基于提示的扩散图像生成模型颜色可访问性评估
Prompt-Driven Color Accessibility Evaluation in Diffusion-based Image Generation Models
AI总结：
本文提出CVDLoss指标，评估扩散模型在不同类别中对颜色可访问性提示的响应能力，揭示现有模型在可访问性方面的不足。
摘要：
生成模型正越来越多地融入创意工作流程中。尽管文本到图像生成在视觉质量和多样性方面表现出色，但颜色可访问性对于色觉缺陷用户仍鲜有研究。我们的工作系统地评估了由常见预训练扩散模型生成的图像在不同类别中的颜色可访问性。我们通过已建立的、现成的色觉缺陷模拟方法量化性能，并引入了“CVDLoss”，一个衡量图像梯度差异以反映结构细节的新指标。我们通过与常用达尔顿化方法的对比验证了CVDLoss的敏感性，证明其对颜色可访问性修改的反应。将CVDLoss应用于模型输出表明，现有扩散模型在响应以可访问性为导向的提示时存在可靠性问题。因此，我们的研究确立了CVDLoss作为面向可访问性图像生成和后期处理的有价值的评估工具，为当前生成模型在解决颜色可访问性方面的局限性提供了见解。
英文摘要：
Generative models are increasingly integrated into creative workflows. While text-to-image generation excels in visual quality and diversity, color accessibility for users with Color Vision Deficiencies (CVD) remains largely unexplored. Our work systematically evaluates color accessibility in images generated by a common pretrained diffusion model, prompted to improve accessibility across diverse categories. We quantify performance using established, off-the-shelf CVD simulation methods and introduce "CVDLoss", a new metric measuring differences in image gradients indicative of structural detail. We validate CVDLoss against a commonly used daltonization method, demonstrating its sensitivity to color accessibility modifications. Applying CVDLoss to model outputs reveals that existing diffusion models struggle to reliably respond to accessibility-focused prompts. Consequently, our study establishes CVDLoss as a valuable evaluation tool for accessibility-aware image generation and post-processing, offering insights into current generative models' limitations in addressing color accessibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
