Viewing stereoscopic images sometimes causes viewers to feel inconvenience, which is called 3D visual discomfort. Excessive horizontal disparity, misalignment between the left and right views, or depth cues conflicts are some of the important factors involved in 3D visual discomfort. The ability to estimate the degree of 3D visual discomfort can be used to improve the 3D display systems and provide acceptable binocular visual quality. Most of the existing visual discomfort prediction (VDP) approaches extract hand-crafted features based on perceptual modeling and statistical analysis of disparities. We have proposed a simple yet effective VDP model based on unsupervised learning of sparse features which are highly predictive of subjective discomfort levels. These features are extracted from the aggregation of left and right disparity maps. This aggregation effectively highlights the areas with sudden changes and high levels of disparities where discomfort is most likely to occur. The regression model trained by the features, predicts high correlated 3D visual discomfort scores on each dataset. The cross-database results are also superior to other reported ones.

观看立体图像有时会导致观看者感到不便，这称为 3D 视觉不适。过度的水平差异、左右视图之间的错位或深度线索冲突是 3D 视觉不适的一些重要因素。估计 3D 视觉不适程度的能力可用于改进 3D 显示系统并提供可接受的双目视觉质量。大多数现有的视觉不适预测 (VDP) 方法都是基于感知建模和视差统计分析来提取手工制作的特征。我们提出了一种简单而有效的 VDP 模型，该模型基于稀疏特征的无监督学习，可以高度预测主观不适水平。这些特征是从左右视差图的聚合中提取的。这种聚合有效地突出了最有可能发生不适的突然变化和高度差异的区域。由特征训练的回归模型预测每个数据集上的高相关 3D 视觉不适分数。跨库结果也优于其他报告的结果。