Smart Visual Optimizer: An Adaptive Human-in-the-Loop System for Intelligent Image Enhancement and Quality-Aware Decision Support

Abstract

Image enhancement remains a challenging problem due to the diversity of image degradation types, varying user preferences,and different application requirements. Traditional enhancement methods often rely on fixed processing pipelines that may notadapt effectively to different image characteristics, which can produce over-enhanced or visually inconsistent results. This paper presents Smart Visual Optimizer, a human-centered adaptive image enhancement framework that combines scene-aware analysis, mode-specific image processing pipelines, objective quality assessment, and interactive user control. The proposedsystem analyzes image characteristics and supports five enhancement modes: AI Super, Product, Social, Low Light, and Scan. Eachmode applies a dedicated processing route designed for its target image category, such as product photographs, portrait images,low-light scenes, general images, and scanned documents. The framework computes quality indicators derived from brightness,contrast, sharpness, saturation, highlight distribution, and shadow distribution, then displays the enhanced result with quality metricsand system recommendations. Experimental evaluation was conducted using 25 real-world images across the supported categories. The results showedconsistent improvement in the average quality score across all tested categories, with the strongest improvements observed in LowLight, Document, and General images. Product and Social modes applied more conservative enhancement to preserve realisticappearance, natural colors, skin texture, and identity. The proposed framework demonstrates that combining adaptive image processing with Human-in-the-Loop interaction canimprove practical image enhancement systems by providing automated optimization, visual comparison, quality-aware feedback,and user-controlled refinement.