TY - GEN
T1 - Method to quality assessment of stereo images
AU - Ma, Jian
AU - An, Ping
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2017/1/4
Y1 - 2017/1/4
N2 - In this paper, we propose a novel full reference image quality assessment model for stereo images. To improve the characteristics of human vision system (HVS) for stereo quality analysis, this model exploits mechanism of the HVS from three aspects: 1) apply contrast sensitivity function (CSF) filtering on the two monocular views. 2) the conventional 2D MAD (most apparent distortion) algorithm is applied on the two monocular views, and then the combined binocular quality is estimated via a weighted sum of the estimates from two stages. In the first stage, the weights are determined based on binocular phase congruency measure. In the second stage, the weights are determined based on a block-based contrast measure. 3) combine the quality from the two stages into a single estimate of overall perceived quality of stereo images. Experimental results on three public benchmark databases show that the proposed model achieves significantly higher consistency with subjective scores. © 2016 IEEE.
AB - In this paper, we propose a novel full reference image quality assessment model for stereo images. To improve the characteristics of human vision system (HVS) for stereo quality analysis, this model exploits mechanism of the HVS from three aspects: 1) apply contrast sensitivity function (CSF) filtering on the two monocular views. 2) the conventional 2D MAD (most apparent distortion) algorithm is applied on the two monocular views, and then the combined binocular quality is estimated via a weighted sum of the estimates from two stages. In the first stage, the weights are determined based on binocular phase congruency measure. In the second stage, the weights are determined based on a block-based contrast measure. 3) combine the quality from the two stages into a single estimate of overall perceived quality of stereo images. Experimental results on three public benchmark databases show that the proposed model achieves significantly higher consistency with subjective scores. © 2016 IEEE.
KW - Human vision system
KW - Images quality assessment
KW - MAD algorithm
KW - Phase congrency
KW - Stereo images
UR - http://www.scopus.com/inward/record.url?scp=85011012844&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85011012844&origin=recordpage
U2 - 10.1109/VCIP.2016.7805563
DO - 10.1109/VCIP.2016.7805563
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781509053162
T3 - VCIP 2016 - 30th Anniversary of Visual Communication and Image Processing
BT - VCIP 2016 - 30th Anniversary of Visual Communication and Image Processing
PB - IEEE
T2 - 2016 IEEE Visual Communication and Image Processing, VCIP 2016
Y2 - 27 November 2016 through 30 November 2016
ER -