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Title page for etd-0903114-121514


URN etd-0903114-121514 Statistics This thesis had been viewed 960 times. Download 0 times.
Author Pin-yuan Hsieh
Author's Email Address No Public.
Department Communication Engineering
Year 2013 Semester 2
Degree Master Type of Document Master's Thesis
Language zh-TW.Big5 Chinese Page Count 56
Title COLOR IMAGE QUALITY ASSESSMENT BASED ON BINARY FEATURES OBTAINED FROM K-MEANS CLUSTERING CLASSIFICATION
Keyword
  • Image quality assessment
  • Structural Similarity measure for color image
  • K-means clustering
  • K-means clustering
  • Structural Similarity measure for color image
  • Image quality assessment
  • Abstract Most of image quality assessment (IQA) methods only concern about gray image, and don’t make use of image color information sufficiently at present. A method for reduced-reference color image quality assessment is proposed, which based on structural features and efficiently uses the color information. The structure information inherent in three-dimensional (3D) color signals can be obtained from K-means clustering classification. The final image quality assessment is defined by the weighted combination of three components. To verify the validity of the proposed metric is evaluated against a large amount of test images in LIVE database and compared with that of the famous Structural Similarity Measure for Color Image (CMSSIM). The experiments show that the proposed objective method has a good coincidence with the subjective perception, and can reflect the image quality effectively.
    Advisor Committee
  • Chun-Hsien Chou - advisor
  • Kuo-cheng Liu - co-chair
  • Ray-chin Wu - co-chair
  • Files indicate in-campus access at 5 years and off-campus not accessible
    Date of Defense 2014-07-25 Date of Submission 2014-09-03


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