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2.1.Image processing performance metrics
Digital image processing is the use of computer algorithm to enhance the properties of digital images. Digital image processing techniques include preprocessing (filtering), segmentation and classification technique. The effectiveness of these techniques can be estimated using performance metrics. Performance metrics are used to determine the effectiveness of image processing technique in achieving expected results. They are the quantities that are used to compare the performances of different systems. In image processing, there are pre-processing performance metrics, segmentation performance metrics and classification performance metrics depending on the stage the metrics are applied.
2.1.1. Preprocessing Performance Metrics
The effectiveness of a filtering operation is quantified using performance metrics (evaluation). Performance metrics of a filtering operation are the methods applied in measuring the effectiveness of a filter in improving the quality of a noisy image. The most common performance metrics used in quantifying filtering operation are a peak signal to noise ratio (PSNR) and mean square error (MSE) [38]. PSNR and MSE are widely used in performance metrics because they are simple and easy to use.
2.1.1.1.Peak Signal-to-Noise Ratio
The Peak Signal-to-Noise Ratio (PSNR) of an image is the ratio of the maximum power of the signal to the maximum power of the noise distorting the image [39]. The PSNR is measured in decibel.
2.1.1.2.Mean Square Error
The mean square error (MSE) is the average of the squared intensity differences between the filtered image pixels and reference (noiseless) image pixels.The metrics assumes that the reduction in perceptual quality of an image is directly related to the visibility of the error signal [39].
2.1.1.3. PSNR Gain
The PSNR gain of a new filter is the value in which the PSNR of the new filter is more than the PSNR of an existing filter.
When the value of gain is positive, it means that the new filter is better that the existing filter. However, if the gain is negative, the existing filter is better. The gain in performance is measured in decibel.
2.1.1.1.True Acceptance Rate
True Acceptance Rate (TAR) is defined as the percentage of times a system correctly verifies a true claim of identity [42]. A filter whose output has the highest value of TAR when classified has the best performance and higher the value the better the technique.
2.1.1.2.False Acceptance Rate
FAR is defined as the percentage of times a system incorrectly verifies a true claim of identity. A filter whose output has the lowest value of FAR when classified has the best performance and higher the value the better the technique.
2.1.1.3.Pixel Error Rate
Pixel Error Rate (PERR) is defined as the percentage of a pixel error in the filtered image with respect to the total number of pixels in the noiseless image. The pixel error is the difference in the number of black pixels in the noiseless image and filtered image after both are converted to binary images. It can also be defined as the total number of pixels in the out image that have the wrong colour. Pixel error is the difference between the number of black pixels in a noiseless image and the number of black pixels in a filtered image. The parameter M and N are the row size and column size of the image respectively. A classification technique with the lowest value of PERR has the best performance and lower the value the better the technique.
2.1.1.4.Recognition Accuracy
Recognition accuracy (RA) is the accuracy with which all the features in an image are recognized. A filter whose output has the highest value of RA when classified has the best performance and higher the value the better the technique.