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Comparison of Colour Difference Methods for Natural Images

机译:自然图像色差方法的比较

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摘要

Perceptual colour difference in simple colour patches has been extensively studied in the history of colour science. However, these methods are not assumed to be applicable for predicting the perceived colour difference in complex colour patches such as digital images of complex scene. In this work existing metrics that predict the perceived colour difference in digital images of complex scene are studied and compared. Performance evaluation was based on the correlations between values of the metrics and results of subjective tests that were done as a pair comparison, in which fifteen test participants evaluated the subjective colour differences in digital images. The test image set consisted of eight images each having four versions of distortion generated by applying different ICC profiles. According to results, none of the metrics were able to predict the perceived colour difference in every test image. The results of iCAM metric had the highest average correlation for all images. However, the scatter of the judgements was very high for two of the images, and if these were excluded from the comparison the Hue-angle was the best performing metric. It was also noteworthy that the performance of the CIELAB colour difference metric was relatively high.
机译:在颜色科学的历史中,已经对简单色块中的感知色差进行了广泛研究。然而,这些方法不被认为适用于预测诸如复杂场景的数字图像之类的复杂色块中的感知色差。在这项工作中,研究和比较了预测复杂场景数字图像中感知到的色差的现有度量。性能评估基于度量值与主观测试结果之间的相关性,这是通过成对比较完成的,其中有十五名测试参与者评估了数字图像中的主观色差。测试图像集由八个图像组成,每个图像具有通过应用不同的ICC配置文件生成的四个版本的失真。根据结果​​,没有一个指标能够预测每个测试图像中感知到的色差。 iCAM度量的结果对所有图像均具有最高的平均相关性。但是,对于其中的两个图像,判断的分散性非常高,如果将这些图像排除在比较之外,则色相角度是性能最好的指标。还值得注意的是,CIELAB色差度量标准的性能相对较高。

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