Error concealment by means of motion refinement and regularized Bregman divergence

TL;DR

This paper proposes an adaptive error concealment method for video transmission that uses Bregman divergences and regularization to improve accuracy, convergence speed, and stability in noisy channel conditions.

Contribution

It introduces a novel adaptive regularization approach based on Bregman functions that enhances error concealment in noisy video transmission environments.

Findings

High-quality regularization parameter estimates achieved

Faster convergence of the error concealment process

Improved accuracy and stability in error correction

Abstract

This work addresses the problem of error concealment in video transmission systems over noisy channels employing Bregman divergences along with regularization. Error concealment intends to improve the effects of disturbances at the reception due to bit-errors or cell loss in packet networks. Bregman regularization gives accurate answers after just some iterations with fast convergence, better accuracy, and stability. This technique has an adaptive nature: the regularization functional is updated according to Bregman functions that change from iteration to iteration according to the nature of the neighborhood under study at iteration n. Numerical experiments show that high-quality regularization parameter estimates can be obtained. The convergence is sped up while turning the regularization parameter estimation less empiric, and more automatic.