Orthogonality Deficiency Compensation for Improved Frequency Selective Image Extrapolation

Abstract

This paper describes a very efficient algorithm for image signal extrapolation. It can be used for various applications in image and video communication, e.g. the concealment of data corrupted by transmission errors or prediction in video coding. The extrapolation is performed on a limited number of known samples and extends the signal beyond these samples. Therefore the signal from the known samples is iteratively projected onto different basis functions in order to generate a model of the signal. As the basis functions are not orthogonal with respect to the area of the known samples we propose a new extension, the orthogonality deficiency compensation, to cope with the non-orthogonality. Using this extension, very good extrapolation results for structured as well as for smooth areas are achievable. This algorithm improves PSNR up to 2 dB and gives a better visual quality for concealment of block losses compared to extrapolation algorithms existent so far.

Cited by in corpus (3)

• Resampling Images to a Regular Grid from a Non-Regular Subset of Pixel Positions Using Frequency Selective Reconstruction
• Increasing Imaging Resolution by Non-Regular Sampling and Joint Sparse Deconvolution and Extrapolation
• A Fast Algorithm for Selective Signal Extrapolation with Arbitrary Basis Functions