自然图像自顶向下的恰可察觉差异估计
Towards Top-Down Just Noticeable Difference Estimation of Natural Images
摘要：
自然图像的最小可察觉差异（JND）是指人类视觉系统（HVS）无法感知的最大像素强度变化幅度。现有的JND估计的努力主要致力于在空间域或/和频域中建模不同的掩蔽效应，然后将它们融合到整体JND估计中。在这项工作中，我们转向一种截然不同的方式来解决这个问题，采用自顶向下的设计理念。所提出的JND估计模型不是以自下而上的方式明确地制定和融合不同的掩蔽效应，而是致力于首先预测原始图像的临界感知无损（CPL）对应物，然后计算原始图像和预测的CPL图像之间的差异图作为JND图。我们进行主观实验，以确定500幅图像的临界点，并发现在这些临界点的所有500幅图像的累积归一化KLT系数能量值的分布可以很好地表征由威布尔分布。给定一个测试图像，其相应的临界点是由一个简单的加权平均方案，其中的权重由拟合的威布尔分布函数确定。JND模型的性能进行评估显式与直接JND预测和隐式与两个应用程序，包括JND引导的噪声注入和JND引导的图像压缩。实验结果表明，我们提出的JND模型可以实现更好的性能比几个最新的JND模型。此外，我们还比较了建议的JND模型与现有的视觉差异预测（VDP）指标在失真检测和歧视的能力。结果表明，我们的JND模型也有很好的性能在这个任务。
英文摘要：
Just noticeable difference (JND) of natural images refers to the maximum pixel intensity change magnitude that typical human visual system (HVS) cannot perceive. Existing efforts on JND estimation mainly dedicate to modeling the diverse masking effects in either/both spatial or/and frequency domains, and then fusing them into an overall JND estimate. In this work, we turn to a dramatically different way to address this problem with a top-down design philosophy. Instead of explicitly formulating and fusing different masking effects in a bottom-up way, the proposed JND estimation model dedicates to first predicting a critical perceptual lossless (CPL) counterpart of the original image and then calculating the difference map between the original image and the predicted CPL image as the JND map. We conduct subjective experiments to determine the critical points of 500 images and find that the distribution of cumulative normalized KLT coefficient energy values over all 500 images at these critical points can be well characterized by a Weibull distribution. Given a testing image, its corresponding critical point is determined by a simple weighted average scheme where the weights are determined by a fitted Weibull distribution function. The performance of the proposed JND model is evaluated explicitly with direct JND prediction and implicitly with two applications including JND-guided noise injection and JND-guided image compression. Experimental results have demonstrated that our proposed JND model can achieve better performance than several latest JND models. In addition, we also compare the proposed JND model with existing visual difference predicator (VDP) metrics in terms of the capability in distortion detection and discrimination. The results indicate that our JND model also has a good performance in this task.
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