  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.
