Fast orthogonality deficiency compensation for improved frequency selective image extrapolation

TL;DR

This paper presents a highly efficient algorithm for signal extrapolation in image and video processing, significantly reducing computational complexity while maintaining high-quality results for various image regions.

Contribution

The paper introduces a fast orthogonality deficiency compensation algorithm that improves frequency selective image extrapolation with over 100 times faster processing.

Findings

Achieves high-quality image extrapolation for edges, smooth, and structured areas.

Reduces processing time by a factor greater than 100.

Maintains extrapolation quality despite lower computational complexity.

Abstract

The purpose of this paper is to introduce a very efficient algorithm for signal extrapolation. It can widely be used in many applications in image and video communication, e. g. for concealment of block errors caused by transmission errors or for prediction in video coding. The signal extrapolation is performed by extending a signal from a limited number of known samples into areas beyond these samples. Therefore a finite set of orthogonal basis functions is used and the known part of the signal is projected onto them. Since the basis functions are not orthogonal regarding the area of the known samples, the projection does not lead to the real portion a basis function has of the signal. The proposed algorithm efficiently copes with this non-orthogonality resulting in very good objective and visual extrapolation results for edges, smooth areas, as well as structured areas. Compared to an existent implementation, this algorithm has a significantly lower computational complexity without any degradation in quality. The processing time can be reduced by a factor larger than 100.