COMPUTATIONAL SYSTEM FOR ADAPTIVE COMPRESSION OF MULTIMEDIA DATA

Abstract

This invention relates to an adaptive compression system designed to optimize the transmission and storage of multimedia data. For example, in telemedicine applications, the system reduces the size of DICOM medical images by up to 83%, while maintaining SSIM ≥ 0.99, ensuring diagnostic quality for real-time transmission. Using iterative algorithms and machine learning, the system dynamically adjusts compression parameters based on input file characteristics such as resolution and bit depth. The quality of the compressed file is evaluated by metrics such as SSIM and PSNR. Ensuring an ideal balance between efficiency and visual quality. Technical innovations: 1. Integration of supervised learning for dynamic prediction of compression parameters. 2. Iterative adjustment based on quality assessments (SSIM and PSNR). 3. Support for multiple input formats, such as TIFF, PNG, JPEG, DICOM, CIF, and bitmapping. 4. Compatibility with emerging technologies, including H.266-based compression. With applications in various sectors, such as healthcare, entertainment, digital archiving, and... EDUCATION, THE SOLUTION IS SCALABLE AND HIGHLY ADAPTABLE, REDUCING COSTS AND MAXIMIZING EFFICIENCY IN CRITICAL OPERATIONS.