IEEE Trans Image Process. 2015 Mar;24(3):1101-14. doi: 10.1109/TIP.2014.2383327.
Being able to predict the degree of visual discomfort that is felt when viewing stereoscopic 3D (S3D) images is an important goal toward ameliorating causative factors, such as excessive horizontal disparity, misalignments or mismatches between the left and right views of stereo pairs, or conflicts between different depth cues. Ideally, such a model should account for such factors as capture and viewing geometries, the distribution of disparities, and the responses of visual neurons. When viewing modern 3D displays, visual discomfort is caused primarily by changes in binocular vergence while accommodation in held fixed at the viewing distance to a flat 3D screen. This results in unnatural mismatches between ocular fixations and ocular focus that does not occur in normal direct 3D viewing. This accommodation vergence conflict can cause adverse effects, such as headaches, fatigue, eye strain, and reduced visual ability. Binocular vision is ultimately realized by means of neural mechanisms that subserve the sensorimotor control of eye movements. Realizing that the neuronal responses are directly implicated in both the control and experience of 3D perception, we have developed a model-based neuronal and statistical framework called the 3D visual discomfort predictor (3D-VDP)that automatically predicts the level of visual discomfort that is experienced when viewing S3D images. 3D-VDP extracts two types of features: 1) coarse features derived from the statistics of binocular disparities and 2) fine features derived by estimating the neural activity associated with the processing of horizontal disparities. In particular, we deploy a model of horizontal disparity processing in the extrastriate middle temporal region of occipital lobe. We compare the performance of 3D-VDP with other recent discomfort prediction algorithms with respect to correlation against recorded subjective visual discomfort scores,and show that 3D-VDP is statistically superior to the other methods.
能够预测观看立体 3D（S3D）图像时感到的视觉不适程度，是改善导致不适的因素的重要目标，例如过度的水平视差、左右视图之间的不对准或不匹配、或不同深度线索之间的冲突。理想情况下，这样的模型应该考虑到捕获和观看几何、视差分布以及视觉神经元的反应等因素。在观看现代 3D 显示器时，视觉不适主要是由于双眼聚散度的变化引起的，而调节则保持在观看距离的平面 3D 屏幕上固定不变。这导致了眼注视和眼焦点之间的不自然匹配，而这种情况在正常的直接 3D 观看中不会发生。这种调节聚散冲突会引起头痛、疲劳、眼睛疲劳和视觉能力下降等不良反应。双眼视觉最终是通过神经机制实现的，这些机制为眼球运动的感觉运动控制提供支持。认识到神经元反应直接涉及 3D 感知的控制和体验，我们开发了一种基于模型的神经元和统计框架，称为 3D 视觉不适预测器（3D-VDP），它可以自动预测观看 S3D 图像时所经历的视觉不适程度。3D-VDP 提取两种类型的特征：1）来自双眼视差统计的粗特征，2）通过估计与水平视差处理相关的神经活动得出的细特征。特别是，我们在枕叶外侧颞中区域部署了一个水平视差处理模型。我们将 3D-VDP 的性能与其他最近的不适预测算法进行了比较，根据与记录的主观视觉不适评分的相关性进行比较，并表明 3D-VDP 在统计学上优于其他方法。
