134 Chapter 6
Background and motivation 135
2. Define subjective quality factors
3. Associate with technical quality parameters 4. Measure technical
quality parameters 1. Select basic perceptual
attribute
5. Predict subjective judgment Objective prediction Framework to predict 3D QoE
Figure 6.1: The framework to predict 3D video QoE
In this thesis, the following sections focus on the block “Objective prediction”. But, first the block “Framework to predict 3D QoE” will be discussed in this section.
Figure 6.2 presents the axes of 3D video QoE with associated subjective quality factors. Selected image quality factors were proposed in [NTT, 2014]. One of the tasks of any selected objective method should be measurement of all artifacts related to 2D image quality axis. This complex issue was investigated a lot by various research groups.
Therefore, it will be excluded from the studies of present thesis e.g. all test images must be free from any 2D image quality artifacts (see Chapter 2for review).
Spatial distorsion
-Reduced resolution, blurring -Block distortion
-False outline Temporal distortions -Jerkiness
-Flicker -Motion blur
-Interruptions and frozen images Spatio-temporal distortion
‐Mosquito noise
‐“Busy” edges
‐Disturbance (failure)
Visual comfort
3D video QoE
3D geometrical distortion Image quality
In planar direction -Magnification -Miniaturization In Z depth direction -Compression -Stretching
‐Visual annoyance
‐Visual fatigue Overall
percept Perceptual
attributes
Subjective quality factors
Figure 6.2: Subjective quality factors associated with basic perceptual attributes of 3D video QoE.
Subjectively, 3D geometrical distortions can be perceived as compression or stretch- ing and magnification or miniaturization of objects in depth direction. Such distortions include the cardboard effect, puppet theater effect, gigantism, and miniaturization. Ob-
136 Chapter 6
jectively, shape distortion can be computed when the camera and visualization space parameters are known (see for details Section 2.2.1equation 2.8).
Even though it is known how to estimate the quantity of 3D geometrical distor- tions objectively, very few studies have been done to study it subjectively. Mendiburu indicates that a roundness factor between 0.7 and 1 is not discernible under perfect con- ditions (roundness factor =1) [Mendiburu, 2009]. However, these numbers were obtained empirically and not supported by any subjective tests. Thus, perceptual thresholds of roundness factor have not been studied accurately, especially considering target applica- tions (TV, cinema, etc.). Also, little attention was paid to the impact of depth distortion on visual comfort. Nevertheless, depth distortion by itself supposedly does not violate the physiological mechanism responsible for depth perception like in the case of the vergence-accommodation conflict or severe view asymmetries. Furthermore, in the fol- lowing experimental work, all test sequences will be free from noticeable stereoscopic distortions.
Visual discomfort as a result of the vergence-accommodation conflict or view asym- metries is a typical problem of 3D systems only. That is why the axis “Visual comfort”
in Figure6.2gets the top priority in this thesis manuscript. However, visual fatigue will not be taken into account. As explained in Section3.5.1, it can be induced by multiple excessive efforts of the visual system and requires some time to emerge. But, in the following subjective experiments, stereoscopic videos with a maximum duration of 15 seconds are used, which might be not sufficient to consider visual fatigue. Therefore, further description of the block “Framework to predict 3D QoE” is done for the basic perceptual attribute “Visual Comfort” excluding visual fatigue.
Figure 6.3 characterizes visual annoyance in terms of technical quality parameters (P x), which determine the possible causes of visual discomfort in S3D. Once this sub- jective quality factor is linked to the technical quality parameters, it can be evaluated subjectively and objectively.
Visual annoyance Subjective
quality factor
Technical quality parameters Px
Objective measurement of Px AlgorithmPx (Left, Right)
Distortion level D of Px, degradation units
AlgoPx(L,R)= DPx -Vertical shift (Pvertical)
-View magnification (Pfocal) -View rotation (Protation) -Keystone distortion (Pkeystone) -Luminance mismatch (Pblack, Pwhite)
-Color mismatch (Pred,Pgreen,Pblue) -Maximum crossed and
uncrossed disparity (PCDmax, PUDmax)
-ALGOvertical(L,R) -ALGOfocal(L,R) -ALGOrotation(L,R) -ALGOkeystone(L,R) -ALGOblack,white(L,R) -ALGOred,green,blue(L,R) -ALGOCDmax(L,R) -ALGOUDmax(L,R)
- n, º (lines) - n, º - n, º - n, º - n, % - n, % - n, º (pixels) - n, º (pixels)
Figure 6.3: Technical quality parameters associated with the basic perceptual attribute
“Visual comfort”.
For objective evaluation, technical parameters P xshould be measured using a ded- icated algorithm or formula (AlgorithmP x). The output of the such algorithm is a distortion value (DP x) in a unit of degradation. For example, vertical shift can be mea- sured in degree of visual angle (n°) or number of lines; mismatch of white luminance level in percentage of mismatch (n%); maximum crossed and uncrossed disparities in degree of visual angle or number of pixels. Though, when it is possible it is recommended to
Objective model proposition 137
translate measured values into degrees of visual angle since the display size and visual- ization distance influence perception of stereoscopic content. But the usage of a degree of visual angle generalizes such dependencies of results.
Several software are available on the market that can accomplish objective mea- surements (StereoLabs tool, Cel-Scope, Sony MPE-200 etc.). They measure technical parameters but they do not provide reliable information about the impact on human perception.
As discussed in Chapter 1, binocular vision is a physiological mechanism. Hence, without the integration of human perceptual information, it will not possible to predict visual discomfort induced by 3D system and, thus, to conclude about 3D video QoE. Therefore, similar to recently standardized 2D metrics [OPTICOM, 2008, SwissQual, 2010], it seems reasonable to develop a 3D picture metric that considers the properties of human vision, rather than using data metric approaches that only take into account the characteristics of the signal.
Another issue of objective quality measurement is the necessity of establishing the link between the predicted MOS scores and the subjective ones to associate with a certain quality level. For instance, if an objective metric evaluates video quality with score of 23, it is impossible to conclude what it means in terms of quality (“Good?”, “Poor?”,
“Comfortable?”). But when a score of 23 is referenced to a continuous quality scale, it is easy to deduce that the assessed video clip has “poor” quality. Thus, the performance of an objective model can be assessed using the results of subjective tests obtained with exactly the same scale that was used for objective prediction. Also, predicted MOS scores should have a high correlation and reliability with subjective test results.
In the next section a new approach of objective quality assessment is proposed, which characterizes a detected technical quality parameter based on its influence on viewer perception and avoids the direct prediction of MOS with a related quality level to increase reliability and decrease the complexity level.
Based on the above discussion, our motivation is to develop an objective 3D model for the characterization of 3D QoE that fulfills the following characteristics:
• Detected problem is categorized in accordance with human perceptual thresholds.
• Model that predicts category rather than a MOS score.
• Predicted objective scores can be easily validated via a subjective test.