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

• URL: http://arxiv.org/abs/2205.11226v1
• Date: Mon, 23 May 2022 12:15:24 GMT
• Title: Scalable Kernel-Based Minimum Mean Square Error Estimator for Accelerated Image Error Concealment
• Authors: J\'an Koloda, J\"urgen Seiler, Antonio M. Peinado, and Andr\'e Kaup
• Abstract summary: We propose a novel scalable spatial error concealment algorithm. It exploits the excellent reconstructing abilities of the kernel-based minimum mean square error K-MMSE estimator. It produces high quality reconstructions, equivalent to K-MMSE, while requiring around one tenth of its computational time.
• Score: 2.3484130340004326
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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