Color-Image Quality Assessment: From Prediction to Optimization
Color-Image Quality Assessment: From Prediction to Optimization
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DOI：
10.1109/tip.2014.2302684
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发表时间：
2014-03-01
影响因子：
10.6
通讯作者：
Urban, Philipp
中科院分区：
文献类型：
--
作者：
Preiss, Jens;Fernandes, Felipe;Urban, Philipp
虽然图像差异度量在视觉数据上显示出良好的预测性能，但如果用作优化复杂图像处理任务的目标函数，它们通常会产生伪影污染的结果。我们在这方面研究了最近提出的彩色图像差异（CID）指标，该指标是专门为预测色域映射失真而开发的。我们提出了一种使用 CID 度量作为目标函数来优化色域映射的算法。生成的图像包含各种视觉伪影，这些伪影可通过多次修改来解决，从而产生改进的颜色图像差异 (iCID) 指标。基于 iCID 的优化没有伪影，并且在很大程度上保留了原始图像的对比度、结构和颜色。此外，通过修改提高了视觉数据的预测性能。
While image-difference metrics show good prediction performance on visual data, they often yield artifact-contaminated results if used as objective functions for optimizing complex image-processing tasks. We investigate in this regard the recently proposed color-image-difference (CID) metric particularly developed for predicting gamut-mapping distortions. We present an algorithm for optimizing gamut mapping employing the CID metric as the objective function. Resulting images contain various visual artifacts, which are addressed by multiple modifications yielding the improved color-image-difference (iCID) metric. The iCID-based optimizations are free from artifacts and retain contrast, structure, and color of the original image to a great extent. Furthermore, the prediction performance on visual data is improved by the modifications.