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Prediction of Incomplete Chromatic Adaptation Under Illuminant A from Images

机译：从图像预测光源A下色度的不完全适应

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摘要

The authors propose a method of image rendering to predict the incomplete chromatic-adaptation effect for paintings. A simple model of incomplete chromatic adaptation is developed to predict the appearance of the paintings under the illumination of an incandescent light source and to produce the full color image on a display device. The authors extend the von Kries framework to incomplete chromatic adaptation. An index parameter representing the degree of incomplete chromatic adaptation is defined based on the color temperature of the black-body radiators. First, the optimum value of the index parameter is determined by visual experiments on memory matching using real paintings and color patches, so that the color image produced on the display is matched to the original appearance of objects in a real scene. This approach is shown to have better performance in comparison with the traditional CIECAM02. Next, an algorithm is presented to estimate the index parameter of the incomplete adaptation index based on the image data of colorimetric rendering for a target painting. It is found that the index parameter can be estimated using only three features extracted from the color image. The color images rendered with the estimated parameter are used to predict the incomplete chromatic-adaptation effect for the original painting under the incandescent light source. The feasibility of the proposed method is confirmed based on a series of experiments using a variety of paintings.
机译：作者提出了一种图像渲染方法来预测绘画的不完全色彩适应效果。开发了不完全的色彩适应的简单模型，以预测在白炽灯光源照射下的绘画外观，并在显示设备上产生全彩色图像。作者将von Kries框架扩展到不完全的色彩适应。基于黑体辐射器的色温来定义表示不完全的色彩适应程度的指标参数。首先，通过视觉实验在使用真实绘画和色块的存储器匹配上确定索引参数的最佳值，以使显示器上产生的彩色图像与真实场景中对象的原始外观匹配。与传统的CIECAM02相比，该方法具有更好的性能。接下来，提出一种算法，用于基于目标绘画的比色渲染的图像数据来估计不完全适应指数的指数参数。发现仅使用从彩色图像提取的三个特征可以估计索引参数。使用估计参数渲染的彩色图像用于预测白炽灯光源下原始绘画的不完全色彩适应效果。基于使用各种绘画进行的一系列实验，证实了该方法的可行性。

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  • 来源
    《Journal of Imaging Science and Technology》 |2014年第3期|030403.1-030403.9|共9页
  • 作者

    Shoji Tominaga; Takahiko Horiuchi; Shiori Nakajima; Mariko Yano;

  • 作者单位

    Graduate School of Advanced Integration Science, Chiba University, Japan;

    Graduate School of Advanced Integration Science, Chiba University, Japan;

    Graduate School of Advanced Integration Science, Chiba University, Japan;

    Graduate School of Advanced Integration Science, Chiba University, Japan;

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