Added citation

README.md

@@ -65,4 +65,17 @@ This dataset enables research in:
 - **Style-aware image enhancement**: Developing algorithms that adapt to user aesthetic preferences
 - **Personalized rendering**: Creating systems that learn individual aesthetic tastes
 - **Benchmarking aesthetic models**: Evaluating existing and new approaches to aesthetic quality assessment
-- **Human-AI collaboration**: Understanding how human preferences can guide AI rendering decisions
+- **Human-AI collaboration**: Understanding how human preferences can guide AI rendering decisions
+
+## Citation
+```
+@misc{plohotnyuk2025dear,
+  title={DEAR: Dataset for Evaluating the Aesthetics of Rendering},
+  author={Plohotnyuk, Vsevolod and Panshin, Artyom and Bani{\'c}, Nikola and Bianco, Simone and Freeman, Michael and Ershov, Egor},
+  year={2025},
+  eprint={2512.05209},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV},
+  note = {Preprint at \url{https://arxiv.org/abs/2512.05209}}
+}
+```