2019 Fiscal Year Annual Research Report
Modeling the Perceptual Underpinnings for Quality Assessment of Restored Textures
| Project/Area Number |
17K00232
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| Research Institution | Shizuoka University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
大橋 剛介 静岡大学, 工学部, 教授 (80293603)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Keywords | quality assessment / texture quality / visual perception / visual masking / image restoration / machine learning / big data |
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| Outline of Annual Research Achievements |
The objective of this research project is to investigate the perceptual foundations of restored textures, to research and develop associated
computational models, and to research and develop practical QA algorithms for restoration applications. In Year 3 of this project, the objective
was to develop and apply an automatic RTQA algorithm for restoration. This objective was achieved via machine learning. Specifically, object classification was used to restore compressed texture regions by using texture information from different images containing objects from the same category. In addition, the techniques were used to develop a machine-learning-based blind QA algorithm for multiply distorted images.
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