Two kinds of parametric piecewise rational interpolation kernels for image magnification

TL;DR

This paper introduces new piecewise rational interpolation kernels for image magnification, demonstrating improved performance over traditional cubic kernels in key image quality metrics.

Contribution

The paper presents novel rational cubic/linear and five quartic/linear interpolation kernels with symmetry, smoothness, and approximation properties, including special cases and performance improvements.

Findings

One quartic/linear kernel outperforms cubic in PSNR, SSIM, FSIM

All kernels are symmetric and C^1 continuous

Proposed kernels include known cases as special instances

Abstract

We study the constructions of piecewise rational interpolation kernels that are supported on the interval $[-2,2]$, and present one novel rational cubic/linear and five quartic/linear interpolation kernels. All proposed kernels are symmetric, $C^1$ continuous, and possess certain degrees of approximation order. The proposed quartic/linear interpolation kernels include the cubic and the cubic/linear interpolation kernel as special cases. Our numerical results show that one of the quartic/linear interpolation kernels can outperform the cubic interpolation kernel in terms of PSNR, SSIM, and FSIM.