作为感知色差度量的多尺度切片Wasserstein距离
Multiscale Sliced Wasserstein Distances as Perceptual Color Difference Measures
AI总结：
针对现有色差度量难以处理错位图像对的问题，本文提出基于多尺度切片Wasserstein距离的无需训练感知色差度量，在错位场景下表现更优，且可作为颜色迁移损失函数。
摘要：
当前用于摄影图像的色差（CD）度量通常通过在“感知均匀”颜色空间中比较同位像素、图像块，或在学习到的潜在空间中比较特征来实现。因此，这些度量无法充分捕捉人类对错位图像对的颜色感知，而错位图像对在数字摄影中十分常见（例如不同智能手机拍摄的同一场景）。本文提出了一种基于多尺度切片Wasserstein距离的感知色差度量方法，该方法可高效比较具有相似颜色和结构的非局部图像块。这与现代颜色感知认知相符，即颜色和结构作为感知组织的统一过程密不可分。同时，该方法易于实现且无需训练。实验结果表明，本文的色差度量在评估摄影图像色差方面表现优异，且在图像存在错位的情况下始终优于对比模型。此外，本文通过实验验证了该度量在数学意义上满足度量性质，并展示了其作为图像和视频颜色迁移任务损失函数的应用潜力。代码可在https://github.com/real-hjq/MS-SWD获取。
英文摘要：
Contemporary color difference (CD) measures for photographic images typically operate by comparing co-located pixels, patches in a ``perceptually uniform'' color space, or features in a learned latent space. Consequently, these measures inadequately capture the human color perception of misaligned image pairs, which are prevalent in digital photography (e.g., the same scene captured by different smartphones). In this paper, we describe a perceptual CD measure based on the multiscale sliced Wasserstein distance, which facilitates efficient comparisons between non-local patches of similar color and structure. This aligns with the modern understanding of color perception, where color and structure are inextricably interdependent as a unitary process of perceptual organization. Meanwhile, our method is easy to implement and training-free. Experimental results indicate that our CD measure performs favorably in assessing CDs in photographic images, and consistently surpasses competing models in the presence of image misalignment. Additionally, we empirically verify that our measure functions as a metric in the mathematical sense, and show its promise as a loss function for image and video color transfer tasks. The code is available at https://github.com/real-hjq/MS-SWD.
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。
