Adaptive block size selection in a hybrid image compression algorithm employing the DCT and SVD

• Published in: International journal on smart sensing and intelligent systems Vol. 17; no. 1
• Main Authors: Garg, Garima, Kumar, Raman
• Format: Journal Article
• Language: English
• Published: Sydney Sciendo 01.01.2024; De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
• Subjects: Adaptive algorithms; Algorithms; Compression ratio; Discrete cosine transform; Efficiency; hybrid approach; Image compression; Image enhancement; Image processing; Image quality; Medical imaging; Multimedia; Multimedia communications; Noise levels; Ratios; Real time; real-time applications; Remote sensing; Signal quality; Signal to noise ratio; Singular value decomposition
• ISSN: 1178-5608; 1178-5608
• DOI: 10.2478/ijssis-2024-0005

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.