Conclusions 79
screen size and disparity by Xing et al. [Xing et al., 2010b]. A prior subjective test defined the viewing location as an insignificant factor of 3D video QoE, while various combinations of the content, baseline, and screen size were found to be significant. The insignificance of the viewing distance can be explained by the content selection. The sequences, which had been chosen for the test, remained within the comfortable viewing zone (DoF=±0.2), which was calculated taking into account maximum screen disparities and viewing distance. The objective metric encompasses the estimated image disparity plus the weighted screen size. However, it can not detect visual discomfort perceived by the viewers because perceptual thresholds of view asymmetries and ZoC are not taken into account. From perceptual issues, only crosstalk was considered in another work by the same authors [Xing et al., 2010c].
3.6.3 Including comfort attribute
Sohn et. al. proposed two new object-dependent disparity features: relative dispar- ity (the mean disparity difference between neighboring objects) and object thickness (the ratio of mean width relative to the mean absolute disparity of an object) for the evaluation of visual discomfort [Sohn et al., 2013]. Their results demonstrated that the difference in disparities between neighboring objects and the stimulus width should be taken into account in visual discomfort prediction algorithms. Using the new features was able to improve the prediction performance of metrics that use traditional disparity features (those taking into account the disparity magnitude and special frequency of a stereoscopic scene). All these features were collected from state of the art studies. View asymmetry thresholds were not included in the proposed metrics. Nevertheless, the au- thors made sure that vertical asymmetries did not influence the results of the subjective studies.
Winkler in [Winkler, 2014] presented various metrics which are able to detect some common sources of visual discomfort in stereoscopic content. These metrics detect dis- parity and view mismatches (based on the work of Takaya [Takaya, 2010]). The disparity range is computed to define the ZoC, to check that maximum disparity does not cause eye divergence, and to verify the disparity transaction between frames to avoid depth discontinuities. Unfortunately, geometrical view asymmetries were not considered. The proposed metrics are computationally efficient and make a step towards the evaluation of 3D QoE by taking into account image quality, depth, and comfort. However, visual- ization parameters and perceptual thresholds should be integrated to consider the final viewer’s perception of rendered stereoscopic content. Another potential issue of this research is how to combine the scores of these and other quality metrics into one score of 3D video QoE.
80 Chapter 3
each primary attribute could be linked directly with the technical parameters of a 3D system.
• Image quality can be evaluated separately from the comfort and depth compo- nents. In subjective studies it is often evaluated by creating such degradations as JPEG compression, noise, and blur. For its objective evaluation, conventional 2D quality metrics can be applied.
• Opposite to depth quantity, the concept ofdepth qualityseems to be quite diffi- cult to judge for viewers. In subjective studies both depth quality and depth quan- tity are assessed by changing the range of disparities or DoF. Neither of the two concepts take into account the depth component in terms of stereoscopic distortions (e.g magnification/miniaturization of object dimensions and stretching/compres- sion of depth). Objectively, some mathematical methods permit the evaluation of the resulting distortions of rendered content. However, the perceptual limits of ge- ometrical distortions are not known: what level of shape distortion is perceptible, what level of distortion is annoying, what impact various levels of shape distortions have on the overall 3D video QoE, and so on.
• Visual comfort is the dominant factor of 3D video QoE. Hence it is important to understand the potential sources of visual discomfort and assess its impact on human perception. Subjectively, discomfort is evaluated by creating stereoscopic stimuli with view asymmetries or outside the zone of comfort. There are a few metrics that exist to assess visual comfort objectively. Unfortunately, none of them takes into account all the possible reasons of visual discomfort.
• The state-of-the-artobjective metricsin Section3.6.1evaluate the quality of the signal without considering the perception of depth involved and resemble 2D met- rics concerning spatial distortions. The metrics in Section3.6.2consider the depth dimension without taking into account if it remains within the zone of comfort.
None of the metrics in Section 3.6.3 examine the potential impact of view asym- metries. So, visual discomfort might not always be predicted correctly. Hence, a comprehensive objective metric of 3D video QoE does not exist at the moment.
• Acomprehensive objective metricof 3D video QoE should consider all quality aspects of a rendered signal which depends on camera parameters, the visualiza- tion environment, display technology, 3D representation format, and the viewer’s perception. It seems to be impossible without considering the human perceptual thresholds.
Part I
Visual attention in 3D
81
Chapter 4
State-of-the-art of visual attention in S3D
Contents
3.1 Introduction . . . . 61 3.2 3D Quality of Experience . . . . 61 3.3 Components influencing 3D video QoE . . . . 62 3.3.1 Picture quality . . . . 62 3.3.2 Depth quality. . . . 62 3.3.3 Visual (dis)comfort and visual fatigue . . . . 63 3.3.4 Additional perception dimensions. . . . 63 3.4 Models of 3D QoE . . . . 64 3.5 Subjective assessment methods of 3D QoE . . . . 66 3.5.1 Assessment of visual discomfort and fatigue . . . . 70 3.6 Objective assessment methods of 3D QoE. . . . 74 3.6.1 2D image quality . . . . 77 3.6.2 Including depth attribute . . . . 78 3.6.3 Including comfort attribute . . . . 79 3.7 Conclusions . . . . 79
4.1 Introduction
This chapter presents some of the state-of-the-art studies concerning visual attention.
It also provides a review of recent studies comparing visual attention for S3D and 2D conditions.