TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
No-reference Image Quality Assessment With A Gradient-induced Dictionary |
| 영문제목(English Title) |
No-reference Image Quality Assessment With A Gradient-induced Dictionary |
| 저자(Author) |
Leida Li
Dong Wu
Jinjian Wu
Jiansheng Qian
Beijing Chen
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| 원문수록처(Citation) |
VOL 10 NO. 01 PP. 0288 ~ 0307 (2016. 01) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Image distortions are typically characterized by degradations of structures. Dictionaries learned from natural images can capture the underlying structures in images, which are important for image quality assessment (IQA). This paper presents a general-purpose no-reference image quality metric using a GRadient-Induced Dictionary (GRID). A dictionary is first constructed based on gradients of natural images using K-means clustering. Then image features are extracted using the dictionary based on Euclidean-norm coding and max-pooling. A distortion classification model and several distortion-specific quality regression models are trained using the support vector machine (SVM) by combining image features with distortion types and subjective scores, respectively. To evaluate the quality of a test image, the distortion classification model is used to determine the probabilities that the image belongs to different kinds of distortions, while the regression models are used to predict the corresponding distortion-specific quality scores. Finally, an overall quality score is computed as the probability-weighted distortion-specific quality scores. The proposed metric can evaluate image quality accurately and efficiently using a small dictionary. The performance of the proposed method is verified on public image quality databases. Experimental results demonstrate that the proposed metric can generate quality scores highly consistent with human perception, and it outperforms the state-of-the-arts.
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| 키워드(Keyword) |
Image quality assessment
no-reference
dictionary
K-means clustering
SVM
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| 파일첨부 |
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