TY - GEN
T1 - 3D image quality estimation (ANN) based on depth/disparity and 2D metrics
AU - Kukolj, Dragan
AU - Dordevic, Dragana
AU - Okolisan, David
AU - Ostojic, Ivana
AU - Sandic-Stankovic, Dragana
AU - Hewage, Chaminda
PY - 2014/1/9
Y1 - 2014/1/9
N2 - Immersive image/video services will be soon available to the mass market due to the technological advancement of 3D video technologies, which include 3D-Ready TV monitors at affordable prices. However, in order to provide demanding customers with a better service over resource limited (e.g., bandwidth) and unreliable communication channels, system parameters need to be changed 'on the fly'. Measured 3D video quality can be used as feedback information to fine tune the system parameters. The main aim of this paper is to analyze and present impact of objective image quality assessment metrics on perception of 3D image/video. Neural Network statistical estimator was used to examine the correlation between objective measures on input image base and Differential Mean Opinion Score (DMOS) of used image base. For this purpose part of LIVE 3D Image Quality Database [7] was used. The results suggest that comparison of the neural network DMOS estimators based on full-reference and no-reference objective metrics shown very similar behavior and accuracy.
AB - Immersive image/video services will be soon available to the mass market due to the technological advancement of 3D video technologies, which include 3D-Ready TV monitors at affordable prices. However, in order to provide demanding customers with a better service over resource limited (e.g., bandwidth) and unreliable communication channels, system parameters need to be changed 'on the fly'. Measured 3D video quality can be used as feedback information to fine tune the system parameters. The main aim of this paper is to analyze and present impact of objective image quality assessment metrics on perception of 3D image/video. Neural Network statistical estimator was used to examine the correlation between objective measures on input image base and Differential Mean Opinion Score (DMOS) of used image base. For this purpose part of LIVE 3D Image Quality Database [7] was used. The results suggest that comparison of the neural network DMOS estimators based on full-reference and no-reference objective metrics shown very similar behavior and accuracy.
KW - Image Quality Assessment
KW - Mean Opinion Score
KW - Objective Image Quality Assessment
KW - Objective Metric
UR - http://www.scopus.com/inward/record.url?scp=84893809085&partnerID=8YFLogxK
U2 - 10.1109/CINTI.2013.6705177
DO - 10.1109/CINTI.2013.6705177
M3 - Conference contribution
AN - SCOPUS:84893809085
SN - 9781479901975
T3 - CINTI 2013 - 14th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings
SP - 125
EP - 130
BT - CINTI 2013 - 14th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings
T2 - 14th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2013
Y2 - 19 November 2013 through 21 November 2013
ER -