Existing image complexity metrics cannot distinguish meaningful content from
noise. This means that white noise images, which contain no meaningful
information, are judged as highly complex. We present a new image complexity
metric through hierarchical clustering of patches. We use the minimum
description length principle to determine the number of clusters and designate
certain points as outliers and, hence, correctly assign white noise a low
score. The presented method has similarities to theoretical ideas for measuring
meaningful complexity. We conduct experiments on seven different sets of
images, which show that our method assigns the most accurate scores to all
images considered. Additionally, comparing the different levels of the
hierarchy of clusters can reveal how complexity manifests at different scales,
from local detail to global structure. We then present ablation studies showing
the contribution of the components of our method, and that it continues to
assign reasonable scores when the inputs are modified in certain ways,
including the addition of Gaussian noise and the lowering of the resolution.

提出了一种基于层次聚类和最小描述长度的图像复杂度衡量方法，可以正确识别出白噪声图像，并在多组实验中表现出最准确的衡量结果，同时还可以从不同层次的聚类中揭示出复杂度的局部和全局特征以及相关构成因素。