The Experts below are selected from a list of 189 Experts worldwide ranked by ideXlab platform

R W G Hunt - One of the best experts on this subject based on the ideXlab platform.

  • application of a Model of Color Appearance to practical problems in imaging
    Proceedings of the IEEE, 2002
    Co-Authors: R W G Hunt
    Abstract:

    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.

  • the ciecam02 Color Appearance Model
    Color Imaging Conference, 2002
    Co-Authors: Nathan Moroney, Mark D Fairchild, R W G Hunt, Ronnier M Luo, Todd Newman
    Abstract:

    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.

Alejandro C Parraga - One of the best experts on this subject based on the ideXlab platform.

  • saliency estimation using a non parametric low level vision Model
    Computer Vision and Pattern Recognition, 2011
    Co-Authors: Naila Murray, Maria Vanrell, Xavier Otazu, Alejandro C Parraga
    Abstract:

    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.

  • saliency estimation using a non parametric low level vision Model
    Computer Vision and Pattern Recognition, 2011
    Co-Authors: Naila Murray, Maria Vanrell, Xavier Otazu, Alejandro C Parraga
    Abstract:

    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.

Dazun Zhao - One of the best experts on this subject based on the ideXlab platform.

  • implementing Color transformation across media based on Color Appearance Model by neural networks
    Electronic imaging and multimedia technology. Conference, 2005
    Co-Authors: Binghua Chai, Ningfang Liao, Dazun Zhao
    Abstract:

    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.

  • approximating the ciecam02 Color Appearance Model by means of neural networks
    Chinese Optics Letters, 2004
    Co-Authors: Binghua Chai, Ningfang Liao, Dazun Zhao
    Abstract:

    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.

  • Method of cross-media Color reproduction based on the Munsell system
    Color Science and Imaging Technologies, 2002
    Co-Authors: Weiping Yang, Junsheng Shi, Dazun Zhao, Bing Peng, Fengxiang Bai
    Abstract:

    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.

Chih-yuan Yang - One of the best experts on this subject based on the ideXlab platform.

  • Color Reproduction System Based on Color Appearance Model and Gamut Mapping
    Input Output and Imaging Technolgies II, 2000
    Co-Authors: Fang-hsuan Cheng, Chih-yuan Yang
    Abstract:

    By the progress of computer, computer peripherals such as Color monitor and printer are often used to generate Color image. However, cross media Color reproduction by human perception is usually different. Basically, the influence factors are device calibration and characterization, viewing condition, device gamut and human psychology. In this thesis, a Color reproduction system based on Color Appearance Model and gamut mapping is proposed. It consists of four parts; device characterization, Color management technique, Color Appearance Model and gamut mapping.

Yoshinobu Nayatani - One of the best experts on this subject based on the ideXlab platform.

  • an integrated Color Appearance Model using cieluv and its applications
    Color Research and Application, 2008
    Co-Authors: Yoshinobu Nayatani, Hideki Sakai
    Abstract:

    A new type of Color-Appearance Model is presented together with its formulations. It is named In-CAM(CIELUV), which means the integrated Color-Appearance Model using CIELUV space. Using the In-CAM(CIELUV), we can integrate its fields of applications in both Colorimetric engineering and artistic Color design. Various applications are introduced in Colorimetric and Color design fields. The In-CAM(CIELUV) connects directly Colorimetric Color space and perceptual Hue-Tone Color order systems. In other words, the In-CAM (CIELUV) gives a Colorimetric basis for Hue-Tone system. The three Color attributes in the In-CAM(CIELUV) space are mutually independent. This is a very convenient feature for selecting Color combinations. Some two-Color combinations selected systematically in the In-CAM(CIELUV) space are shown. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 125–134, 2008

  • proposal of an abridged Color Appearance Model ciecat94lab and its field trials
    Color Research and Application, 1999
    Co-Authors: Yoshinobu Nayatani, Kenjiro Hashimoto, Tadashi Yano, Hiroaki Sobagaki
    Abstract:

    An abridged Color-Appearance Model named CIECAT94LAB is proposed in the present study. It consists of the CIE 1994 chromatic-adaptation transform with some improvements and the CIELAB formula. CIECAT94LAB is easy in computation and can predict almost all the Color-Appearance phenomena. It was tested using the experiments conducted by CSAJ, Breneman, and McCann et al. In addition, a new measure is proposed for comparing the predictability among various Color-Appearance Models. The measure is independent of the scale in each of the Color-Appearance Models. © 1999 John Wiley & Sons, Inc. Col Res Appl, 24, 422–438, 1999

  • revision of the chroma and hue scales of a nonlinear Color Appearance Model
    Color Research and Application, 1995
    Co-Authors: Yoshinobu Nayatani
    Abstract:

    The following questions were raised to chroma and hue scales of the nonlinear Color-Appearance Model: 1) significant nonuniformities of the chroma scales for different hues, and 2) deviations of hue scale between the Model and the Munsell and the NCS schemes. It was suggested that the problems were caused by the use of the coefficient es(0) proposed by Hunt. Instead of es(0), a new coefficient Es(0) was proposed, which corresponds to the chromatic strengths of spectral Colors (including Colors on the redpurple locus). By using Es(0), the nonlinear Color-Appearance Model could predict the hue and chroma scales of the Munsell and the NCS schemes quite nicely. the method in the present study is generally applicable for determining hue and chroma perceptions of object Colors irrespective of the Color-Appearance Model used.

  • lightness dependency of chroma scales of a nonlinear Color Appearance Model and its latest formulation
    Color Research and Application, 1995
    Co-Authors: Yoshinobu Nayatani, Hiroaki Sobagaki, Kenjiro Hashimoto Tadashi Yano
    Abstract:

    It has been noticed that the lightness dependency of equal chroma loci of the nonlinear Color-Appearance Model already reported deviates markedly from that of equal Chroma loci of the Munsell scheme. to solve this problem, the new chroma CN of the Model is proposed by applying the correction of lightness LA to the chroma C previously used. the chroma C is now used to predict the saturation of the newly revised Model. the introduction of CN improves significantly the agreement of lightness dependency between the Model chroma, the Munsell Chroma, and the NCS chromaticness at various lightness levels. the formulation for the revised nonlinear ColorAppearance Model is given in the Appendix.