A Smarter Way to Compress and Decompress Data for Cloud Storage

The present work contributes an adaptive lossless compression-decompression technique for different data types, such as text, image, audio, and video files. The primary objective is to optimize cloud storage by maximize space savings while maintaining data confidentiality and reducing data loss. A Comparative analysis of experimental research of various compression methods is presented in the paper. The proposed method used search-based compression techniques using linear, binary, and interpolation search, to get effective lossless compression. The performance of method is computed using a dataset of different file formats, observing key parameters, including compression ratio, decompression time and space savings. The results are presented through graphs and tables and compared the computed results with existing methods available in the literature. The method provides a higher compression ratio and faster decompression time as compared to existing methods. It effectively reduces storage space on cloud servers without reducing data integrity. Interpolation-based compression is performing as compared to linear and binary search approaches, achieving up to 85% compression for audio and video files and 79% for text-based files while maintaining information integrity. An efficient and scalable lossless compression method works on different file formats and Its adaptability makes it a practical solution for cloud storage optimization, improving storage efficiency, retrieval speed, and cost-effectiveness. Comparative analysis confirms its superiority over existing techniques, highlighting its potential for large-scale cloud applications.