Chan San To, Fried Eliot
Mechanics and Materials Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa 904-0495, Japan.
Proc Natl Acad Sci U S A. 2024 Dec 17;121(51):e2406735121. doi: 10.1073/pnas.2406735121. Epub 2024 Dec 13.
Inspired by the way that digital artists zoom out of the canvas to assess the visual impact of their works, we introduce a conceptually simple yet effective metric for quantifying the clarity of digital images. This metric contrasts original images with progressively "melted" counterparts, produced by randomly flipping adjacent pixel pairs. It measures the presence of stable structures, assigning the value zero to completely uniform or random images and finite values for those with discernible patterns. This metric respects the color diversity of the original image and withstands image compression and color quantization. Its suitability for diverse image analysis problems is demonstrated through its effective evaluation of textural images, the identification of structural transitions in physical systems like the Potts model, and its consistency with color theory in digital arts. This allows us to demonstrate that color in visual art functions as a state variable, akin to the spin configuration in magnets, driving artistic designs to transition between states with distinct clarity. When combined with the Shannon entropy, which quantifies color diversity, the structural stability metric can serve as a navigation tool for artists to explore pathways on the complex structural information landscape toward the completion of their artwork. As a practical demonstration, we apply our metric to refine and optimize an emote design for a video game. The structural stability metric emerges as a versatile tool for extracting nuanced structural information from digital images, which may enhance decision-making and data analysis across scientific and creative domains.
受数字艺术家从画布上缩放以评估其作品视觉效果方式的启发,我们引入了一种概念简单但有效的指标来量化数字图像的清晰度。该指标将原始图像与通过随机翻转相邻像素对生成的逐渐“融化”的对应图像进行对比。它测量稳定结构的存在情况,将完全均匀或随机的图像赋值为零,而对于具有可辨别图案的图像赋予有限值。该指标尊重原始图像的颜色多样性,并且能够经受图像压缩和颜色量化。通过对纹理图像的有效评估、在诸如Potts模型等物理系统中识别结构转变以及与数字艺术中的颜色理论保持一致,证明了其适用于各种图像分析问题。这使我们能够证明视觉艺术中的颜色起着状态变量的作用,类似于磁体中的自旋构型,驱动艺术设计在具有不同清晰度的状态之间转变。当与量化颜色多样性的香农熵相结合时,结构稳定性指标可以作为艺术家的导航工具,用于在复杂的结构信息景观中探索通向完成其作品的路径。作为一个实际演示,我们应用我们的指标来优化一款电子游戏的表情符号设计。结构稳定性指标成为从数字图像中提取细微结构信息的通用工具,这可能会增强科学和创意领域的决策制定和数据分析。