Abstract
Scaling images by interpolation is a common operation in image processing. Since the interpolation kernels used are nite, they cannot not be bandlimited, and we have aliasing effects. Two effects usually appear and cause a noticeable degradation in quality of the image. The rst is jagged edges and the second is low frequency modulation of high frequency components such as the sampling noise. Both effects result from aliasing. The classical analysis of the aliasing nds the energy of the aliased components in the interpolated signal assuming the input signal is a white wide sense stationary signal. We use a polyphase analysis of interpolation to explain the aliasing effects. We dene the Normalized Aliasing Index for measuring the aliasing expected from an interpolation lter . This measure enables a fair comparison of interpolation lters. We then show that the classical analysis is identical to the polyphase one when an appropriate equalizer is used in series to the interpolation lter .
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