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Bi-Level Video Codec for Machine Vision Embedded Applications

机译:机器视觉嵌入式应用的双层视频编解码器

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摘要

Wireless Visual Sensor Networks (WVSN) are feasible today due to the advancement in many fields of electronics such as Complementary Metal Oxide Semiconductor (CMOS) cameras, low power electronics, distributed computing and radio transceivers. The energy budget in WVSN is limited due to the small form factor of the Visual Sensor Nodes (VSNs) and the wireless nature of the application. The images captured by VSN contain huge amount of data which leads to high communication energy consumptions. Hence there is a need for designing efficient algorithms which are computationally less complex and provide high compression ratio. The change coding and Region of Interest (ROIs) coding are the options for data reduction of the VSN. But, for higher number of objects in the images, the compression efficiency of both the change coding and ROI coding becomes worse than that of image coding. This paper explores the compression efficiency of the Bi-Level Video Codec (BVC) for several representative machine vision applications. We proposed to implement image coding, change coding and ROI coding at the VSN and to select the smallest bit stream among the three. Results show that the compression performance of the BVC for such applications is always better than that of change coding and ROI coding.
机译:由于诸如互补金属氧化物半导体(CMOS)相机,低功率电子设备,分布式计算和无线电收发器等电子设备许多领域的进步,无线视觉传感器网络(WVSN)在当今已经可行。由于视觉传感器节点(VSN)的体积小以及应用程序的无线特性,WVSN中的能量预算受到限制。 VSN捕获的图像包含大量数据,这会导致较高的通信能耗。因此,需要设计有效的算法,该算法在计算上不那么复杂并且提供高压缩率。变更编码和感兴趣区域(ROI)编码是VSN数据缩减的选项。但是,对于图像中的更多对象,变化编码和ROI编码的压缩效率都变得比图像编码的压缩效率差。本文探讨了针对几种代表性机器视觉应用的双层视频编解码器(BVC)的压缩效率。我们建议在VSN上实现图像编码,更改编码和ROI编码,并在这三个中选择最小的比特流。结果表明,对于此类应用,BVC的压缩性能始终优于更改编码和ROI编码。

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