Evaluating text-to-image generation models requires alignment with human perception, yet existing human-centric metrics are constrained by limited data coverage, suboptimal feature extraction, and inefficient loss functions. To address these challenges, we introduce Human Preference Score v3 (HPSv3), which comprises: (1) HPDv3, the first full-spectrum human preference dataset integrating 1.7M text-image pairs and 1M annotated pairwise comparisons from state-of-the-art generative models and high-quality real-world images, and (2) a preference model leveraging VLM-based feature extraction and RankNet loss for fine-grained ranking. Furthermore, we propose Chain-of-Human-Preference (CoHP), a novel reasoning approach for iterative image refinement. CoHP improves image quality efficiently without requiring additional training data. By using HPSv3 as a reward model, CoHP ensures that the highest-quality image is selected at each iteration, progressively enhancing the output. Extensive experiments demonstrate that HPSv3 serves as a robust benchmark for full-spectrum image evaluation, and CoHP offers an efficient, human-aligned approach to enhancing image generation quality.