Дослідження методу фрактального стиснення зображень з метою покращення якості стиснення
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Date
2024
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Український державний університет науки і технологій, ННІ ≪Інститут промислових та бізнес технологій≫, ІВК ≪Системні технології≫, Дніпро
Abstract
UKR: Розвиток мережі Internet, поряд з доступністю все більш потужних комп'ютерів та інших цифрових пристроїв, камер, сканерів та принтерів, привели до широкого використання цифрових зображень. У зв'язку з цим зростає інтерес до поліпшення алгоритмів стиснення даних, наприклад зображень. Стиснення даних є актуальним як для швидкості передачі, так і ефективності зберігання. Крім багатьох видів комерційного використання, технології стиснення становлять також інтерес у військовій галузі, наприклад, програми обробки даних телеметрії, отриманих від перехоплювачів ракет, або для архівного зберігання даних про зображення місцевості для моделювання оборонних дій. Вирішення проблеми стиснення зображення або, в загальному сенсі, кодування зображення, використовувало досягнення та стимулювало розвиток багатьох галузей техніки та математики. В статті досліджується фрактальне стиснення зображень —метод стиснення даних, що ґрунтується на використанні самоподібних патернів у зображенні. Цей метод дозволяє досягти високого ступеня стиснення за умови збереження деталей зображення.
ENG: The development of the Internet, along with the availability of increasingly powerful computers and other digital devices, cameras, scanners and printers, has led to the wide-spread use of digital images. In this regard, interest in improving data compression algorithms, such as images, is growing. Data compression is important for both transfer speed and storage efficiency. In addition to many commercial uses, compression technologies are also of interest in the military industry, such as applications for processing telemetry data from missile interceptors or for archiving terrain image data for defense simulations. Solving the problem of image compression, or, more generally, image coding, has used advances and stimulated the development of many fields of engineering and mathematics. The article examines fractal image compression — a data compression method based on the use of self-similar patterns in an image. This method allows you to achieve a high degree of compression while preserving image details. Fractal image compression is a unique and efficient approach to data compression based on the mathematical theory of fractals. Nowadays, it has important applications and advantages that make it a valuable tool in image processing. The main advantages include: 1. Preservation of details during compression. One of the key advantages of fractal compression is its ability to preserve a high degree of image detail in a relatively small amount of storage. This is especially important in situations where image quality must be preserved with limited storage and data transfer resources. 2. Efficiency of transmission through the network. Fractal compression allows for compact images, making it suitable for image transmission over a low-bandwidth network. This is especially true for mobile devices, the Internet of Things, and other scenarios where high bandwidth is not always available. 3. Adaptive compression for different resolutions. Fractal compression allows you to adapt the level of compression depending on the resolution and details of the image. This means that it can be used to compress various image sizes without significant loss of quality. 4. Data archiving and storage. Fractal compression can be useful for archiving and long-term storage of images, as it allows you to effectively reduce the amount of data without losing important information. This is especially important for libraries, archives, research databases and other data repositories. Fractal image compression remains a relevant and valuable tool in today's environment, thanks to its ability to efficiently compress, preserve details, and adapt to different usage scenarios. Therefore, the study of its efficiency, the optimization of the soft-ware code to obtain a faster and better compression result, is an urgent task.
ENG: The development of the Internet, along with the availability of increasingly powerful computers and other digital devices, cameras, scanners and printers, has led to the wide-spread use of digital images. In this regard, interest in improving data compression algorithms, such as images, is growing. Data compression is important for both transfer speed and storage efficiency. In addition to many commercial uses, compression technologies are also of interest in the military industry, such as applications for processing telemetry data from missile interceptors or for archiving terrain image data for defense simulations. Solving the problem of image compression, or, more generally, image coding, has used advances and stimulated the development of many fields of engineering and mathematics. The article examines fractal image compression — a data compression method based on the use of self-similar patterns in an image. This method allows you to achieve a high degree of compression while preserving image details. Fractal image compression is a unique and efficient approach to data compression based on the mathematical theory of fractals. Nowadays, it has important applications and advantages that make it a valuable tool in image processing. The main advantages include: 1. Preservation of details during compression. One of the key advantages of fractal compression is its ability to preserve a high degree of image detail in a relatively small amount of storage. This is especially important in situations where image quality must be preserved with limited storage and data transfer resources. 2. Efficiency of transmission through the network. Fractal compression allows for compact images, making it suitable for image transmission over a low-bandwidth network. This is especially true for mobile devices, the Internet of Things, and other scenarios where high bandwidth is not always available. 3. Adaptive compression for different resolutions. Fractal compression allows you to adapt the level of compression depending on the resolution and details of the image. This means that it can be used to compress various image sizes without significant loss of quality. 4. Data archiving and storage. Fractal compression can be useful for archiving and long-term storage of images, as it allows you to effectively reduce the amount of data without losing important information. This is especially important for libraries, archives, research databases and other data repositories. Fractal image compression remains a relevant and valuable tool in today's environment, thanks to its ability to efficiently compress, preserve details, and adapt to different usage scenarios. Therefore, the study of its efficiency, the optimization of the soft-ware code to obtain a faster and better compression result, is an urgent task.
Description
А. Журба: ORCID 0000-0002-9983-3971
Keywords
фрактал, фрактальне стиснення, самоподібність, афінні перетворення, коефіцієнт стиснення, декомпресія, fractal, fractal compression, self-similarity, affine transformations, compression ratio, decompression, КІТС
Citation
Журба А. О. Дослідження методу фрактального стиснення зображень з метою покращення якості стиснення. Системні технології. Дніпро, 2024. Т. 4, № 153. С. 24–33. DOI: 10.34185/1562-9945-4-153-2024-03.