GTT: Graph template transforms with applications to image coding
GTT: Graph template transforms with applications to image coding
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GTT：图模板随着图像编码的应用而转变
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Antonio Ortega
中科院分区：
文献类型：
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作者：
Eduardo Pavez;Hilmi E. Egilmez;Yongzhe Wang;Antonio Ortega
卡尔胡宁-洛夫变换(KLT)被认为是去相关平稳高斯过程的最优方法，它提供了有效的图像变换编码。尽管KLT允许对这类信号进行有效的表示，但变换本身完全是数据驱动的，并且计算复杂。本文提出了一类新的变换，称为图模板变换(GTTS)，它通过利用图模板所表示的信号的先验信息来逼近KLT。为了构造GTT，(I)定义了导致一类变换的设计矩阵，然后(Ii)使用约束优化框架基于给定的图模板来学习图，该图模板构造了先验已知信息。我们的实验结果表明，所提出的GTTS的一些实例可以在显著降低复杂度的情况下接近KLT的率失真性能。
The Karhunen-Loeve transform (KLT) is known to be optimal for decorrelating stationary Gaussian processes, and it provides effective transform coding of images. Although the KLT allows efficient representations for such signals, the transform itself is completely data-driven and computationally complex. This paper proposes a new class of transforms called graph template transforms (GTTs) that approximate the KLT by exploiting a priori information known about signals represented by a graph-template. In order to construct a GTT (i) a design matrix leading to a class of transforms is defined, then (ii) a constrained optimization framework is employed to learn graphs based on given graph templates structuring a priori known information. Our experimental results show that some instances of the proposed GTTs can closely achieve the rate-distortion performance of KLT with significantly less complexity.