The Experts below are selected from a list of 51 Experts worldwide ranked by ideXlab platform

Klaus Witt - One of the best experts on this subject based on the ideXlab platform.

Guihua Cui - One of the best experts on this subject based on the ideXlab platform.

  • Colour Difference Evaluation using display Colours
    Lighting Research & Technology, 2017
    Co-Authors: J Liang, Guihua Cui, M Georgoula, N Zou, M. Ronnier Luo
    Abstract:

    Two separate but similar experiments were carried out at Leeds University (UK) and Zhejiang University (China), respectively. Both experiments were conducted to assess Colour Differences using Eizo...

  • Colour Difference Evaluation for white light sources
    Lighting Research & Technology, 2014
    Co-Authors: Luo, Guihua Cui, M Georgoula
    Abstract:

    An experiment was carried out to study Colour discrimination on a display to simulate six different white light sources. Each centre includes 20 pairs of illuminant mode Colours, which were assesse...

  • Colour Difference Evaluation
    Advanced Color Image Processing and Analysis, 2012
    Co-Authors: Manuel Melgosa, Alain Trémeau, Guihua Cui
    Abstract:

    For a pair of homogeneous Colour samples or two complex images viewed under specific conditions, Colour-Difference formulas try to predict the visually perceived (subjective) Colour Difference starting from instrumental (objective) Colour measurements. The history related to the five up-to-date CIE-recommended Colour-Difference formulas is reviewed, with special emphasis on the structure and performance of the last one, CIEDE2000. Advanced Colour-Difference formulas with an associated Colour space (e.g., DIN99d, CAM02, Euclidean OSA-UCS, etc.) are also discussed. Different indices proposed to measure the performance of a given Colour-Difference formula (e.g., PF/3, STRESS, etc.) are reviewed. Among current trends on Colour-Difference Evaluation, it can be mentioned the research activities carried out by different CIE Technical Committees (e.g., CIE TC’s 1-55, 1-57, 1-63, 1-81 and 8-02), the need of new reliable experimental datasets, the development of Colour-Difference formulas based on IPT and Colour-appearance models, and the concept of “total Differences,” which considers the interactions between Colour properties and other object attributes like texture, translucency, and gloss.

  • Colour Difference Evaluation using crt Colours part i data gathering and testing Colour Difference formulae
    Color Research and Application, 2001
    Co-Authors: Guihua Cui, Ronnier M Luo, B. Rigg
    Abstract:

    A large set of Colour discrimination data based upon Cathode Ray Tube (CRT) Colours was accumulated. The experiment was carried out to study the effect of changes in viewing parameters on perceived Colour Differences. It was divided into sixteen phases. Each phase was conducted under a different set of experimental conditions including different sample sizes, backgrounds, frames, widths, and Colours of separation between pairs of samples. For each phase, 134 pairs of Colours with average 2.7 CIELAB Colour Difference were studied. The Colours closely corresponded to the five Colour centers proposed by the CIE. Each pair was assessed by 10 or 20 observers using the gray scale method. The visual results from the present study were also used to test six Colour-Difference formulae: CIELAB, CMC, BFD, CIE94, LCD, and CIEDE2000. The latter outperformed the other formulae and is the new CIE Colour-Difference equation. The current results were also compared with the other datasets based upon surface Colours. The present results are consistent with those obtained for surface Colours. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 394–402, 2001

  • Colour-Difference Evaluation Using CRT Colours. Part II: Parametric Effects
    Color Research & Application, 2001
    Co-Authors: Guihua Cui, M. Ronnier Luo, B. Rigg, Li Wei
    Abstract:

    A large set of Colour discrimination data based upon cathode ray tube (CRT) Colours was accumulated and described in Part I of this article. The experiment was divided into 16 different phases according to different viewing parameters studied: sample size (large and small), background (mid-gray, white, black, gray, red, yellow, green, and blue), frame (black and no frame), and width (no gap, 1-pixel, 2-pixel, and large gap) and Colour (mid-gray and black) of separations between pairs of samples. This part of the article investigates the changes of perceived Colour Differences caused by these variables. In addition, investigations were made to find the results from which phase agree best with results for surface Colours. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 403–412, 2001

Li Wei - One of the best experts on this subject based on the ideXlab platform.

  • An Optimized Uniform Colour Appearance Space
    Journal of Beijing Institute of Technology, 2003
    Co-Authors: Li Wei
    Abstract:

    An optimized uniform Colour appearance space named UCAS is deduced based on the revised edition of Colour appearance model CIECAM97s according to BFD, RIT DuPont, Witt1999 and Leeds Colour Difference Evaluation data sets. Tested by a synthetically statistical index P ′ F/3 with the introduction of an optimized lightness correcting factor, the results show that the uniformity of UCAS is close to the new German standard DIN99 issued in 1999 and better than CIELAB recommended by CIE in 1976.

  • Colour-Difference Evaluation Using CRT Colours. Part II: Parametric Effects
    Color Research & Application, 2001
    Co-Authors: Guihua Cui, M. Ronnier Luo, B. Rigg, Li Wei
    Abstract:

    A large set of Colour discrimination data based upon cathode ray tube (CRT) Colours was accumulated and described in Part I of this article. The experiment was divided into 16 different phases according to different viewing parameters studied: sample size (large and small), background (mid-gray, white, black, gray, red, yellow, green, and blue), frame (black and no frame), and width (no gap, 1-pixel, 2-pixel, and large gap) and Colour (mid-gray and black) of separations between pairs of samples. This part of the article investigates the changes of perceived Colour Differences caused by these variables. In addition, investigations were made to find the results from which phase agree best with results for surface Colours. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 403–412, 2001

  • Colour‐Difference Evaluation using CRT Colours. Part I: Data gathering and testing Colour Difference formulae
    Color Research & Application, 2001
    Co-Authors: Guihua Cui, M. Ronnier Luo, B. Rigg, Li Wei
    Abstract:

    A large set of Colour discrimination data based upon Cathode Ray Tube (CRT) Colours was accumulated. The experiment was carried out to study the effect of changes in viewing parameters on perceived Colour Differences. It was divided into sixteen phases. Each phase was conducted under a different set of experimental conditions including different sample sizes, backgrounds, frames, widths, and Colours of separation between pairs of samples. For each phase, 134 pairs of Colours with average 2.7 CIELAB Colour Difference were studied. The Colours closely corresponded to the five Colour centers proposed by the CIE. Each pair was assessed by 10 or 20 observers using the gray scale method. The visual results from the present study were also used to test six Colour-Difference formulae: CIELAB, CMC, BFD, CIE94, LCD, and CIEDE2000. The latter outperformed the other formulae and is the new CIE Colour-Difference equation. The current results were also compared with the other datasets based upon surface Colours. The present results are consistent with those obtained for surface Colours. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 394–402, 2001

Ming Ronnier Luo - One of the best experts on this subject based on the ideXlab platform.

  • CIECAM02 and Its Recent Developments
    Advanced Color Image Processing and Analysis, 2012
    Co-Authors: Ming Ronnier Luo
    Abstract:

    The development of colorimetry can be divided into three stages: Colour specification, Colour Difference Evaluation and Colour appearance modelling. Stage 1 considers the communication of Colour information by numbers. The second stage is Colour Difference Evaluation. While the CIE system has been successfully applied for over 80 years, it can only be used under quite limited viewing conditions, e.g., daylight illuminant, high luminance level, and some standardised viewing/illuminating geometries. However, with recent demands on crossmedia Colour reproduction, e.g., to match the appearance of a Colour or an image on a display to that on hard copy paper, conventional colorimetry is becoming insufficient. It requires a Colour appearance model capable of predicting Colour appearance across a wide range of viewing conditions so that Colour appearance modelling becomes the third stage of colorimetry. Some call this as advanced colorimetry. This chapter will focused on the recent developments based on CIECAM02.

  • Applying Colour science in Colour design
    Optics and Laser Technology, 2005
    Co-Authors: Ming Ronnier Luo
    Abstract:

    Although Colour science has been widely used in a variety of industries over the years, it has not been fully explored in the field of product design. This paper will initially introduce the three main application fields of Colour science: Colour specification, Colour-Difference Evaluation and Colour appearance modelling. By integrating these advanced Colour technologies together with modern Colour imaging devices such as display, camera, scanner and printer, some computer systems have been recently developed to assist designers for designing Colour palettes through Colour selection by means of a number of widely used Colour order systems, for creating harmonised Colour schemes via a categorical Colour system, for generating emotion Colours using various Colour emotional scales and for facilitating Colour naming via a Colour-name library. All systems are also capable of providing accurate Colour representation on displays and output to different imaging devices such as printers.

  • CIE Division 8: a servant for the imaging industry
    Color Science and Imaging Technologies, 2002
    Co-Authors: Ming Ronnier Luo
    Abstract:

    With the strong demand from the imaging industry, the CIE Division 8 Image Technology was established in November 1997. This young and dynamic division is aimed to study procedures and prepare guides and standards for the optical, visual and metrological aspects of the communication, processing, and reproduction of images, using all types of analogue and digital devices, storage media and imaging media. It is a servant for the imaging industry to achieve successful Colour practice using the knowledge of Colour science and Colour engineering. There are six CIE Division 8 Technical Committees (TC): TC 8-01 Colour Appearance Modeling for Colour Management Applications, TC 8-02 Colour Difference Evaluation in Images, TC 8-03 Gamut Mapping, TC 8-04 Adaptation under Mixed Illumination Conditions, TC 8-05 Communication of Colour Information, and TC 8-06 Image Technology Vocabulary. This paper will introduce the aims and activities in each TC.

  • CIE 2000 color Difference formula: CIEDE2000
    9th Congress of the International Colour Association, 2002
    Co-Authors: Ming Ronnier Luo
    Abstract:

    The CIE Technical Committee TC 1-47 Hue and Lightness Dependent Correction to Industrial Colour Difference Evaluation was established in October 1998 and its aim was to improve the performance of the CIE94 color-Difference formula. As a result of close collaboration between the TC members, the CIE 2000 color Difference formula, CIEDE2000, was developed within two years. This paper describes the development of this formula.

M. Ronnier Luo - One of the best experts on this subject based on the ideXlab platform.

  • Colour Difference Evaluation using display Colours
    Lighting Research & Technology, 2017
    Co-Authors: J Liang, Guihua Cui, M Georgoula, N Zou, M. Ronnier Luo
    Abstract:

    Two separate but similar experiments were carried out at Leeds University (UK) and Zhejiang University (China), respectively. Both experiments were conducted to assess Colour Differences using Eizo...

  • Colour-Difference Evaluation Using CRT Colours. Part II: Parametric Effects
    Color Research & Application, 2001
    Co-Authors: Guihua Cui, M. Ronnier Luo, B. Rigg, Li Wei
    Abstract:

    A large set of Colour discrimination data based upon cathode ray tube (CRT) Colours was accumulated and described in Part I of this article. The experiment was divided into 16 different phases according to different viewing parameters studied: sample size (large and small), background (mid-gray, white, black, gray, red, yellow, green, and blue), frame (black and no frame), and width (no gap, 1-pixel, 2-pixel, and large gap) and Colour (mid-gray and black) of separations between pairs of samples. This part of the article investigates the changes of perceived Colour Differences caused by these variables. In addition, investigations were made to find the results from which phase agree best with results for surface Colours. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 403–412, 2001

  • Colour‐Difference Evaluation using CRT Colours. Part I: Data gathering and testing Colour Difference formulae
    Color Research & Application, 2001
    Co-Authors: Guihua Cui, M. Ronnier Luo, B. Rigg, Li Wei
    Abstract:

    A large set of Colour discrimination data based upon Cathode Ray Tube (CRT) Colours was accumulated. The experiment was carried out to study the effect of changes in viewing parameters on perceived Colour Differences. It was divided into sixteen phases. Each phase was conducted under a different set of experimental conditions including different sample sizes, backgrounds, frames, widths, and Colours of separation between pairs of samples. For each phase, 134 pairs of Colours with average 2.7 CIELAB Colour Difference were studied. The Colours closely corresponded to the five Colour centers proposed by the CIE. Each pair was assessed by 10 or 20 observers using the gray scale method. The visual results from the present study were also used to test six Colour-Difference formulae: CIELAB, CMC, BFD, CIE94, LCD, and CIEDE2000. The latter outperformed the other formulae and is the new CIE Colour-Difference equation. The current results were also compared with the other datasets based upon surface Colours. The present results are consistent with those obtained for surface Colours. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 394–402, 2001

  • The LLAB (l : c) Colour Model
    Color Research & Application, 1996
    Co-Authors: M. Ronnier Luo, Wen-guey Kuo
    Abstract:

    A new Colour model, named LLAB(l:c) is derived. It includes two parts: the BFD chromatic adaptation transform derived by Lam and Rigg, and a modified CIELAB uniform Colour space. The model's performance was compared with the other spaces and models using the LUTCHI Colour Appearance Data Set. The results show that LLAB(l:c) model is capable of precisely quantifying the change of Colour appearance under a wide range of viewing parameters such as light sources, surrounds/media, achromatic backgrounds, sizes of stimuli, and luminance levels. It had a similar performance as that of the Hunt Colour appearance model. The LLAB(l:c) model was also tested using various Colour Difference datasets. The model gave a similar performance as the state-of-the-art Colour Difference formulae such as CMC, CIE94, and BFD. This performance is considered to be very satisfactory, and the model, therefore, should be considered for field trials in applications such as Colour specification, Colour Difference Evaluation, cross-image reproduction, gamut mapping, prediction of metamerism and Colour constancy, and quantification of Colour-rendering properties. The model does not give predictions for chroma (as distinct from Colourfulness), or for brightness, and it does not include any rod response. © 1996 John Wiley & Sons, Inc.