TY - GEN
T1 - Implementation and evaluate the no-reference image quality assessment based on spatial and spectral entropies on the different image quality databases
AU - Ali, Mohammed Abdalla Abdelrahim
AU - Deriche, Mohamed A.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/8/31
Y1 - 2015/8/31
N2 - The mainstream approach to image quality assessment has centered around accurately modeling the single most relevant strategy employed by the human visual system (HVS) when judging image quality. In this work, it suggest that a single strategy used for single database may not be sufficient; rather, The no-reference/blind image quality assessment (NR-IQA) is the most difficult due to the reference images are not available. Spatial-Spectral Entropy-based Quality (SSEQ) index has been proven successful in image modeling and feature extraction. However, it have been improve the general-purpose no-reference (NR) image quality assessment (IQA) model that utilizes local spatial and spectral entropy features used and their relevance to perception and thoroughly evaluate the algorithm on another databases than LIVE IQA database (applied to another three databases of subjective image quality: 1. The TID database, 2. the Toyama database, and 3. the Categorical Subjective Image Quality -CSIQ- database.). Need find that SSEQ matches well with human subjective opinions of image quality, and is statistically superior to the full-reference (FR) IQA algorithm SSIM and several top.
AB - The mainstream approach to image quality assessment has centered around accurately modeling the single most relevant strategy employed by the human visual system (HVS) when judging image quality. In this work, it suggest that a single strategy used for single database may not be sufficient; rather, The no-reference/blind image quality assessment (NR-IQA) is the most difficult due to the reference images are not available. Spatial-Spectral Entropy-based Quality (SSEQ) index has been proven successful in image modeling and feature extraction. However, it have been improve the general-purpose no-reference (NR) image quality assessment (IQA) model that utilizes local spatial and spectral entropy features used and their relevance to perception and thoroughly evaluate the algorithm on another databases than LIVE IQA database (applied to another three databases of subjective image quality: 1. The TID database, 2. the Toyama database, and 3. the Categorical Subjective Image Quality -CSIQ- database.). Need find that SSEQ matches well with human subjective opinions of image quality, and is statistically superior to the full-reference (FR) IQA algorithm SSIM and several top.
KW - Image quality assessment
KW - No-reference
KW - Spatial entropy
KW - Spectral entropy
KW - Support vector machine
UR - https://www.scopus.com/pages/publications/84960483152
U2 - 10.1109/ICoICT.2015.7231420
DO - 10.1109/ICoICT.2015.7231420
M3 - Conference contribution
AN - SCOPUS:84960483152
T3 - 2015 3rd International Conference on Information and Communication Technology, ICoICT 2015
SP - 188
EP - 194
BT - 2015 3rd International Conference on Information and Communication Technology, ICoICT 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Information and Communication Technology, ICoICT 2015
Y2 - 27 May 2015 through 29 May 2015
ER -