TY - GEN
T1 - Structure-preserving Image Quality Assessment
AU - Wang, Yilin
AU - Zhang, Qiang
AU - Li, Baoxin
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/8/4
Y1 - 2015/8/4
N2 - Perceptual Image Quality Assessment (IQA) has many applications. Existing IQA approaches typically work only for one of three scenarios: full-reference, non-reference, or reduced-reference. Techniques that attempt to incorporate image structure information often rely on hand-crafted features, making them difficult to be extended to handle different scenarios. On the other hand, objective metrics like Mean Square Error (MSE), while being easy to compute, are often deemed ineffective for measuring perceptual quality. This paper presents a novel approach to perceptual quality assessment by developing an MSE-like metric, which enjoys the benefit of MSE in terms of inexpensive computation and universal applicability while allowing structural information of an image being taken into consideration. The latter was achieved through introducing structure-preserving kernelization into a MSE-like formulation. We show that the method can lead to competitive FR-IQA results. Further, by developing a feature coding scheme based on this formulation, we extend the model to improve the performance of NR-IQA methods. We report extensive experiments illustrating the results from both our FR-IQA and NR-IQA algorithms with comparison to existing state-of-the-art methods.
AB - Perceptual Image Quality Assessment (IQA) has many applications. Existing IQA approaches typically work only for one of three scenarios: full-reference, non-reference, or reduced-reference. Techniques that attempt to incorporate image structure information often rely on hand-crafted features, making them difficult to be extended to handle different scenarios. On the other hand, objective metrics like Mean Square Error (MSE), while being easy to compute, are often deemed ineffective for measuring perceptual quality. This paper presents a novel approach to perceptual quality assessment by developing an MSE-like metric, which enjoys the benefit of MSE in terms of inexpensive computation and universal applicability while allowing structural information of an image being taken into consideration. The latter was achieved through introducing structure-preserving kernelization into a MSE-like formulation. We show that the method can lead to competitive FR-IQA results. Further, by developing a feature coding scheme based on this formulation, we extend the model to improve the performance of NR-IQA methods. We report extensive experiments illustrating the results from both our FR-IQA and NR-IQA algorithms with comparison to existing state-of-the-art methods.
KW - Image Quality Assessment
KW - Mean Square Error
KW - kernel method
UR - http://www.scopus.com/inward/record.url?scp=84946098341&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84946098341&partnerID=8YFLogxK
U2 - 10.1109/ICME.2015.7177436
DO - 10.1109/ICME.2015.7177436
M3 - Conference contribution
AN - SCOPUS:84946098341
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
BT - 2015 IEEE International Conference on Multimedia and Expo, ICME 2015
PB - IEEE Computer Society
T2 - IEEE International Conference on Multimedia and Expo, ICME 2015
Y2 - 29 June 2015 through 3 July 2015
ER -