The Experts below are selected from a list of 1326 Experts worldwide ranked by ideXlab platform
Dongil Han - One of the best experts on this subject based on the ideXlab platform.
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ICNC (2) - A real time color Gamut Mapping method using a neural network
Lecture Notes in Computer Science, 2005Co-Authors: Hak-sung Lee, Dongil HanAbstract:In this paper, a neural network is applied to process the color Gamut Mapping in real time. Firstly, a neural network is trained to learn the highly nonlinear input and output relationship of the color Gamut Mapping. And then, the trained neural network is simplified with a look-up table and an address decoder for a fast computation. The proposed method can be easily implemented in high speed and low cost hardware. Simulation result shows the soundness of the proposed method.
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A cost effective color Gamut Mapping architecture for digital TV color reproduction enhancement
IEEE Transactions on Consumer Electronics, 2005Co-Authors: Dongil HanAbstract:A cost effective three-dimensional color Gamut Mapping architecture is described. The conventional three-dimensional reduced resolution look-up table is considered and the concept of three-dimensional reduced resolution difference look-up table is introduced for cost effective and real-time color Gamut Mapping. The overall architecture uses one-dimensional memory decomposition of three-dimensional Gamut Mapping look-up table and three-dimensional interpolation and simple adding operation for generating the final Gamut mapped colors. The required computational cost is greatly reduced by look-up table resolution adjustment and further reduced by the Gamut Mapping rule modification. The proposed architecture greatly reduces the required memory size and hardware complexity compared to the conventional methods and it is suitable for real-time applications. The proposed hardware architecture is suitable for FPGA and ASIC implementation and could be applied to the real-time display quality enhancement purposes.
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Real-time color Gamut Mapping method for digital TV display quality enhancement
IEEE Transactions on Consumer Electronics, 2004Co-Authors: Dongil HanAbstract:A novel real-time color Gamut Mapping method is described. The color Gamut Mapping method that is used for enhancing the color reproduction quality between PC monitor ad printer devices is adopted for digital TV display quality enhancement. The high definition digital TV display devices operate at the clock speed of around 70 MH to 150 MHz and permit several nano seconds for real-time color Gamut Mapping. Thus, the concept of three-dimensional reduced resolution look-up table is introduced for real-time processing. The required hardware can be greatly reduced by look-up table resolution adjustment. The proposed hardware architecture is successfully implemented in FPGA and ASIC and also successfully adopted in digital TV display quality enhancement purposes.
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APCHI - Real-time color Gamut Mapping architecture and implementation for color-blind people
Lecture Notes in Computer Science, 2004Co-Authors: Dongil HanAbstract:A novel color Gamut Mapping method and architecture is described. The color Gamut Mapping allows versatile color display devices to generate transformed colors so that certain colors which are confused can be recognized by the color-blind users. And real-time hardware architecture for color Gamut Mapping is also described. The concept of three-dimensional reduced resolution look-up table is proposed and applied for color Gamut Mapping. The proposed architecture greatly reduces the required memory size and computational loads compared to the conventional methods and it is suitable for real-time applications. The proposed real-time architecture can easily be implemented in high-speed color display applications especially for color-blind users. The experimental results show that the proposed method is successfully used for color transform, which enables confused colors to be differentiated.
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Implementation of real time color Gamut Mapping using neural network
Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications 2005. SMCia 05., 1Co-Authors: Hak-sung Lee, Dongil HanAbstract:A color Gamut Mapping is a process of Mapping colors from the Gamut of a source medium to fit the Gamut of the reproduction medium. The input and output relationship of the color Gamut Mapping is highly nonlinear and there is a need to process the color Gamut Mapping in real-time. In this paper, a neural network is applied to the real time color Gamut Mapping. By the learning ability, the neural network is trained to effectively handle the high nonlinearity of the color Gamut Mapping. And also real time hardware architecture of neural network is presented in this paper. Simulation result shows the soundness of the proposed method.
Ivar Farup - One of the best experts on this subject based on the ideXlab platform.
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spatial colour Gamut Mapping by orthogonal projection of gradients onto constant hue lines
International Symposium on Visual Computing, 2012Co-Authors: Ali Alsam, Ivar FarupAbstract:We present a computationally efficient, artifact-free, spatial Gamut Mapping algorithm. The proposed algorithm offers a compromise between the colorimetrically optimal Gamut clipping and an ideal spatial Gamut Mapping. This is achieved by the iterative nature of the method: At iteration level zero, the result is identical to Gamut clipping. The more we iterate the more we approach an optimal spatial Gamut Mapping result. Our results show that a low number of iterations, 20-30, is sufficient to produce an output that is as good or better than that achieved in previous, computationally more expensive, methods. More importantly, we introduce a new method to calculate the gradients of a vector valued image by means of a projection operator which guarantees that the hue of the Gamut mapped colour vector is identical to the original. Furthermore, the algorithm results in no visible halos in the Gamut mapped image a problem which is common in previous spatial methods. Finally, the proposed algorithm is fast- Computational complexity is O(N), N being the number of pixels. Results based on a challenging small destination Gamut supports our claims that it is indeed efficient.
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CCIW - Spatial colour Gamut Mapping by means of anisotropic diffusion
Lecture Notes in Computer Science, 2011Co-Authors: Ali Alsam, Ivar FarupAbstract:We present a computationally efficient, artifact-free, spatial colour Gamut Mapping algorithm. The proposed algorithm offers a compromise between the colorimetrically optimal Gamut clipping and an ideal spatial Gamut Mapping. It exploits anisotropic diffusion to reduce the introduction of halos often appearing in spatially Gamut mapped images. It is implemented as an iterative method. At iteration level zero, the result is identical to Gamut clipping. The more we iterate the more we approach an optimal, spatial Gamut Mapping result. Our results show that a low number of iterations, 10-20, is sufficient to produce an output that is as good or better than that achieved in previous, computationally more expensive, methods. The computational complexity for one iteration is O(N), N being the number of pixels. Results based on a challenging small destination Gamut supports our claims that it is indeed efficient.
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SCIA - Colour Gamut Mapping as a Constrained Variational Problem
Image Analysis, 2009Co-Authors: Ali Alsam, Ivar FarupAbstract:We present a novel, computationally efficient, iterative, spatial Gamut Mapping algorithm. The proposed algorithm offers a compromise between the colorimetrically optimal Gamut clipping and the most successful spatial methods. This is achieved by the iterative nature of the method. At iteration level zero, the result is identical to Gamut clipping. The more we iterate the more we approach an optimal, spatial, Gamut Mapping result. Optimal is defined as a Gamut Mapping algorithm that preserves the hue of the image colours as well as the spatial ratios at all scales. Our results show that as few as five iterations are sufficient to produce an output that is as good or better than that achieved in previous, computationally more expensive, methods. Being able to improve upon previous results using such low number of iterations allows us to state that the proposed algorithm is O (N ), N being the number of pixels. Results based on a challenging small destination Gamut supports our claims that it is indeed efficient.
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A Multiscale Framework for Spatial Gamut Mapping
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2007Co-Authors: Ivar Farup, Carlo Gatta, Alessandro RizziAbstract:Image reproduction devices, such as displays or printers, can reproduce only a limited set of colors, denoted the color Gamut. The Gamut depends on both theoretical and technical limitations. Reproduction device Gamuts are significantly different from acquisition device Gamuts. These facts raise the problem of reproducing similar color images across different devices. This is well known as the Gamut Mapping problem. Gamut Mapping algorithms have been developed mainly using colorimetric pixel-wise principles, without considering the spatial properties of the image. The recently proposed multilevel Gamut Mapping approach takes spatial properties into account and has been demonstrated to outperform spatially invariant approaches. However, they have some important drawbacks. To analyze these drawbacks, we build a common framework that encompasses at least two important previous multilevel Gamut Mapping algorithms. Then, when the causes of the drawbacks are understood, we solve the typical problem of possible hue shifts. Next, we design appropriate operators and functions to strongly reduce both haloing and possible undesired over compression. We use challenging synthetic images, as well as real photographs, to practically show that the improvements give the expected results.
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multispectral Gamut Mapping and visualization a first attempt
Color Imaging Conference, 2005Co-Authors: Arne Magnus Bakke, Ivar Farup, Jon Yngve HardebergAbstract:A method is proposed for performing spectral Gamut Mapping, whereby spectral images can be altered to fit within an approximation of the spectral Gamut of an output device. Principal component analysis (PCA) is performed on the spectral data, in order to reduce the dimensionality of the space in which the method is applied. The convex hull of the spectral device measurements in this space is computed, and the intersection between the Gamut surface and a line from the center of the Gamut towards the position of a given spectral reflectance curve is found. By moving the spectra that are outside the spectral Gamut towards the center until the Gamut is encountered, a spectral Gamut Mapping algorithm is defined. The spectral Gamut is visualized by approximating the intersection of the Gamut and a 2-dimensional plane. The resulting outline is shown along with the center of the Gamut and the position of a spectral reflectance curve. The spectral Gamut Mapping algorithm is applied to spectral data from the Macbeth Color Checker and test images, and initial results show that the amount of clipping increases with the number of dimensions used.
Ali Alsam - One of the best experts on this subject based on the ideXlab platform.
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spatial colour Gamut Mapping by orthogonal projection of gradients onto constant hue lines
International Symposium on Visual Computing, 2012Co-Authors: Ali Alsam, Ivar FarupAbstract:We present a computationally efficient, artifact-free, spatial Gamut Mapping algorithm. The proposed algorithm offers a compromise between the colorimetrically optimal Gamut clipping and an ideal spatial Gamut Mapping. This is achieved by the iterative nature of the method: At iteration level zero, the result is identical to Gamut clipping. The more we iterate the more we approach an optimal spatial Gamut Mapping result. Our results show that a low number of iterations, 20-30, is sufficient to produce an output that is as good or better than that achieved in previous, computationally more expensive, methods. More importantly, we introduce a new method to calculate the gradients of a vector valued image by means of a projection operator which guarantees that the hue of the Gamut mapped colour vector is identical to the original. Furthermore, the algorithm results in no visible halos in the Gamut mapped image a problem which is common in previous spatial methods. Finally, the proposed algorithm is fast- Computational complexity is O(N), N being the number of pixels. Results based on a challenging small destination Gamut supports our claims that it is indeed efficient.
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CCIW - Spatial colour Gamut Mapping by means of anisotropic diffusion
Lecture Notes in Computer Science, 2011Co-Authors: Ali Alsam, Ivar FarupAbstract:We present a computationally efficient, artifact-free, spatial colour Gamut Mapping algorithm. The proposed algorithm offers a compromise between the colorimetrically optimal Gamut clipping and an ideal spatial Gamut Mapping. It exploits anisotropic diffusion to reduce the introduction of halos often appearing in spatially Gamut mapped images. It is implemented as an iterative method. At iteration level zero, the result is identical to Gamut clipping. The more we iterate the more we approach an optimal, spatial Gamut Mapping result. Our results show that a low number of iterations, 10-20, is sufficient to produce an output that is as good or better than that achieved in previous, computationally more expensive, methods. The computational complexity for one iteration is O(N), N being the number of pixels. Results based on a challenging small destination Gamut supports our claims that it is indeed efficient.
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SCIA - Colour Gamut Mapping as a Constrained Variational Problem
Image Analysis, 2009Co-Authors: Ali Alsam, Ivar FarupAbstract:We present a novel, computationally efficient, iterative, spatial Gamut Mapping algorithm. The proposed algorithm offers a compromise between the colorimetrically optimal Gamut clipping and the most successful spatial methods. This is achieved by the iterative nature of the method. At iteration level zero, the result is identical to Gamut clipping. The more we iterate the more we approach an optimal, spatial, Gamut Mapping result. Optimal is defined as a Gamut Mapping algorithm that preserves the hue of the image colours as well as the spatial ratios at all scales. Our results show that as few as five iterations are sufficient to produce an output that is as good or better than that achieved in previous, computationally more expensive, methods. Being able to improve upon previous results using such low number of iterations allows us to state that the proposed algorithm is O (N ), N being the number of pixels. Results based on a challenging small destination Gamut supports our claims that it is indeed efficient.
Jan Morovic - One of the best experts on this subject based on the ideXlab platform.
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CIE Guidelines for Evaluation of Gamut Mapping Algorithms: Summary and Related Work (Pub. 156)
2015Co-Authors: Jan MorovicAbstract:The CIE Guidelines for the Evaluation of Gamut Mapping Algorithms (referred to as Guidelines in the remainder of this entry) set out experimental conditions under which color Gamut Mapping algorithms are to be evaluated so that results can be compared and combined from separate experiments. TheGuidelines were published [1] in 2004 by Division 8 of the CIE and cover a number of aspects of experimental evaluation, both mandatory and optional. They also include case studies for applying them to various color reproduction scenarios and a checklist that can be used to determine an experiment’s compliance with the Guidelines.
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a multi resolution full colour spatial Gamut Mapping algorithm
Color Imaging Conference, 2003Co-Authors: Jan Morovic, Yu WangAbstract:A multi–resolution, full–colour spatial Gamut Mapping algorithm (GMA) is proposed in this paper. Its aim is to maintain as much of an original image’s overall, and in particular spatial, information as possible within the limits of a reproduction medium’s Gamut. First, the original image is decomposed into different spatial frequency bands. Second, lightness compression and initial Gamut Mapping are applied to the lowest frequency band image. Third, the next higher frequency band is added to the Gamut mapped image and the result is processed by subsequent Gamut Mapping transformations. The third step is repeated until the highest frequency band is reached. The effect of this algorithm is that intra–image differences in the original image are well maintained in the Gamut mapped reproduction. A psychophysical experiment is then described whose results show that this algorithm is in the pair of most accurate GMAs and can outperform all other algorithms tested here for images which are less accurately reproduced by all GMAs.
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Color Imaging Conference - A Multi-Resolution, Full-Colour Spatial Gamut Mapping Algorithm.
2003Co-Authors: Jan Morovic, Yu WangAbstract:A multi–resolution, full–colour spatial Gamut Mapping algorithm (GMA) is proposed in this paper. Its aim is to maintain as much of an original image’s overall, and in particular spatial, information as possible within the limits of a reproduction medium’s Gamut. First, the original image is decomposed into different spatial frequency bands. Second, lightness compression and initial Gamut Mapping are applied to the lowest frequency band image. Third, the next higher frequency band is added to the Gamut mapped image and the result is processed by subsequent Gamut Mapping transformations. The third step is repeated until the highest frequency band is reached. The effect of this algorithm is that intra–image differences in the original image are well maintained in the Gamut mapped reproduction. A psychophysical experiment is then described whose results show that this algorithm is in the pair of most accurate GMAs and can outperform all other algorithms tested here for images which are less accurately reproduced by all GMAs.
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evaluating Gamut Mapping algorithms for universal applicability
Color Research and Application, 2001Co-Authors: Jan Morovic, Ronnier M LuoAbstract:The aim of this article is to present the evaluation of Gamut Mapping algorithms (GMAs) in a series of three experiments intended to serve as the basis for developing solutions that are accurate and universally applicable. An evolutionary Gamut Mapping development strategy is used, in which five test images are reproduced between a CRT and printed media obtained using different GMAs. Initially, a number of previously published algorithms were chosen and psychophysically evaluated, whereby an important characteristic of this evaluation was the separate evaluation for individual colour regions within test images. New algorithms were then developed on this experimental basis, subsequently evaluated, and the process was repeated once more. In this series of experiments, the new GCUSP algorithm, which consists of a chroma-dependent lightness compression followed by a compression towards the lightness of the reproduction cusp on the lightness axis, gave the most accurate and stable performance overall. The results of these experiments were also useful for improving the understanding of some Gamut Mapping factors—in particular Gamut difference between media. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 85–102, 2001
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The fundamentals of Gamut Mapping : A survey
Journal of Imaging Science and Technology, 2001Co-Authors: Jan Morovic, M R LuoAbstract:This article aims to give a survey of the fundamentals of Gamut Mapping by describing the cross-media color reproduction context in which it occurs, by giving definitions of terms used in conjunction with it, by describing its aims and by giving an overview of parameters that influence it. These parameters are primarily the choice of color space used, the category into which a Gamut Mapping algorithm belongs and whether the approach is image or medium dependent. A succinct summary is then given of the principal trends in Gamut Mapping studies conducted to date.
Hak-sung Lee - One of the best experts on this subject based on the ideXlab platform.
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ICNC (2) - A real time color Gamut Mapping method using a neural network
Lecture Notes in Computer Science, 2005Co-Authors: Hak-sung Lee, Dongil HanAbstract:In this paper, a neural network is applied to process the color Gamut Mapping in real time. Firstly, a neural network is trained to learn the highly nonlinear input and output relationship of the color Gamut Mapping. And then, the trained neural network is simplified with a look-up table and an address decoder for a fast computation. The proposed method can be easily implemented in high speed and low cost hardware. Simulation result shows the soundness of the proposed method.
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Implementation of real time color Gamut Mapping using neural network
Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications 2005. SMCia 05., 1Co-Authors: Hak-sung Lee, Dongil HanAbstract:A color Gamut Mapping is a process of Mapping colors from the Gamut of a source medium to fit the Gamut of the reproduction medium. The input and output relationship of the color Gamut Mapping is highly nonlinear and there is a need to process the color Gamut Mapping in real-time. In this paper, a neural network is applied to the real time color Gamut Mapping. By the learning ability, the neural network is trained to effectively handle the high nonlinearity of the color Gamut Mapping. And also real time hardware architecture of neural network is presented in this paper. Simulation result shows the soundness of the proposed method.
