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A novel depth perception prediction metric for advanced multimedia applications
Multimedia Systems ( IF 3.9 ) Pub Date : 2019-06-28 , DOI: 10.1007/s00530-019-00623-x
Gokce Nur Yilmaz

Ubiquitous multimedia applications diffuse our everyday life activities which appreciate their significance about improving our experiences. Therefore, proliferation of the multimedia applications enhancing these experiences needs critical attention of the researchers. Considering this motivation, to overcome the possible barrier of the proliferation of the 3D video-related multimedia applications providing enhanced quality of experience (QoE) to the end users, an objective metric is proposed in this study. The proposed metric tackles the depth perception prediction part reflecting the most important aspect of the 3D video QoE from the user point of view. Considering that the no reference metric type is the most effective one compared to its counterparts, the proposed metric is developed based on this type. In the light of the envision that human visual system-related cues have critical importance on developing accurate metrics, the focus of the proposed metric is directed on the association of the z-direction motion and stereopsis depth cues in the metric development. These cues are derived from the depth map contents having stressed significant depth levels. In addition, the analysis results of the conducted subjective experiments which are currently the “gold standards” for the reliable depth perception prediction are incorporated with the proposed metric. Considering the effective correlation coefficient and root mean square error performance assessment results taken using the proposed metric in comparison to the widely exploited quality assessment metrics in literature, it can be clearly stated that the development of the improved 3D video multimedia applications can be accelerated using it.

中文翻译:

一种用于高级多媒体应用的新型深度感知预测指标

无处不在的多媒体应用程序传播了我们的日常生活活动,这些活动欣赏它们对改善我们体验的重要性。因此,增强这些体验的多媒体应用程序的扩散需要研究人员的高度关注。考虑到这一动机,为了克服与 3D 视频相关的多媒体应用向最终用户提供增强的体验质量 (QoE) 的扩散可能存在的障碍,本研究中提出了一个客观指标。所提出的度量解决了从用户角度反映 3D 视频 QoE 最重要方面的深度感知预测部分。考虑到无参考度量类型与其对应类型相比是最有效的,因此建议的度量是基于这种类型开发的。鉴于人类视觉系统相关线索对开发准确度量具有至关重要的意义,建议度量的重点是在度量开发中 z 方向运动和立体视觉深度线索的关联。这些线索来自具有强调显着深度水平的深度图内容。此外,所进行的主观实验的分析结果(目前是可靠深度感知预测的“黄金标准”)与所提出的度量标准相结合。考虑到与文献中广泛利用的质量评估指标相比,使用所提出的指标获得的有效相关系数和均方根误差性能评估结果,
更新日期:2019-06-28
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