Scalable Kernel-Based Minimum Mean Square Error Estimator for Accelerated Image Error Concealment

TL;DR

This paper introduces a scalable, hierarchical kernel-based MMSE error concealment algorithm for video, achieving high-quality reconstructions with significantly reduced computational costs.

Contribution

A novel hierarchical and adaptive kernel-based MMSE error concealment method that improves efficiency while maintaining high reconstruction quality.

Findings

Outperforms state-of-the-art algorithms in quality

Requires about one-tenth of the computational time of K-MMSE

Provides high-quality reconstructions comparable to K-MMSE

Abstract

Error concealment is of great importance for block-based video systems, such as DVB or video streaming services. In this paper, we propose a novel scalable spatial error concealment algorithm that aims at obtaining high quality reconstructions with reduced computational burden. The proposed technique exploits the excellent reconstructing abilities of the kernel-based minimum mean square error K-MMSE estimator. We propose to decompose this approach into a set of hierarchically stacked layers. The first layer performs the basic reconstruction that the subsequent layers can eventually refine. In addition, we design a layer management mechanism, based on profiles, that dynamically adapts the use of higher layers to the visual complexity of the area being reconstructed. The proposed technique outperforms other state-of-the-art algorithms and produces high quality reconstructions, equivalent to K-MMSE, while requiring around one tenth of its computational time.