OPTIMIZING FRACTAL IMAGE COMPRESSION VIA METAHEURISTIC ALGORITHMS
Exploring Efficient Techniques for improving Fractal Image Compression Performance
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The recent exponential growth in information technologies and digital cameras has resulted in a rapid influx of digital images. High-resolution digital cameras generate images that require substantial data storage and communication time for storage and transmission. In the healthcare sector, medical imaging modalities like MRI and computer tomography contribute significantly to the vast number of images used for diagnosis. The escalating demand for extensive storage capacity and efficient communication bandwidth has led to the development of image compression techniques. These techniques aim t...
The recent exponential growth in information technologies and digital cameras has resulted in a rapid influx of digital images. High-resolution digital cameras generate images that require substantial data storage and communication time for storage and transmission. In the healthcare sector, medical imaging modalities like MRI and computer tomography contribute significantly to the vast number of images used for diagnosis. The escalating demand for extensive storage capacity and efficient communication bandwidth has led to the development of image compression techniques. These techniques aim to represent images in a compact form, reducing data volume during storage or transmission across networks. Fractal Image Compression (FIC) stands out as an effective technique. Despite FIC's ability to create highly compressed files that decompose rapidly, challenges exist. The trade-off between compression and decompression times, influenced by a minimum speed constraint, results in longer compression times. To address these issues, this book investigates various metaheuristic algorithms' performance to enhance the compression efficiency of FIC.
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