Data reduction in MMBD computing

Abstract

Internet of Things technology is emerging very quickly in human facilities of all types, such as smart home and industry, which leads to a large boom in multimedia big data due to the connection of approximately 50 billion devices to the internet in 2020. It is really a challenging task to manage the IoT multimedia data regarding storage and transmission. The only way to handle this complicated storage and transmission problem is the process of compression techniques. Multimedia data is compressed by reducing its redundancy. Compression algorithms face numerous difficulties because of the large size, high streaming rate, and the high quality of the data, due to their different types and modality of acquisition. This chapter provides an overarching view of data compression challenges related to big data and IoT environment. In this chapter, we provide an overview of the various data compression techniques employed for multimedia big data computing, such as run-length coding, Huffman coding, arithmetic coding, delta modulation, discrete cosine transform, fast Fourier transform, joint photograph expert group, moving picture expert group, and H.261, including the essential theory, the taxonomy, necessary algorithmic details, mathematical foundations, and their relative benefits and disadvantages.