TY - GEN
T1 - Sparse Structural Similarity for Objective Image Quality Assessment
AU - Zhang, Xiang
AU - Wang, Shiqi
AU - Gu, Ke
AU - Jiang, Tingting
AU - Ma, Siwei
AU - Gao, Wen
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/12
Y1 - 2016/1/12
N2 - In this paper, a novel full-reference (FR) image quality assessment (IQA) metric based on sparse representation is proposed. Sparse representation has been widely applied in many applications such as image denoising and restoration. It is a high-efficiency way in representing sparse and redundant natural images. Also it has been shown to be highly related to the human visual perception, which is characterized by a set of responses of neurons in visual cortex. In this paper, the sparse representation is applied in decomposing natural images into multiple layers depending on the visual importance. Inspired by these observations, a novel IQA metric called sparse structural similarity is proposed by measuring the fidelity of the stimulation of visual cortices. Experimental results on public databases indicate that the proposed method is effective in predicting subjective evaluation and as compared to state-of-The-Art FR-IQA methods.
AB - In this paper, a novel full-reference (FR) image quality assessment (IQA) metric based on sparse representation is proposed. Sparse representation has been widely applied in many applications such as image denoising and restoration. It is a high-efficiency way in representing sparse and redundant natural images. Also it has been shown to be highly related to the human visual perception, which is characterized by a set of responses of neurons in visual cortex. In this paper, the sparse representation is applied in decomposing natural images into multiple layers depending on the visual importance. Inspired by these observations, a novel IQA metric called sparse structural similarity is proposed by measuring the fidelity of the stimulation of visual cortices. Experimental results on public databases indicate that the proposed method is effective in predicting subjective evaluation and as compared to state-of-The-Art FR-IQA methods.
KW - Image quality assessment (IQA)
KW - orthogonal matching pursuit (OMP)
KW - sparse representation
UR - https://www.scopus.com/pages/publications/84963750152
U2 - 10.1109/SMC.2015.276
DO - 10.1109/SMC.2015.276
M3 - 会议稿件
AN - SCOPUS:84963750152
T3 - Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
SP - 1561
EP - 1566
BT - Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
Y2 - 9 October 2015 through 12 October 2015
ER -