{"title":"Adaptive block size selection in a hybrid image compression algorithm employing the DCT and SVD","authors":"Garima Garg, Raman Kumar","doi":"10.2478/ijssis-2024-0005","DOIUrl":null,"url":null,"abstract":"\n The rationale behind this research stems from practical implementations in real-world scenarios, recognizing the critical importance of efficient image compression in fields such as medical imaging, remote sensing, and multimedia communication. This study introduces a hybrid image compression technique that employs adaptive block size selection and a synergistic combination of the discrete cosine transform (DCT) and singular value decomposition (SVD) to enhance compression efficiency while maintaining picture quality. Motivated by the potential to achieve significant compression ratios imperceptible to human observers, the hybrid approach addresses the escalating need for real-time image processing. The study pushes the boundaries of image compression by developing an algorithm that effectively combines conventional approaches with the intricacies of modern images, aiming for high compression ratios, adaptive picture content, and real-time efficiency. This article presents a novel hybrid algorithm that dynamically combines the DCT, SVD, and adaptive block size selection to enhance compression performance while keeping image quality constant. The proposed technique exhibits noteworthy accomplishments, achieving compression ratios of up to 60% and a peak signal-to-noise ratio (PSNR) exceeding 35 dB. Comparative evaluations demonstrate the algorithm’s superiority over existing approaches in terms of compression efficiency and quality measures. The adaptability of this hybrid approach makes significant contributions across various disciplines. In multimedia, it enhances data utilization while preserving image integrity; in medical imaging, it guarantees accurate diagnosis with compression-induced distortion (CID) below 1%; and in remote sensing, it efficiently manages large datasets, reducing expenses. The flexibility of this algorithm positions it as a valuable tool for future advancements in the rapidly evolving landscape of technology.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal on Smart Sensing and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/ijssis-2024-0005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The rationale behind this research stems from practical implementations in real-world scenarios, recognizing the critical importance of efficient image compression in fields such as medical imaging, remote sensing, and multimedia communication. This study introduces a hybrid image compression technique that employs adaptive block size selection and a synergistic combination of the discrete cosine transform (DCT) and singular value decomposition (SVD) to enhance compression efficiency while maintaining picture quality. Motivated by the potential to achieve significant compression ratios imperceptible to human observers, the hybrid approach addresses the escalating need for real-time image processing. The study pushes the boundaries of image compression by developing an algorithm that effectively combines conventional approaches with the intricacies of modern images, aiming for high compression ratios, adaptive picture content, and real-time efficiency. This article presents a novel hybrid algorithm that dynamically combines the DCT, SVD, and adaptive block size selection to enhance compression performance while keeping image quality constant. The proposed technique exhibits noteworthy accomplishments, achieving compression ratios of up to 60% and a peak signal-to-noise ratio (PSNR) exceeding 35 dB. Comparative evaluations demonstrate the algorithm’s superiority over existing approaches in terms of compression efficiency and quality measures. The adaptability of this hybrid approach makes significant contributions across various disciplines. In multimedia, it enhances data utilization while preserving image integrity; in medical imaging, it guarantees accurate diagnosis with compression-induced distortion (CID) below 1%; and in remote sensing, it efficiently manages large datasets, reducing expenses. The flexibility of this algorithm positions it as a valuable tool for future advancements in the rapidly evolving landscape of technology.
期刊介绍:
nternational Journal on Smart Sensing and Intelligent Systems (S2IS) is a rapid and high-quality international forum wherein academics, researchers and practitioners may publish their high-quality, original, and state-of-the-art papers describing theoretical aspects, system architectures, analysis and design techniques, and implementation experiences in intelligent sensing technologies. The journal publishes articles reporting substantive results on a wide range of smart sensing approaches applied to variety of domain problems, including but not limited to: Ambient Intelligence and Smart Environment Analysis, Evaluation, and Test of Smart Sensors Intelligent Management of Sensors Fundamentals of Smart Sensing Principles and Mechanisms Materials and its Applications for Smart Sensors Smart Sensing Applications, Hardware, Software, Systems, and Technologies Smart Sensors in Multidisciplinary Domains and Problems Smart Sensors in Science and Engineering Smart Sensors in Social Science and Humanity