Design of Low-Artifact Interpolation Kernels by Means of Computer Algebra

TL;DR

This paper introduces new piecewise-polynomial kernels for image interpolation, optimized to reduce artifacts using symbolic computation, and demonstrates their superior performance over existing methods through extensive quality assessments.

Contribution

The paper presents a novel symbolic approach to designing low-artifact interpolation kernels with improved image quality outcomes.

Findings

Kernels outperform existing linear interpolators in artifact reduction

Optimization based on anisotropic artifact measures improves image quality

Symbolic kernel design enables flexible and effective interpolation solutions

Abstract

We present a number of new piecewise-polynomial kernels for image interpolation. The kernels are constructed by optimizing a measure of interpolation quality based on the magnitude of anisotropic artifacts. The kernel design process is performed symbolically using Mathematica computer algebra system. Experimental evaluation involving 14 image quality assessment methods demonstrates that our results compare favorably with the existing linear interpolators.