Scalable Kernel-Based Minimum Mean Square Error Estimator for Accelerated Image Error Concealment
This addresses the need for efficient error concealment in block-based video systems like DVB and streaming services, offering a scalable solution with reduced computational burden.
The paper tackles the problem of high computational cost in kernel-based minimum mean square error (K-MMSE) estimators for image error concealment in video systems, achieving reconstructions equivalent to K-MMSE with about one-tenth the computational time.
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.