Content-Aware Automated Parameter Tuning for Approximate Color Transforms
Content-Aware Automated Parameter Tuning for Approximate Color Transforms
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DOI：
10.1145/3406324.3410713
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Chatura Samarakoon;G. Amaratunga;Phillip Stanley-Marbell
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文献类型：
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作者：
Chatura Samarakoon;G. Amaratunga;Phillip Stanley-Marbell
文献中报道了许多近似颜色变换，其目的是通过不知不觉地改变显示图像的颜色内容来降低显示功耗。为了实用，这些技术需要在选择转换参数时具有内容感知能力，以保持感知质量。本文提出了一种计算效率高的方法，用于计算基于待转换内容的近似颜色变换参数的参数下界。我们对62名参与者进行了一项用户研究，并对6400对图像进行了比较，以得出所提出的解决方案。我们使用用户研究结果，通过使用简单的基于图像颜色的启发式方法，以1.6%的均方误差可靠地预测这个下界。我们表明，这些启发式的Pearson和Spearman秩相关系数大于0.7 (p<0.01)，并且我们的模型可以推广到用户研究之外的数据。用户研究结果还表明，颜色变换能够节省高达50%的电力，大多数用户报告的视觉障碍可以忽略不计。
There are numerous approximate color transforms reported in the literature that aim to reduce display power consumption by imperceptibly changing the color content of displayed images. To be practical, these techniques need to be content-aware in picking transformation parameters to preserve perceptual quality. This work presents a computationally-efficient method for calculating a parameter lower bound for approximate color transform parameters based on the content to be transformed. We conduct a user study with 62 participants and 6,400 image pair comparisons to derive the proposed solution. We use the user study results to predict this lower bound reliably with a 1.6% mean squared error by using simple image-color-based heuristics. We show that these heuristics have Pearson and Spearman rank correlation coefficients greater than 0.7 (p<0.01) and that our model generalizes beyond the data from the user study. The user study results also show that the color transform is able to achieve up to 50% power saving with most users reporting negligible visual impairment.