Digital Image Representation by Atomic Functions: The Compression and Protection of Data for Edge Computing in IoT Systems

Abstract

Digital images are used in various technological, financial, economic, and social processes. Huge datasets of high-resolution images require protected storage and low resource-intensive processing, especially when applying edge computing (EC) for designing Internet of Things (IoT) systems for industrial domains such as autonomous transport systems. For this reason, the problem of the development of image representation, which provides compression and protection features in combination with the ability to perform low complexity analysis, is relevant for EC-based systems. Security and privacy issues are important for image processing considering IoT and cloud architectures as well. To solve this problem, we propose to apply discrete atomic transform (DAT) that is based on a special class of atomic functions generalizing the well-known up-function of V.A. Rvachev. A lossless image compression algorithm based on DAT is developed, and its performance is studied for different structures of DAT. This algorithm, which combines low computational complexity, efficient lossless compression, and reliable protection features with convenient image representation, is the main contribution of the paper. It is shown that a sufficient reduction of memory expenses can be obtained. Additionally, a dependence of compression efficiency measured by compression ratio (CR) on the structure of DAT applied is investigated. It is established that the variation of DAT structure produces a minor variation of CR. A possibility to apply this feature to data protection and security assurance is grounded and discussed. In addition, a structure or file for storing the compressed and protected data is proposed, and its properties are considered. Multi-level structure for the application of atomic functions in image processing and protection for EC in IoT systems is suggested and analyzed.

Spatial Complexity Reduction in Remote Sensing Image Compression by Atomic Functions

“…Therefore, splitting images into blocks of a size smaller than 512 × 512 might be preferable. Nevertheless, this may reduce an efficiency of CABAC [8], as well as image representation feature provided by DAC [21]. So, the size considered seems to be a reasonable trade-off.…”

“…In the current paper, discrete atomic compression (DAC), which is lossy image compression algorithm [20,21], is considered. Unlike JPEG, AGU, ADCT and WebP based on discrete cosine transform (DCT), discrete atomic transform (DAT) is applied in DAC.…”

“…Moreover, there is built-in data protection feature in DAC [20]. Besides, image processing by DAC allows obtaining such image representation convenient for applying various artificial intelligence methods [21]. Compared to JPEG, the algorithm DAC provides higher memory reduction for visually lossless case (for ≥ 35 [27]).…”

IoT Multi-Vector Cyberattack Detection Based on Machine Learning Algorithms: Traffic Features Analysis, Experiments, and Efficiency

“…One possible feature involved in building an IoT infrastructure is an IoT system of groups of identical or similar technical characteristic devices. If a specified device has a vulnerability, such homogeneity multiplies its impact [3][4][5].…”

Effect of Compressed Sensing Rates and Video Resolutions on a PoseNet Model in an AIoT System

“…due to limited bandwidth [10,11]. Many research studies have been conducted to reduce the problems in the AIoT system [12][13][14][15][16][17]. A general distributed federated learning framework was presented to avoid the communication bottleneck of most centrally managed AIoT systems in [12].…”