Score Metrics For Reference-Based And Blind Blur Estimation
| dc.contributor.author | Bong, David Boon Liang | |
| dc.date.accessioned | 2017-10-30T07:22:44Z | |
| dc.date.available | 2017-10-30T07:22:44Z | |
| dc.date.issued | 2014-07 | |
| dc.description.abstract | Blur artifact is a type of common distortion for digital images. The presence of blur artifact in image is an annoyance to image viewers, and affects the perceived quality of the image. Telecommunication service providers and imaging product manufacturers are interested in knowing this perceived quality for the purpose of process and product improvement. However, quality feedback from the image viewers is tedious, expensive and has to be done in compliance with the standards for subjective evaluation such as the ITU-R BT. 500 standard. On the other hand, automatic assessment does not need to comply with any laboratory standard because the assessment is performed by computation method without using human observers. The automatic assessment is an objective estimation to predict the degree of blurriness of an image. Objective estimation can be categorized to the reference-based and blind methods. Reference-based estimation uses partial or full information of the original image, and compares it to the blur image. Blur score is derived from the difference between the original and test images. On the other hand, blind estimation predicts the degree of blurriness from the test image alone. Classical methods for blur estimation such as mean squared errors do not produce high correlation, especially for blur distortion. Hence, there is a need to have new assessment method to measure blurriness effectively. In this research, a method for reference-based estimation is proposed by using the contraction of the local contrast masses. Local contrast is determined from the luminance histogram, and the differences of the local contrast masses are used for the derivation of blur scores. The proposed method is further improved as a blind method by using mathematical knowledge of double Gaussian convolution. The minimum of the Gaussian convolution is used to create valid Gaussian reblur image. From this reblur process, new blind blur score is created together with the contraction value of local contrast masses. Both of the proposed methods are performed in the spatial domain without data transformation, conversion or filtering. In addition, prior training is not required at all for both methods. Simulations were carried out to check the performances of both methods. Simulation results show high correlation of 0.9476 ~ 0.9888 between the proposed blur scores with respect to image blurriness. Comprehensive applications and correlation analysis were performed by using three image databases. Application results show Pearson correlation of 0.8429 ~ 0.9652 for the new blur scores. For most applications, the proposed methods are also shown to be statistically more significant than other methods from the same category. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/5168 | |
| dc.language.iso | en | en_US |
| dc.publisher | Universiti Sains Malaysia | en_US |
| dc.subject | Blur artifact is a type of | en_US |
| dc.subject | common distortion for digital images | en_US |
| dc.title | Score Metrics For Reference-Based And Blind Blur Estimation | en_US |
| dc.type | Thesis | en_US |
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