The quantification of visual aesthetics and complexity have a long history, the latter previously operationalized via the application of compression algorithms. Here we generalize and extend the compression approach beyond simple complexity measures to quantify algorithmic distance in historical and contemporary visual media. The proposed "ensemble" approach works by compressing a large number of transformed versions of a given input image, resulting in a vector of associated compression ratios. This approach is more efficient than other compression-based algorithmic distances, and is particularly suited for the quantitative analysis of visual artifacts, because human creative processes can be understood as algorithms in the broadest sense. Unlike comparable image embedding methods using machine learning, our approach is fully explainable through the transformations. We demonstrate that the method is cognitively plausible and fit for purpose by evaluating it against human complexity judgments, and on automated detection tasks of authorship and style. We show how the approach can be used to reveal and quantify trends in art historical data, both on the scale of centuries and in rapidly evolving contemporary NFT art markets. We further quantify temporal resemblance to disambiguate artists outside the documented mainstream from those who are deeply embedded in Zeitgeist. Finally, we note that compression ensembles constitute a quantitative representation of the concept of visual family resemblance, as distinct sets of dimensions correspond to shared visual characteristics otherwise hard to pin down. Our approach provides a new perspective for the study of visual art, algorithmic image analysis, and quantitative aesthetics more generally.
翻译:视觉美学和复杂度的量化具有悠久的历史, 后者以前是通过压缩算法应用的。 在这里, 我们推广和扩大压缩方法, 超越简单复杂的计量方法, 以量化历史和当代视觉媒体的算法距离。 提议的“ 共性” 方法通过压缩大量版本的某个特定输入图像的变异版本, 从而形成相关的压缩比例矢量。 这个方法比其他基于压缩的算法距离更有效, 特别适合视觉艺术品的定量分析, 因为人类的创造过程可以被理解为最广义的算法。 与使用机器学习的可比图像嵌入方法不同, 我们的方法可以通过转换来充分解释。 我们证明该方法在认知上是可信的,适合目的, 通过根据人类复杂度判断来评估该方法, 以及作者和风格的自动检测任务。 我们展示了该方法如何用来揭示和量化历史数据中的趋势, 无论是在几个世纪的尺度上还是在迅速演变的当代NFT艺术市场中。 我们进一步量化时间相似性, 使记录的主流以外的艺术家与那些深入嵌入Zegeemie程的人不同, 我们的方法通过直观的视觉分析, 最后观的图像分析, 提供了一种不同的直观分析。