Normalized Weighting Schemes for Image Interpolation Algorithms

TL;DR

This paper introduces four normalized geometric weighting schemes for image interpolation, improving the realism and quality of interpolated images across different scaling ratios.

Contribution

The paper proposes four novel normalized weighting schemes based on geometric shapes for image interpolation, enhancing image quality at various scaling ratios.

Findings

HR scheme performs best at small scaling ratios.

AC scheme outperforms others at higher scaling ratios.

Interpolated images with these schemes are comparable or superior to traditional methods.

Abstract

Image interpolation algorithms pervade many modern image processing and analysis applications. However, when their weighting schemes inefficiently generate very unrealistic estimates, they may negatively affect the performance of the end user applications. Therefore, in this work, the author introduced four weighting schemes based on some geometric shapes for digital image interpolation operations. And, the quantity used to express the extent of each shape weight was the normalized area, especially when the sums of areas exceeded a unit square size. The introduced four weighting schemes are based on the minimum side based diameter (MD) of a regular tetragon, hypotenuse based radius (HR), the virtual pixel length based height for the area of the triangle (AT), and the virtual pixel length for hypotenuse based radius for the area of the circle (AC). At the smaller scaling ratio, the image interpolation algorithm based on the HR scheme scored the highest at 66.6 % among non traditional image interpolation algorithms presented. But, at the higher scaling ratio, the AC scheme based image interpolation algorithm scored the highest at 66.6 % among non traditional algorithms presented and, here, its image interpolation quality was generally superior or comparable to the quality of images interpolated by both non traditional and traditional algorithms.