Research on the Construction and Application of a SVM-Based Quantification Model for Streetscape Visual Complexity

Visual complexity is a crucial criterion for evaluating the quality of urban environments and a key dimension in arousal theory and visual preference theory. Objectively quantifying visual complexity holds significant importance for decision-making support in urban planning. This study proposes a visual complexity quantification model based on a support vector machine (SVM), incorporating six key indicators, to establish a mapping relationship between objective image features and subjective complexity perception. This model can efficiently and scientifically predict street view complexity on a large scale. The research findings include the following: (1) the introduction of a new quantification dimension for the urban environment complexity—hierarchical complexity– which reflects the richness of street elements based on an in-depth semantic understanding of images; (2) the established complexity quantification model demonstrates high accuracy, with the indicators ranked by contribution for compression ratio, grayscale contrast, hierarchical complexity, fractal dimension, color complexity, and symmetry; and (3) the model was applied to predict and analyze the visual complexity of the Xiaobailou and Wudadao Districts in Tianjin, revealing that the visual complexity of most streets is moderate, and targeted recommendations were proposed based on different levels of visual complexity.