Frequency selective extrapolation with residual filtering for image error concealment

TL;DR

This paper introduces a modified frequency selective extrapolation method for image error concealment that incorporates the low-pass nature of natural images, resulting in improved reconstruction quality.

Contribution

It proposes a new FSE approach that models the low-pass characteristic of natural images, enhancing error concealment performance.

Findings

Significant PSNR gains over traditional FSE

Improved image reconstruction quality

Effective error concealment in image communication

Abstract

The purpose of signal extrapolation is to estimate unknown signal parts from known samples. This task is especially important for error concealment in image and video communication. For obtaining a high quality reconstruction, assumptions have to be made about the underlying signal in order to solve this underdetermined problem. Among existent reconstruction algorithms, frequency selective extrapolation (FSE) achieves high performance by assuming that image signals can be sparsely represented in the frequency domain. However, FSE does not take into account the low-pass behaviour of natural images. In this paper, we propose a modified FSE that takes this prior knowledge into account for the modelling, yielding significant PSNR gains.