Feature Keypoint-Based Image Compression Technique Using a Well-Posed Nonlinear Fourth-Order PDE-Based Model

A digital image compression framework based on nonlinear partial differential equations (PDEs) is proposed in this research article. First, a feature keypoint-based sparsification algorithm is proposed for the image coding stage. The interest keypoints corresponding to various scale-invariant image...

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Bibliographic Details
Published in:	Mathematics (Basel) Vol. 8; no. 6; p. 930
Main Author:	Barbu, Tudor
Format:	Journal Article
Language:	English
Published:	MDPI AG 01.06.2020
Subjects:	image sparsification lossy image (de)compression nonlinear PDE-based structural inpainting scale-invariant feature keypoint scattered data point interpolation well-posed PDE model
ISSN:	2227-7390, 2227-7390
Online Access:	Get full text
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Summary:	A digital image compression framework based on nonlinear partial differential equations (PDEs) is proposed in this research article. First, a feature keypoint-based sparsification algorithm is proposed for the image coding stage. The interest keypoints corresponding to various scale-invariant image feature descriptors, such as SIFT, SURF, MSER, ORB, and BRIEF, are extracted, and the points from their neighborhoods are then used as sparse pixels and coded using a lossless encoding scheme. An effective nonlinear fourth-order PDE-based scattered data interpolation is proposed for solving the decompression task. A rigorous mathematical investigation of the considered PDE model is also performed, with the well-posedness of this model being demonstrated. It is then solved numerically by applying a consistent finite difference method-based numerical approximation algorithm that is next successfully applied in the image compression and decompression experiments, which are also discussed in this work.
ISSN:	2227-7390 2227-7390
DOI:	10.3390/math8060930