On the Use of Visual Transformer for Image Complexity Assessment

Perceiving image complexity is a crucial aspect of human visual understanding, yet explicitly assessing image complexity poses challenges. Historically, this aspect has been understudied due to its inherent subjectivity, stemming from its reliance on human perception, and the semantic dependency of image complexity in the face of diverse real-world images. Different computational models for image complexity estimation have been proposed in the literature. These models leverage a variety of techniques ranging from low-level, handcrafted features, to advanced machine learning algorithms. This paper explores the use of recent deep-learning approaches based on Visual Transformer to extract robust information for image complexity estimation in a transfer learning paradigm. Specifically, we propose to leverage three visual backbones, CLIP, DINO-v2, and ImageNetViT, as feature extractors, coupled with a Support Vector Regressor with Radial Basis Function kernel as an image complexity estimator. We test our approach on two widely used benchmark datasets (i.e. IC9600 and SAVOIAS) in an intra-dataset and inter-dataset workflow. Our experiments demonstrate the effectiveness of the CLIP-based features for accurate image complexity estimation with results comparable to end-to-end solutions.