期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 17, 期 3, 页码 2124-2133出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.2994743
关键词
Transform coding; Discrete cosine transforms; Image coding; Standards; Informatics; Internet of Things; Discrete cosine transform; deep residual learning; image compression; Internet of Things (IoT); JPEG
This article proposes a novel method for improving the efficiency of multimedia big data transmission in edge computing through data compression methods and deep learning technology. The method significantly reduces the amount of data transmitted at the sender's end, while at the receiver's end, original data is recovered using signal processing and deep residual learning model to ensure image quality.
With the development of big data and network technology, there are more use cases, such as edge computing, that require more secure and efficient multimedia big data transmission. Data compression methods can help achieving many tasks like providing data integrity, protection, as well as efficient transmission. Classical multimedia big data compression relies on methods like the spatial-frequency transformation for compressing with loss. Recent approaches use deep learning to further explore the limit of the data compression methods in communication constrained use cases like the Internet of Things (IoT). In this article, we propose a novel method to significantly enhance the transformation-based compression standards like JPEG by transmitting much fewer data of one image at the sender's end. At the receiver's end, we propose a two-step method by combining the state-of-the-art signal processing based recovery method with a deep residual learning model to recover the original data. Therefore, in the IoT use cases, the sender like edge device can transmit only 60% data of the original JPEG image without any additional calculation steps but the image quality can still be recovered at the receiver's end like cloud servers with peak signal-to-noise ratio over 31 dB.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据