KLT-based multihypothesis prediction algorithm for DCVS

被引：0

作者：

Zhang, Yao ^{[1
,2
]}

Zhu, Jinxiu ^{[1
,2
]}

Meng, Yu ^{[1
,2
]}

Li, Li ^{[1
,2
]}

机构：

[1] College of Internet of Things Engineering, Hohai University

[2] Changzhou Key Laboratory of Sensor Networks and Environmental Perception

来源：

Journal of Computational Information Systems | 2014年 / 10卷 / 04期

关键词：

Best matching block; DCVS; KLT basis; MH-prediction;

D O I：

10.12733/jcis9362

中图分类号：

学科分类号：

摘要：

In this paper, considering the temporal and spatial correlations in video signals, the block-based Karhunen-Loève transform (KLT) basis which can get the most sparsity representation of current frame is introduced to reconstruct the frame to get better initial reconstruction, where the block-based KLT basis is generated by reference blocks which adaptively extracted from the search window that centered in the best matching block in previously reconstructed frame. In our proposed DCVS framework, the K-frame uses KLT-based intra multihypothesis (MH) prediction algorithm, while for the CS-frame, the KLT-based inter MH prediction algorithm is selected for the current block if the interframe correlation coefficient value exceeds a predetermined threshold. Otherwise, the KLT-based intra MH-prediction algorithm is worthwhile to be selected to generate a better side information (SI) for the sparse reconstruction. The experimental results show that our proposed framework can provide 1dB-4dB increase in PSNR compared to the conventional MH prediction for DCVS. © 2014 Binary Information Press.

引用

页码：1535 / 1542

页数：7

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