Design of Low-Artifact Interpolation Kernels by Means of Computer Algebra
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Abstract (arXiv)
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.
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References · 6
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 2020Image Quality Assessment: Unifying Structure and Texture Similaritycited by 792
- 2013Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Indexcited by 193
- 2018PieAPP: Perceptual Image-Error Assessment through Pairwise Preferencecited by 158
- 2016A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessmentcited by 57
- 2016Mean Deviation Similarity Index: Efficient and Reliable Full-Reference Image Quality Evaluatorcited by 32
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