Abstract
In this paper, a set of techniques used for downsampling and upsampling of 2D images is analyzed on various image datasets. The comparison takes into account a significant number of interpolation kernels, their parameters, and their algebraical form, focusing mostly on linear interpolation methods with symmetric kernels. The most suitable metrics for measuring the performance of upsampling and downsampling filters’ combinations are presented, discussing their strengths and weaknesses. A test benchmark is proposed, and the obtained results are analyzed with respect to the presented metrics, offering explanations about specific filter behaviors in general, or just in certain circumstances. In the end, a set of filters and parameters recommendations is offered based on extensive testing on carefully selected image datasets. The entire research is based on the study of a large set of research papers and on a solid discussion of the underlying signal processing theory.
Highlights
Downsampling of 2D images is a technique employed in order to reduce the resolution of an input image
We describe the basics of polynomial interpolation
We focus on linear interpolation filters, which represent an efficient approach towards image upsampling, providing good trade-offs between quality and computational complexity
Summary
Downsampling of 2D images is a technique employed in order to reduce the resolution of an input image. This is most helpful for reducing the storage size of images while preserving as much of their information as possible. Upsampling is the reverse process of the former, and it consists of obtaining an output image of a higher resolution than that of the input. The problem referenced throughout this paper is known as interpolation, and it resides in inferring a continuous function from a discrete set of values. The interpolation problem is not a new one. Meijering [1] describes the history of interpolation techniques from the Babylonian astronomers up to the modern adaptive and content-aware methods
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