论文标题
基于可扩展内核的最小均方根误差估计器，用于加速图像错误隐藏
Scalable Kernel-Based Minimum Mean Square Error Estimator for Accelerated Image Error Concealment
论文作者
论文摘要
对于基于块的视频系统，例如DVB或视频流服务，错误隐藏非常重要。在本文中，我们提出了一种新型的可扩展空间误差隐藏算法，该算法旨在以减少计算负担获得高质量的重建。所提出的技术利用了基于内核的最小均方误差K-MMSE估计器的出色重建能力。我们建议将这种方法分解为一组层次堆叠的层。第一层执行后续层最终可以完善的基本重建。此外，我们设计了基于轮廓的图层管理机制，该机制动态地适应了较高的层的使用，以适应正在重建的区域的视觉复杂性。该提出的技术优于其他最先进的算法，并产生高质量的重建，相当于K-MMSE，同时需要其计算时间的十分之一。
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.
