the evaluation of Color Appearance. The objective and subjective evaluations’ results showed that our proposed method outperformed the conventional ITMOs in estimating the dynamic range and Color Appearance of ground-truth HDR images.
Dazun Zhao - One of the best experts on this subject based on the ideXlab platform.
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implementing Color transformation across media based on Color Appearance model by neural networks
Electronic imaging and multimedia technology. Conference, 2005Co-Authors: Binghua Chai, Dazun ZhaoAbstract:Interest in Color Appearance models (CAM) has been greatly stimulated recently by the need in handling digital images. This article demonstrates that a multi-layers feed-forward artificial neural network with the error back-propagation algorithm was used to approximate Color Appearance model CIECAM02 with different white points and different media. For the prediction of the forward and inverse model respectively, in order to realize accurate mapping, especially to the inverse model, Color spaces conversion between input Color space and output Color space (that is cylindrical coordinates and rectangular coordinates) was implemented before training the neural networks. Meanwhile we approximated the combination of the forward and inverse CIECAM02 models employing a neural network for different conditions including whites (D65 or D50) and media (booth and CRT) in order to realize the Color transformation from one medium to another conveniently. The experimental results indicated that the prediction could satisfy the accuracy requirement. So in practice we can choose these two kinds of different prediction ways to meet our need according to different situations.
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approximating the ciecam02 Color Appearance model by means of neural networks
Chinese Optics Letters, 2004Co-Authors: Binghua Chai, Ning Fang Liao, Dazun ZhaoAbstract:An artificial neural network used to realize the approximating problem of the Color Appearance model (CAM) CIECAM02 in Color management is demonstrated. GretagMacbeth ColorChecker Charts, which now are widely used in calibration of digital camera, are chosen as samples to implement the forward and reverse Color Appearance models. When the predictive results are evaluated, for forward model, the output Color Appearance space is converted to the uniform Color space based on CAM and is evaluated, while for reverse model, because the prediction precision is insufficient, we try to convert the Color Appearance space, which is the cylinder space, to the cube space similar to the red, green, and blue (RGB) space, and the results show that the precision is obviously improved.
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Method of cross-media Color reproduction based on the Munsell system
Color Science and Imaging Technologies, 2002Co-Authors: Weiping Yang, Junsheng Shi, Dazun Zhao, Bing Peng, Fengxiang BaiAbstract:As new method of characterizing CRT monitors is proposed. The features of this method are, it can take account of some Color Appearance factors, such as the Appearance difference between self-luminous and surface Color, but without the complexity of using any Color Appearance model, and it may improve the performances of an interpolation operation when an arbitrary assigned Color is to be displayed on a CRT screen. The method is introduced more detailedly in Section 2, and preliminary experimental results are given in Section 3.
Alejandro C Parraga - One of the best experts on this subject based on the ideXlab platform.
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saliency estimation using a non parametric low level vision model
Computer Vision and Pattern Recognition, 2011Co-Authors: Naila Murray, Maria Vanrell, Xavier Otazu, Alejandro C ParragaAbstract:Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of Color Appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on Color Appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a Color Appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks.
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saliency estimation using a non parametric low level vision model
Computer Vision and Pattern Recognition, 2011Co-Authors: Naila Murray, Maria Vanrell, Xavier Otazu, Alejandro C ParragaAbstract:Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of Color Appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on Color Appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a Color Appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks.