In the present digital age, where visual stimuli dominate our experiences, image quality plays an important role in determining user satisfaction. In this study, we propose a new direction for advancing the field by focusing on personalized image quality assessment, which can benefit a wide range of industries from streaming services and photography postprocessing algorithms to more specialized domains with different image quality requirements, such as medical imaging. We focus on exploring individual preferences, specifically in the context of contrast, one of the key attributes influencing image quality. We employ a twin neural network to predict individual contrast preference, which is particularly effective for enhancing contrast adjustment for observers with distinct and consistent preferences that deviate from the average. We also introduce an intermediate step toward personalization—a faster and less computationally intensive approach for incorporating observer awareness into general quality assessment models. Our findings highlight the importance of accounting for individual preferences and the positive impact of incorporating them into image processing and quality assessment methodologies.