TY - GEN
T1 - A New Approach of Image Quality Index
AU - Islam, Sheikh Md. Rabiul
AU - Islam, Md. Tariqul
AU - Huang, Xu
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Image quality measurement procedures the quality of images automatically constructed on subjective evaluation. The smallest element of an image is pixel, can be converted to structure based evaluation using structure similarity index. We are proposed a novel modified feature similarity index (MFSIM) for image quality assessment (IQA) in this paper. This index used three features of an image namely Phase Congruency (PC), Gradient Magnitude (GM) and Angle (theta). The primary feature of MFSIM is PC which is contrast in variant system and measure the local structure of images. GM is the secondary feature which plays a complementary role. The angle of an image uses as tertiary feature of MFSIM. For this index need weighting function for evaluating of features and PC act as weighting function. All the task performs using five databases. The result of this proposed method shows higher rank-order correlation, low MSE than any other method like human visual system (HVS).
AB - Image quality measurement procedures the quality of images automatically constructed on subjective evaluation. The smallest element of an image is pixel, can be converted to structure based evaluation using structure similarity index. We are proposed a novel modified feature similarity index (MFSIM) for image quality assessment (IQA) in this paper. This index used three features of an image namely Phase Congruency (PC), Gradient Magnitude (GM) and Angle (theta). The primary feature of MFSIM is PC which is contrast in variant system and measure the local structure of images. GM is the secondary feature which plays a complementary role. The angle of an image uses as tertiary feature of MFSIM. For this index need weighting function for evaluating of features and PC act as weighting function. All the task performs using five databases. The result of this proposed method shows higher rank-order correlation, low MSE than any other method like human visual system (HVS).
KW - Phase Congruency(PC)
KW - Gradient Magnitude (GM)
KW - Angle
KW - linage Quality Assessment (IQA)
KW - Image Quality Assessment (IQA)
UR - http://www.scopus.com/inward/record.url?scp=85047773504&partnerID=8YFLogxK
UR - https://icaeeiub.net/icaee2017.php
U2 - 10.1109/ICAEE.2017.8255357
DO - 10.1109/ICAEE.2017.8255357
M3 - Conference contribution
SN - 9781538608708
T3 - 4th International Conference on Advances in Electrical Engineering, ICAEE 2017
SP - 223
EP - 228
BT - Proceedings of the 2017 4th International Conference on Advances in Electrical Engineering (ICAEE)
A2 - Chowdhury, Mustafa Habib
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - United States
T2 - 4th International Conference on Advances in Electrical Engineering (ICAEE 2017)
Y2 - 28 September 2017 through 30 September 2017
ER -