测量智能手机照片的感知色差
Measuring Perceptual Color Differences of Smartphone Photographs
AI总结：
本文构建了迄今最大的智能手机感知色差评估数据集，并提出基于轻量级神经网络的端到端可学习色差公式，在多项实验中显著优于33种现有方法。
摘要：
测量感知色差（CDs）在现代智能手机摄影中具有重要意义。尽管历史悠久，但大多数CD测量方法一直受限于均匀色块的心理物理学数据或数量有限的简单自然摄影图像。因此，在以更大的内容复杂性和基于学习的图像信号处理器为特征的智能手机摄影时代，现有的CD测量方法是否具有泛化能力是值得怀疑的。在本文中，我们收集了迄今为止用于感知CD评估的最大图像数据集，其中的摄影图像是1）由六款旗舰智能手机拍摄的，2）由Photoshop修改的，3）由智能手机内置滤镜进行后处理的，以及4）使用错误的色彩配置文件进行复现的。然后，我们在受控的实验室环境中进行大规模心理物理学实验，收集了30,000对图像对的感知CD。基于新建立的数据集，我们首次尝试基于轻量级神经网络构建端到端可学习的CD公式，作为对先前几种指标的泛化。大量实验表明，优化后的公式以巨大优势优于33种现有的CD测量方法，在不使用密集监督的情况下提供了合理的局部CD图，对均匀色块数据具有良好的泛化能力，并且在数学意义上经验性地表现为一个合适的度量。我们的数据集和代码已在 https://github.com/hellooks/CDNet 上公开。
英文摘要：
Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography. Despite the long history, most CD measures have been constrained by psychophysical data of homogeneous color patches or a limited number of simplistic natural photographic images. It is thus questionable whether existing CD measures generalize in the age of smartphone photography characterized by greater content complexities and learning-based image signal processors. In this paper, we put together so far the largest image dataset for perceptual CD assessment, in which the photographic images are 1) captured by six flagship smartphones, 2) altered by Photoshop, 3) post-processed by built-in filters of the smartphones, and 4) reproduced with incorrect color profiles. We then conduct a large-scale psychophysical experiment to gather perceptual CDs of 30,000 image pairs in a carefully controlled laboratory environment. Based on the newly established dataset, we make one of the first attempts to construct an end-to-end learnable CD formula based on a lightweight neural network, as a generalization of several previous metrics. Extensive experiments demonstrate that the optimized formula outperforms 33 existing CD measures by a large margin, offers reasonable local CD maps without the use of dense supervision, generalizes well to homogeneous color patch data, and empirically behaves as a proper metric in the mathematical sense. Our dataset and code are publicly available at https://github.com/hellooks/CDNet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
