AI图像增强的感知画质评价

AI图像增强的感知画质评价

随着人工智能图像增强技术的飞速发展，如超分辨率、去噪、色彩增强和风格迁移等方法已广泛应用于摄影、多媒体内容创作等领域。然而，如何客观、准确地评估AI增强后图像的感知质量，仍然是一个极具挑战性的开放问题。传统的图像质量评估指标（如PSNR和SSIM）往往难以充分反映人类视觉系统对图像质量的真实感受。本演讲将围绕"AI图像增强的感知质量评估"这一主题，系统介绍当前主流的全参考和无参考质量评估方法，探讨基于深度学习的感知质量度量模型的最新进展，分析AI增强图像中常见的失真类型与感知特征，并提出面向AI增强图像的新一代感知质量评价方法。

Perceptual Quality Assessment for AI Image Enhancement

With the rapid advancement of AI-powered image enhancement techniques, methods such as super-resolution, denoising, color enhancement, and style transfer have been widely applied in fields like photography and multimedia content creation. However, objectively and accurately assessing the perceptual quality of AI-enhanced images remains a challenging problem. Traditional image quality assessment (IQA) metrics, such as PSNR and SSIM, often fail to adequately capture the perceptual experience of the human visual system. Focusing on perceptual quality assessment for AI image enhancement, this talk provides a systematic overview of mainstream full-reference and no-reference IQA methods, discusses recent advances in deep learning-based IQA models, analyzes common distortion patterns found in AI-enhanced images, and proposes a new IQA paradigm specifically designed for AI-enhanced images.