Computational Photography Assessment: A Framework for Image Quality and Composition Analysis

Abstract

Computational photography has evolved from early digital enhancements to AI-driven image processing. Despite advancements, a gap remains in automated tools for assessing photographic quality and composition. This paper introduces the Professional Photo Analyzer, a framework integrating technical quality assessment, compositional analysis, and metadata contextualization to address challenges of technical isolation, compositional subjectivity, and workflow fragmentation. Our approach unifies noise, blur, and exposure evaluations, adapts aesthetic criteria to different genres, and reconstructs shooting conditions for enhanced analysis. Designed for workflow integration, it provides real-time feedback, batch culling, and educational tools. The experimental results demonstrate superior performance: noise estimation MAE 3.2%, blur detection 98% precision, and composition analysis correlation r = 0.87. User studies show 35% efficiency gains in professional workflows and a 27% improvement in student composition skills. While challenges such as cultural biases and computational demands remain, this research lays the foundation for AI-driven photographic evaluation, bridging human expertise and automation.