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Conversion artifacts

Cardboard effect is a phenomenon in which 3D objects located at different depths appear flat to the audience, as if they were made of cardboard, while the relative depth between the objects is preserved
Edge sharpness mismatch - this artifact may appear due to a blurred depth map at the boundaries of objects. The border becomes precise in one view and blurred in another. The edge-sharpness mismatch artifact is typically caused by the following:
- Use of a “rubber sheet” technique, defined as warping the pixels surrounding the occlusion regions to avoid explicit occlusion filling. In such cases, the edges of the displacement map are blurred and the transition between foreground and background regions is smoothed. The region occupied by edge/motion blur is either “stretched” or “tucked,” depending on the direction of object displacement. Naturally, this approach leads to mismatches in edge sharpness between the views.
- Lack of proper treatment of semitransparent edges, potentially resulting in edge doubling or ghosting.
- Simple occlusion-filling techniques leading to stretching artifacts near object edges.

Stuck to background objects - this error of "sticking" foreground objects to the background

3D quality metrics

PQM

PQM^[1] mimic the HVS as the results obtained aligns very closely to the Mean Opinion Score (MOS) obtained from subjective tests. The PQM quantifies the distortion in the luminance, and contrast distortion using an approximation (variances) weighted by the mean of each pixel block to obtain the distortion in an image. This distortion is subtracted from 1 to obtain the objective quality score.

HV3D

HV3D^[2] quality metric has been designed having the human visual 3D perception in mind. It takes into account the quality of the individual right and left views, the quality of the cyclopean view (the fusion of the right and left view, what the viewer perceives), as well as the quality of the depth information.

VQMT3D

The VQMT3D project ^[3] includes several developed metrics for evaluating the quality of 2D to 3D conversion

Metric	Class	Type	Applicable to
Cardboard effect	Advanced	Qualitative	2D-to-3D conversion
Edge-sharpness mismatch	Unique	Qualitative	2D-to-3D conversion
Stuck-to-background objects	Unique	Qualitative	2D-to-3D conversion
Comparison with the 2D version	Unique	Qualitative	2D-to-3D conversion

^ Joveluro, P.; Malekmohamadi, H.; Fernando, W. A. C; Kondoz, A. M. (2010). Perceptual Video Quality Metric for 3D video quality assessment. IEEE. doi:10.1109/3dtv.2010.5506331.
^ Banitalebi-Dehkordi, Amin; Pourazad, Mahsa T.; Nasiopoulos, Panos (2013). 3D video quality metric for 3D video compression. IEEE. doi:10.1109/ivmspw.2013.6611930.
^ VQMT3D

[Joveluro_Malekmohamadi_Fernando_Kondoz_2010_p.-1] Joveluro, P.; Malekmohamadi, H.; Fernando, W. A. C; Kondoz, A. M. (2010). Perceptual Video Quality Metric for 3D video quality assessment. IEEE. doi:10.1109/3dtv.2010.5506331.

[Banitalebi-Dehkordi_Pourazad_Nasiopoulos_2013_p.-2] Banitalebi-Dehkordi, Amin; Pourazad, Mahsa T.; Nasiopoulos, Panos (2013). 3D video quality metric for 3D video compression. IEEE. doi:10.1109/ivmspw.2013.6611930.

[VQMT3D-3] VQMT3D

[1]

[2]

[3]

@@ Line 12: / Line 12: @@
 ==3D quality metrics==
 ===PQM===
-This metric<ref name="Joveluro Malekmohamadi Fernando Kondoz 2010 p. ">{{cite conference | last=Joveluro | first=P. | last2=Malekmohamadi | first2=H. | last3=Fernando | first3=W. A. C | last4=Kondoz | first4=A. M. | title=Perceptual Video Quality Metric for 3D video quality assessment | publisher=IEEE | year=2010 | doi=10.1109/3dtv.2010.5506331 | page=}}</ref> mimic the HVS as the results obtained aligns very closely to the Mean Opinion Score (MOS) obtained from subjective tests. The PQM quantifies the distortion in the luminance, and contrast distortion using an approximation (variances) weighted by the mean of each pixel block to obtain the distortion in an image. This distortion is subtracted from 1 to obtain the objective quality score.
+PQM<ref name="Joveluro Malekmohamadi Fernando Kondoz 2010 p. ">{{cite conference | last=Joveluro | first=P. | last2=Malekmohamadi | first2=H. | last3=Fernando | first3=W. A. C | last4=Kondoz | first4=A. M. | title=Perceptual Video Quality Metric for 3D video quality assessment | publisher=IEEE | year=2010 | doi=10.1109/3dtv.2010.5506331 | page=}}</ref> mimic the HVS as the results obtained aligns very closely to the Mean Opinion Score (MOS) obtained from subjective tests. The PQM quantifies the distortion in the luminance, and contrast distortion using an approximation (variances) weighted by the mean of each pixel block to obtain the distortion in an image. This distortion is subtracted from 1 to obtain the objective quality score.
 ===HV3D===
 HV3D<ref name="Banitalebi-Dehkordi Pourazad Nasiopoulos 2013 p. ">{{cite conference | last=Banitalebi-Dehkordi | first=Amin | last2=Pourazad | first2=Mahsa T. | last3=Nasiopoulos | first3=Panos | title=3D video quality metric for 3D video compression | publisher=IEEE | year=2013 | doi=10.1109/ivmspw.2013.6611930 | page=}}</ref> quality metric has been designed having the human visual 3D perception in mind. It takes into account the quality of the individual right and left views, the quality of the cyclopean view (the fusion of the right and left view, what the viewer perceives), as well as the quality of the depth information.