Physics Journal
Articles Information
Physics Journal, Vol.1, No.2, Sep. 2015, Pub. Date: Aug. 27, 2015
Quality Measurement of Blurred Images Using NMSE and SSIM Metrics in HSV and RGB Color Spaces
Pages: 105-111 Views: 3289 Downloads: 1258
[01] Ahmed Majeed Hameed, Al-Safwa University College, Department of Computer Technics, Karbala, Iraq.
[02] Moaz H. Ali, Al-Safwa University College, Department of Computer Technics, Karbala, Iraq.
[03] Ramla Abdulnabi Abdulzahra, Al-Safwa University College, Department of Computer Technics, Karbala, Iraq.
Quality measurement is the process of measuring distortion in images, by using some metrics that makes a comparison between the original pure image and the distorted image. Image quality measurement is important and helpful for many applications such as in medicine and space images because images can be affected with many factors of distortions. It is used the Normalize Mean Square Error (NMSE) and the Structural Similarity Index Measurement (SSIM) as a metric to measure the quality of distorted images. Gaussian blurring is the type of distortion which is used, so this distortion is applied manually on four color images using Gaussian blurring function. The distortion is applied on images in the red, green, blue (RGB) and Hue, Saturation, Value (HSV) color spaces. The result is shown that in the (HSV) the achromatic components have been affected strongly by blurring than chromatic components, but in the (RGB) colors and lightness are affected similarly because of the high interdependence between lightness and colors in RGB color space. Experimental results show that in HSV color space there is a high separation between chromatic and achromatic components, where achromatic component has been affected strongly with blur distortion than chromatic components. Also, results of RGB shows a high correlation between chromatic and achromatic components, where these components were identically affected with blur distortion.
Blurring, NMSE, SSIM, HSV, RGB, Gaussian Blurring, Image Quality
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