Astatistical performance in dicator in some image processing problems / Chang Yun Fah - Chang, Yun Fah (2012)
The ability to compare or relate two digital images may be useful in developing performance evaluation algorithms. This thesis investigates the use of a particular correlation measure, 2 p R developed from the multidimensional unreplicated linear functional relationship (MULFR) model with single slope, as a measure or indicator of performance. This MULFR model is an extended version of the ULFR model introduced by Adcock in 1877. A literature survey was carried out showing that 2 p R has not been used before. The coefficient 2 p R was investigated in its ability to handle the issues of non-perfect reference image, multiple image attributes and combining image local-global information simultaneously. This survey is followed with the maximum likelihood estimation of parameters and a brief discussion of some theoretical properties of 2 p R . To investigate robust properties of 2 p R , an extensive simulation exercise was then carried out. Promising results, thus far, motivate the use of 2 p R in two image analysis problems; firstly a character recognition problem and secondly a particular data compression problem. In a handwritten Chinese character recognition problem, the 2 p R achieved the highest recognition rates even the pre-processing stage is removed from the recognition system. A substantial reduction of processing time, approximately 40.36% to 75.31%, was achieved using 2 p R . In JPEG compression problem, 2 p R is used as a measure of image quality which in turn indicates the performance of the compression method. It is shown that 2 p R performs well and satisfies the monotonicity, accuracy and consistency properties when perfect reference image was used. 2 p R was also shown to perform better than some frequently used similarity measures when imperfect reference image was used.