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A no-reference error-tolerability test technique for videos via edge and extreme-value checking and its hardware implementation

机译:通过边缘和极值检查的视频无参考容错测试技术及其硬件实现

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Approximate computing has been shown to be a promising manner to enhance circuit performance via acceptable quality degradation. This manner is applicable to many multimedia applications such as videos. In order to carry out a dynamic approximate computing scheme, on-line quality evaluation/monitoring will be needed. However, to the best of our knowledge, on-line error-tolerability evaluation of videos is seldom studied in the literature. To achieve this, a no-reference manner will be helpful, which means that no reference (golden) videos are needed for comparison with the videos under test. Implementation of on-line test procedures can thus be greatly simplified. In this paper we investigate how to achieve no-reference error-tolerability evaluation for videos with fixed or varied background. For videos with fixed background a no-reference error-tolerability test technique is proposed. By well exploiting some simple attributes, the proposed technique can accurately determine the acceptability for 81,412 erroneous videos with > 90% accuracy. As a comparison, the related previous work can only achieve about 80% accuracy. In addition, our attribute acquirement process requires only 33% of the computation time for the previous work. As for videos with varied background, we also discuss possible solutions. Hardware implementation of the proposed test technique is also addressed. The results show that the hardware contains about 13,702 logic gates, which is 8.11% of an open-source baseline H.264 video decoder. The proposed hardware operates at 20 MHz frequency, which is sufficient to support real-time error-tolerability testing of videos.
机译:近似计算已被证明是通过可接受的质量下降来增强电路性能的一种有前途的方式。这种方式适用于许多多媒体应用,例如视频。为了执行动态近似计算方案,将需要在线质量评估/监控。但是,据我们所知,文献中很少研究视频的在线容错性评估。为此,无参考方式将很有帮助,这意味着无需参考(黄金)视频即可与被测视频进行比较。在线测试程序的实施因此可以大大简化。在本文中,我们研究了如何针对背景固定或变化的视频实现无参考错误容忍度评估。对于具有固定背景的视频,提出了无参考误差容限测试技术。通过很好地利用一些简单的属性,所提出的技术可以准确地确定81,412个错误视频的可接受性,且准确性> 90%。作为比较,以前的相关工作只能达到约80%的精度。此外,我们的属性获取过程仅需要前一工作的33%的计算时间。至于背景各异的视频,我们还将讨论可能的解决方案。还解决了所提出的测试技术的硬件实现。结果表明,该硬件包含约13,702个逻辑门,占开源基线H.264视频解码器的8.11%。拟议的硬件以20 MHz的频率运行,足以支持视频的实时容错测试。

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