Digital Image Representation by Atomic Functions: The Compression and Protection of Data for Edge Computing in IoT Systems
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 indust...
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| Vydáno v: | Sensors (Basel, Switzerland) Ročník 22; číslo 10; s. 3751 |
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| Jazyk: | angličtina |
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01.05.2022
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| 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. |
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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Illiashenko, Oleg Makarichev, Viktor Lukin, Vladimir Kharchenko, Vyacheslav |
| AuthorAffiliation | 1 Department of Computer Systems, Networks and Cybersecurity, National Aerospace University “KhAI”, 17, Chkalov Str., 61070 Kharkiv, Ukraine; v.makarichev@khai.edu (V.M.); v.kharchenko@csn.khai.edu (V.K.) 2 Department of Information-Communication Technologies, National Aerospace University “KhAI”, 17, Chkalov Str., 61070 Kharkiv, Ukraine; v.lukin@khai.edu |
| AuthorAffiliation_xml | – name: 1 Department of Computer Systems, Networks and Cybersecurity, National Aerospace University “KhAI”, 17, Chkalov Str., 61070 Kharkiv, Ukraine; v.makarichev@khai.edu (V.M.); v.kharchenko@csn.khai.edu (V.K.) – name: 2 Department of Information-Communication Technologies, National Aerospace University “KhAI”, 17, Chkalov Str., 61070 Kharkiv, Ukraine; v.lukin@khai.edu |
| Author_xml | – sequence: 1 givenname: Viktor orcidid: 0000-0003-1481-9132 surname: Makarichev fullname: Makarichev, Viktor – sequence: 2 givenname: Vladimir orcidid: 0000-0002-1443-9685 surname: Lukin fullname: Lukin, Vladimir – sequence: 3 givenname: Oleg orcidid: 0000-0002-4672-6400 surname: Illiashenko fullname: Illiashenko, Oleg – sequence: 4 givenname: Vyacheslav orcidid: 0000-0001-5352-077X surname: Kharchenko fullname: Kharchenko, Vyacheslav |
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| Cites_doi | 10.1007/s12083-021-01191-6 10.1007/s13721-018-0169-x 10.1002/hbm.25803 10.1109/JIOT.2021.3088875 10.1109/ACCESS.2017.2693438 10.1016/j.csbj.2021.01.028 10.3390/info13020089 10.3390/app11209680 10.2991/978-94-6239-009-6 10.1016/j.iatssr.2019.11.008 10.1109/TIFS.2021.3096024 10.1070/RM1990v045n01ABEH002324 10.1007/978-3-030-12195-2 10.1016/B978-0-12-809474-7.00019-7 10.5220/0009407003130320 10.32620/reks.2021.4.03 10.1145/3009906 10.1016/j.jpdc.2020.12.015 10.1007/978-3-030-90158-5 10.1088/1757-899X/452/4/042102 10.1007/s11042-020-09850-1 10.1016/j.asej.2019.03.002 10.32620/reks.2021.1.12 10.32620/reks.2020.1.05 10.1201/9781315097954 10.3390/rs14010125 10.1109/ACCESS.2021.3096136 10.1186/s42400-020-00066-2 10.4018/978-1-5225-3643-7 10.3390/s22030927 10.3390/s22072665 10.1007/978-3-030-38141-7 10.1145/3437537 10.1049/PBPC042E |
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| SubjectTerms | Algorithms Artificial intelligence atomic function atomic wavelet discrete atomic transform Image processing image protection image representation Internet of Things lossless image compression Medical imaging equipment Software Trends |
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| Title | Digital Image Representation by Atomic Functions: The Compression and Protection of Data for Edge Computing in IoT Systems |
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