基于学习的图像编解码器在指纹存储中的有效性
Effectiveness of learning-based image codecs on fingerprint storage
AI总结：
本研究首次评估基于学习的图像编解码器在指纹存储中的适应性，发现在固定码率下其失真和细节点保留显著优于 JPEG2000，且不影响自动识别与人工视觉检查。
摘要：
基于学习的编码技术的成功以及基于学习的图像编码标准（如 JPEG-AI）的发展，表明这类解决方案将被应用于包括指纹等生物特征数据存储在内的不同领域。然而，基于学习的压缩伪影的特殊性质在提取生物特征和特征点（例如细节点）方面的影响和有效性上引发了若干问题。大多数模型是在自然彩色图像上训练的，其特征与通常的生物特征图像（如指纹或虹膜图像）截然不同，这一事实使该问题更加突出。事实上，这些问题需要被准确地质疑和调查，因为此类分析在很大程度上仍未被探索。本研究通过测量其对细节点提取和表征的影响，首次调查了基于学习的图像编解码器在指纹图像存储中的适应性。实验结果表明，在固定码率点下，学习到的解决方案在失真和细节点保留方面均显著优于以往的指纹编码标准（如 JPEG2000）。确实，实验结果证明，学习到的压缩伪影的特殊性既不妨碍自动指纹识别（因为细节点类型和位置未被显著改变），也不损害供人类视觉检查的图像质量（因为它们在 BD rate 和 PSNR 上分别获得了 47.8% 和 +3.97dB 的提升）。
英文摘要：
The success of learning-based coding techniques and the development of learning-based image coding standards, such as JPEG-AI, point towards the adoption of such solutions in different fields, including the storage of biometric data, like fingerprints. However, the peculiar nature of learning-based compression artifacts poses several issues concerning their impact and effectiveness on extracting biometric features and landmarks, e.g., minutiae. This problem is utterly stressed by the fact that most models are trained on natural color images, whose characteristics are very different from usual biometric images, e.g, fingerprint or iris pictures. As a matter of fact, these issues are deemed to be accurately questioned and investigated, being such analysis still largely unexplored. This study represents the first investigation about the adaptability of learning-based image codecs in the storage of fingerprint images by measuring its impact on the extraction and characterization of minutiae. Experimental results show that at a fixed rate point, learned solutions considerably outperform previous fingerprint coding standards, like JPEG2000, both in terms of distortion and minutiae preservation. Indeed, experimental results prove that the peculiarities of learned compression artifacts do not prevent automatic fingerprint identification (since minutiae types and locations are not significantly altered), nor do compromise image quality for human visual inspection (as they gain in terms of BD rate and PSNR of 47.8% and +3.97dB respectively).
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。
