IEEE Trans Image Process. 2017 Mar;26(3):1202-1215. doi: 10.1109/TIP.2016.2642791. Epub 2016 Dec 21.
Subjective and objective measurement of the perceptual quality of depth information in symmetrically and asymmetrically distorted stereoscopic images is a fundamentally important issue in stereoscopic 3D imaging that has not been deeply investigated. Here, we first carry out a subjective test following the traditional absolute category rating protocol widely used in general image quality assessment research. We find this approach problematic, because monocular cues and the spatial quality of images have strong impact on the depth quality scores given by subjects, making it difficult to single out the actual contributions of stereoscopic cues in depth perception. To overcome this problem, we carry out a novel subjective study where depth effect is synthesized at different depth levels before various types and levels of symmetric and asymmetric distortions are applied. Instead of following the traditional approach, we ask subjects to identify and label depth polarizations, and a depth perception difficulty index (DPDI) is developed based on the percentage of correct and incorrect subject judgements. We find this approach highly effective at quantifying depth perception induced by stereo cues and observe a number of interesting effects regarding image content dependency, distortion-type dependence, and the impact of symmetric versus asymmetric distortions. Furthermore, we propose a novel computational model for DPDI prediction. Our results show that the proposed model, without explicitly identifying image distortion types, leads to highly promising DPDI prediction performance. We believe that these are useful steps toward building a comprehensive understanding on 3D quality-of-experience of stereoscopic images.
对称和非对称失真立体图像深度信息感知质量的主观和客观测量是立体 3D 成像中一个非常重要但尚未深入研究的基本问题。在这里,我们首先按照传统的绝对类别评分协议进行主观测试,该协议广泛用于一般图像质量评估研究。我们发现这种方法存在问题,因为单眼线索和图像的空间质量对被试给出的深度质量评分有很大影响,使得难以单独确定立体线索在深度感知中的实际贡献。为了克服这个问题,我们进行了一项新的主观研究,在应用各种类型和水平的对称和非对称失真之前,在不同的深度水平上合成深度效果。我们没有遵循传统的方法,而是要求受试者识别和标记深度极化,并基于正确和错误判断的百分比开发了深度感知困难指数 (DPDI)。我们发现这种方法非常有效地量化了立体线索引起的深度感知,并观察到了一些关于图像内容依赖性、失真类型依赖性以及对称与非对称失真影响的有趣效果。此外,我们提出了一种用于 DPDI 预测的新计算模型。我们的结果表明,所提出的模型无需显式识别图像失真类型,即可实现非常有前景的 DPDI 预测性能。我们相信,这些是朝着建立对立体图像体验质量的全面理解迈出的有用步骤。