DEVELOPING COMPUTATIONAL AESTHETICS FRAMEWORKS TO EVALUATE QUALITY IN DIGITAL ARTWORK CREATIONS - Kamble, Kanchan . P; Patil, Milind; Rajurkar, Harish; Medhe, Vidya Atish; Li, Wei; Cathrine R, Thephilah (ShodhKosh: Journal of Visual and Performing Arts, 2026)
The recent development of digital art and AI-driven creative technologies has created a greater necessity of organised ways of assessing the aesthetic value of digital artworks. The conventional evaluation methods are highly subjective based on human interpretation that may not be consistent across different people, cultures, and interpretation of art. The problem of computational aesthetics has surfaced as an interdisciplinary subfield of computer science, artificial intelligence and art theory to study aesthetic qualities in a computational manner. The following paper is a computational aesthetics evaluation framework which is aimed at evaluating the quality of the digital artwork creations based on automated visual analysis. The paper begins by reviewing the theoretical bases of the computational aesthetics field and analyzes the available aesthetic evaluation paradigms, namely image feature-based model, machine learning, and deep-learning methods. Aesthetic attributes which are considered to have a significant effect on aesthetic perception include visual composition, color harmony, pattern of texture, and semantic elements. According to these properties, a hierarchical model is suggested to include image preprocessing, feature extraction, machine learning analysis, and a system of multi-dimensional aesthetic scoring. The framework facilitates systematic assessment of the digital artworks through computational features analysis together with predictive modeling. Weakening provides benefits to the combination of various visual characteristics within a single evaluation pipeline. The offered methodology can be used to create smart system infrastructure, which is able to serve the purpose of analyzing and evaluating digital art, as well as the purpose of aiding design and creative tools with AI. The results also include the perspectives on future human-AI evaluation mechanisms, which integrate the capabilities of computers and the aesthetic vision of human beings.