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Sparse representations for spatial prediction and texture refinement

机译：空间预测和纹理细化的稀疏表示

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摘要

In this work, we propose a novel approach for signal prediction based on the use of sparse signal representations and Matching Pursuit (MP) techniques. The paper first focuses on spatial texture prediction in a conventional block-based hybrid coding scheme and secondly addresses inter-layer prediction in a scalable video coding (SVC) framework. For spatial prediction the signal reconstruction of the block to predict is based on basis functions selected with the MP iterative algorithm, to best match a causal neighborhood. Inter-layer MP based prediction employs base layer upsampled components additionally to the causal neighborhood in order to improve the representation of high frequencies. New solutions are proposed for efficiently deriving and exploiting the atoms dictionary through phase refinement and mono-dimensional basis functions. Experimental results indicate noticeable improvement of rate/distortion performance compared to the standard prediction methods as specified in H.264/AVC and its extension SVC.
机译：在这项工作中，我们提出了一种基于稀疏信号表示和匹配追踪（MP）技术的信号预测新方法。本文首先关注传统的基于块的混合编码方案中的空间纹理预测，其次解决可伸缩视频编码（SVC）框架中的层间预测。对于空间预测，要预测的块的信号重构基于MP迭代算法选择的基本函数，以最佳地匹配因果邻域。基于层间MP的预测除因果邻域外还采用了基础层上采样分量，以改善高频的表示。提出了新的解决方案，用于通过相位细化和一维基函数有效地推导和利用原子字典。实验结果表明，与H.264 / AVC及其扩展SVC中指定的标准预测方法相比，速率/失真性能有了显着改善。

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