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A Measurement Coding System for Block-Based Compressive Sensing Images by Using Pixel-Domain Features

机译:基于像素域特征的基于块的压缩传感图像测量编码系统

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Compressive sensing (CS) is data acquiring and innovative mathematical approach that accelerate and efficient sampling from large into small volumes of data. Moreover, it could be dramatically reduced amounts of sensor, power consumption, storage size, and bandwidth which results in lower hardware costs [1]. In wireless cameras network for video surveillance, the large amount of data is produced. However, there is still a lot of redundant data in measurement domain. To solve this problem, coding techniques such as block-based CS (BCS), intra-prediction and quantization is applied to avoid higher rate-distortion than other CS frameworks. Therefore, new imaging architecture has been proposed to be sensed, removed redundant information, and compressed simultaneously, thus leading to the faster image acquisition system.
机译:压缩感测(CS)是一种数据获取和创新的数学方法,可加速从大数据到小数据的高效采样。而且,可以显着减少传感器的数量,功耗,存储大小和带宽,从而降低硬件成本[1]。在用于视频监控的无线摄像机网络中,会产生大量数据。但是,在测量域中仍然有很多冗余数据。为了解决这个问题,应用了诸如基于块的CS(BCS),帧内预测和量化之类的编码技术来避免比其他CS框架更高的速率失真。因此,已经提出了一种新的成像体系结构,该体系结构被感测,去除冗余信息并同时被压缩,从而导致更快的图像采集系统。

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