  This paper introduces a new data-driven, non-parametric method for image
quality and aesthetics assessment, surpassing existing approaches and requiring
no prompt engineering or fine-tuning. We eliminate the need for expressive
textual embeddings by proposing efficient image anchors in the data. Through
extensive evaluations of 7 state-of-the-art self-supervised models, our method
demonstrates superior performance and robustness across various datasets and
benchmarks. Notably, it achieves high agreement with human assessments even
with limited data and shows high robustness to the nature of data and their
pre-processing pipeline. Our contributions offer a streamlined solution for
assessment of images while providing insights into the perception of visual
information.
