EXPLORING ADVANCED DATA COMPRESSION TECHNIQUES TO ENHANCE STORAGE EFFICIENCY AND OPTIMIZED DATA TRANSMISSION.
Abstract
As data generation continues to surge exponentially, the demand for efficient storage solutions and optimized data transmission has become paramount. This project explores advanced data compression techniques aimed at addressing these challenges by reducing data size without compromising its integrity. By investigating both lossless and lossy compression algorithms, the study focuses on improving storage efficiency and reducing bandwidth consumption during data transmission. Key techniques such as Huffman coding, Run-Length Encoding (RLE), and advanced algorithms like wavelet compression and deep learning-based methods are analyzed. The research further evaluates their effectiveness in different domains, including multimedia, text, and sensor data transmission. Experimental results demonstrate the potential of these techniques to significantly enhance storage utilization and improve transmission speeds, thereby providing cost-effective and scalable solutions for data-intensive applications. This work contributes to optimizing storage management systems and network infrastructures, addressing the growing challenges in data handling in today’s digital landscape.
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