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

Error concealment (EC) is of great importance for block-based video systems, such as digital video broadcasting or video streaming services. In this paper, we propose a novel scalable spatial EC 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.