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.

翻译：本文提出了一种新的数据驱动、非参数方法用于图像质量与美学评估，该方法超越现有技术，无需任何提示工程或微调。通过提出数据中高效的图像锚点，我们消除了对具象文本嵌入的需求。基于对7种最先进自监督模型的广泛评估，本方法在多种数据集和基准测试中展现出优越的性能与鲁棒性。值得注意的是，即使在有限数据条件下，该方法仍能达到与人类评估高度的一致性，并对数据性质及其预处理流程表现出强鲁棒性。我们的贡献为图像评估提供了精简解决方案，同时为视觉信息感知提供了新的见解。