Image Visual Complexity Evaluation Based on Deep Ordinal Regression

Image complexity is an important indicator in computer vision that helps people to more accurately evaluate and understand visual image information. Compared with traditional regression algorithms, ordinal regression methods are better suited to handling relationships and structures among ordinal data, providing more accurate reference for image complexity assessment. Currently, IC9600 dataset has made significant progress for providing the largest image complexity dataset, and ICNet provided a baseline model to evaluate the complexity score of images, but it neglects the ordinal property of complexity scores. This paper focuses on exploring a method to evaluate image complexity based on deep ordinal regression. We propose an evaluation model (ICCORN) that combines convolutional neural network ICNet and ordinal regression approach CORN. The model firstly extracts global semantic information and local detail information, and then considers ordinal relationship between complexity scores in the prediction process. The model demonstrates a high degree of correlation with human perception, as indicated by an increased Pearson correlation coefficient of 0.955. Furthermore, other evaluation metrics have also yielded favorable results.