TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Image Quality Assessment by Combining Masking Texture and Perceptual Color Difference Model |
| 영문제목(English Title) |
Image Quality Assessment by Combining Masking Texture and Perceptual Color Difference Model |
| 저자(Author) |
Zhisen Tang
Yuanlin Zheng
Wei Wang
Kaiyang Liao
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| 원문수록처(Citation) |
VOL 14 NO. 07 PP. 2938 ~ 2956 (2020. 07) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Objective image quality assessment (IQA) models have been developed by effective features to imitate the characteristics of human visual system (HVS). Actually, HVS is extremely sensitive to color degradation and complex texture changes. In this paper, we firstly reveal that many existing full reference image quality assessment (FR-IQA) methods can hardly measure the image quality with contrast and masking texture changes. To solve this problem, considering texture masking effect, we proposed a novel FR-IQA method, called Texture and Color Quality Index (TCQI). The proposed method considers both in the masking effect texture and color visual perceptual threshold, which adopts three kinds of features to reflect masking texture, color difference and structural information. Furthermore, random forest (RF) is used to address the drawbacks of existing pooling technologies. Compared with other traditional learning-based tools (support vector regression and neural network), RF can achieve the better prediction performance. Experiments conducted on five large-scale databases demonstrate that our approach is highly consistent with subjective perception, outperforms twelve the state-of-the-art IQA models in terms of prediction accuracy and keeps a moderate computational complexity. The cross database validation also validates our approach achieves the ability to maintain high robustness.
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| 키워드(Keyword) |
Image quality assessment
full-reference
masking texture
color difference
human visual system
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| 파일첨부 |
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