Measurement-domain intra prediction framework for compressively sensed images
Measurement-domain intra prediction framework for compressively sensed images
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DOI：
10.1109/iscas.2017.8050262
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
Jian-Bin Zhou;Dajiang Zhou;Li Guo;T. Yoshimura;S. Goto
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文献类型：
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作者：
Jian-Bin Zhou;Dajiang Zhou;Li Guo;T. Yoshimura;S. Goto
本文提出了一种与基于压缩感知（CS）的图像传感器兼容的测量域内预测编码框架。在这个框架中，我们提出了一种低复杂度的图像内预测算法，可以直接应用于图像传感器捕获的测量。此外，我们提出了一个结构随机0/1测量矩阵，嵌入可从测量中提取的块边界信息，用于内预测。实验结果表明，我们提出的框架可以压缩测量值并提高编码效率，与基于CS的传感器直接输出相比，bd率降低了30%。这可以大大节省在物联网时代大规模部署的无线摄像系统的能耗和通信带宽。
This paper presents a measurement-domain intra prediction coding framework that is compatible with compressive sensing (CS) based image sensors. In this framework, we propose a low-complexity intra prediction algorithm that can be directly applied to the measurements captured by the image sensor. Moreover, we propose a structural random 0/1 measurement matrix, embedding the block boundary information that can be extracted from the measurements for intra prediction. Experiment results show that our proposed framework can compress the measurements and increase coding efficiency, with 30% BD-rate reduction compared to the direct output of CS based sensors. This can significantly save both the energy consumption and the bandwidth in communication of wireless camera systems to be massively deployed in the era of IoT.