Application of DCT compression on images in IoT systems

Abstract

Through the integration of sensors, software, and wireless connectivity into everyday activities, the Internet of Things (IoT) creates intelligent ecosystems whose applications are inevitably permeating all segments of life, including smart cities, agriculture, healthcare, and manufacturing. Such systems frequently utilize image and video data as information sources for machine learning and analysis, which are essential for anomaly detection, security surveillance, facial recognition, traffic optimization, and—in the case of this study—monitoring bee activity within a beehive. Given that digital images contain vast amounts of data transmitted through digital processing systems, it is vital to efficiently address the challenges of data transmission within resource-constrained IoT networks. The primary objective of the research presented in this paper is to demonstrate that the application of DCT (Discrete Cosine Transform) compression on images can significantly reduce data volume while simultaneously preserving the image quality required for further application. To evaluate the compression, DCT was applied to JPEG images obtained from smart beekeeping system cameras, utilizing the Python programming language with OpenCV and NumPy libraries. The images were sourced from publicly available datasets. The analysis involved applying DCT compression across six defined quality levels: 1, 5, 20, 50, 80, and 100, covering a range that allows for the assessment of the trade-off between compression ratio and image quality. As expected, the lowest quality factor yields the smallest file sizes but results in the lowest image quality. However, it is significant that even with a moderate factor of 20, substantial optimization is achieved — a fivefold reduction in data while maintaining satisfactory image quality for further processing. The optimal result is achieved at a factor of 80, which provides a twofold reduction in data and an output image where quality differences, compared to the original, are indiscernible to the naked eye.