Recently, the advancements in the field of wireless technologies and micro-electro-mechanical systems lead to the development of potential applications in wireless sensor networks (WSNs). The visual sensors in WSN create a significant impact on computer vision based applications such as pattern recognition and image restoration. generate a massive quantity of multimedia data. Since transmission of images consumes more computational resources, various image compression techniques have been proposed. But, most of the existing image compression techniques are not applicable for sensor nodes due to its limitations on energy, bandwidth, memory, and processing capabilities. In this article, we introduce a highly reliable and low-complexity image compression scheme using neighborhood correlation sequence (NCS) algorithm. The NCS algorithm performs the bit reduction operation and then encoded by a codec (such as PPM, Deflate, and Lempel Ziv Markov chain algorithm.) to further compress the image. The proposed NCS algorithm increases the compression performance and decreases the energy utilization of the sensor nodes with high fidelity. Moreover, it achieved a minimum end to end delay of 1074.46 ms at the average bit rate of 4.40 bpp and peak signal to noise ratio of 48.06 on the applied test images. On comparing with state-of-art methods, the proposed method maintains a better tradeoff between compression efficiency and reconstructed image quality.

中文翻译：

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无线传感器网络中使用邻域相关序列算法的高可靠低复杂度图像压缩方案


最近，无线技术和微机电系统领域的进步导致了无线传感器网络（WSN）潜在应用的发展。无线传感器网络中的视觉传感器对基于计算机视觉的应用（例如模式识别和图像恢复）产生了重大影响。产生海量的多媒体数据。由于图像的传输消耗更多的计算资源，因此已经提出了各种图像压缩技术。但是，大多数现有的图像压缩技术由于其能量、带宽、内存和处理能力的限制而不适用于传感器节点。在本文中，我们介绍了一种使用邻域相关序列（NCS）算法的高度可靠且低复杂度的图像压缩方案。 NCS算法进行比特缩减操作，然后通过编解码器（例如PPM、Deflate和Lempel Ziv Markov链算法）进行编码，以进一步压缩图像。所提出的NCS算法提高了压缩性能并降低了高保真度传感器节点的能量利用率。此外，在所应用的测试图像上，平均比特率为4.40 bpp，峰值信噪比为48.06，最小端到端延迟为1074.46 ms。与最先进的方法相比，所提出的方法在压缩效率和重建图像质量之间保持了更好的权衡。