Held Robert T, Cooper Emily A, O'Brien James F, Banks Martin S
University of California, San Francisco and University of California, Berkeley, CA 94720.
ACM Trans Graph. 2010 Mar 1;29(2). doi: 10.1145/1731047.1731057.
We present a probabilistic model of how viewers may use defocus blur in conjunction with other pictorial cues to estimate the absolute distances to objects in a scene. Our model explains how the pattern of blur in an image together with relative depth cues indicates the apparent scale of the image's contents. From the model, we develop a semiautomated algorithm that applies blur to a sharply rendered image and thereby changes the apparent distance and scale of the scene's contents. To examine the correspondence between the model/algorithm and actual viewer experience, we conducted an experiment with human viewers and compared their estimates of absolute distance to the model's predictions. We did this for images with geometrically correct blur due to defocus and for images with commonly used approximations to the correct blur. The agreement between the experimental data and model predictions was excellent. The model predicts that some approximations should work well and that others should not. Human viewers responded to the various types of blur in much the way the model predicts. The model and algorithm allow one to manipulate blur precisely and to achieve the desired perceived scale efficiently.
我们提出了一个概率模型，该模型描述了观看者如何结合散焦模糊和其他图像线索来估计场景中物体的绝对距离。我们的模型解释了图像中的模糊模式以及相对深度线索是如何表明图像内容的表观比例的。基于该模型，我们开发了一种半自动算法，该算法将模糊应用于清晰渲染的图像，从而改变场景内容的表观距离和比例。为了检验该模型/算法与实际观看者体验之间的对应关系，我们对人类观看者进行了一项实验，并将他们对绝对距离的估计与模型的预测进行了比较。我们针对因散焦而具有几何正确模糊的图像以及具有常用正确模糊近似值的图像进行了此项实验。实验数据与模型预测之间的一致性非常好。该模型预测，某些近似值应该效果良好，而其他近似值则不然。人类观看者对各种类型模糊的反应与模型预测的方式大致相同。该模型和算法使人们能够精确地控制模糊，并有效地实现所需的感知比例。
