: 10.62796/pijst
Sarthak Singh, B.Tech (Computer Science), KIET, Ghaziabad (UP)
Dr. Chandan Kumar, PhD (HIS), MBA (IT & HR), SVSU, Meerut
Published Date: 03-05-2025 Issue: Vol. 2 No. 8 (2025): August 2025 Published Paper PDF: Download
Abstract- Information Theory and Data Compression are fundamental areas in computer science and communications, dealing with the efficient representation, transmission, and storage of data. Information theory, introduced by Claude Shannon, provides a mathematical framework to quantify the amount of information contained in a message, using concepts like entropy, mutual information, and channel capacity. These principles form the foundation for understanding how data can be compressed, transmitted, and decoded effectively. Data compression techniques aim to reduce the size of data without losing essential information, thereby enhancing storage capacity and transmission speed. Lossless compression methods, such as Huffman coding and Lempel-Ziv-Welch (LZW), ensure no data loss, making them ideal for applications where accuracy is crucial, like text or executable files. Lossy compression algorithms, including JPEG and MP3, offer higher compression ratios by discarding some data, making them suitable for multimedia files where perfect accuracy is not required. This paper explores the theoretical foundations of information theory, its application in data compression, and current trends in both lossless and lossy techniques. The analysis highlights the trade-offs between compression efficiency, computational complexity, and data fidelity, illustrating the ongoing evolution in these critical fields of study.
Keywords-Information Theory, Data Compression, Entropy, Huffman Coding, Lempel-Ziv-Welch (LZW), Lossless Compression, Lossy Compression, Channel Capacity, Mutual Information.
Published Date: 03-05-2025 Issue: Vol. 2 No. 8 (2025): August 2025 Published Paper PDF: Download
Abstract- Information Theory and Data Compression are fundamental areas in computer science and communications, dealing with the efficient representation, transmission, and storage of data. Information theory, introduced by Claude Shannon, provides a mathematical framework to quantify the amount of information contained in a message, using concepts like entropy, mutual information, and channel capacity. These principles form the foundation for understanding how data can be compressed, transmitted, and decoded effectively. Data compression techniques aim to reduce the size of data without losing essential information, thereby enhancing storage capacity and transmission speed. Lossless compression methods, such as Huffman coding and Lempel-Ziv-Welch (LZW), ensure no data loss, making them ideal for applications where accuracy is crucial, like text or executable files. Lossy compression algorithms, including JPEG and MP3, offer higher compression ratios by discarding some data, making them suitable for multimedia files where perfect accuracy is not required. This paper explores the theoretical foundations of information theory, its application in data compression, and current trends in both lossless and lossy techniques. The analysis highlights the trade-offs between compression efficiency, computational complexity, and data fidelity, illustrating the ongoing evolution in these critical fields of study.
Keywords-Information Theory, Data Compression, Entropy, Huffman Coding, Lempel-Ziv-Welch (LZW), Lossless Compression, Lossy Compression, Channel Capacity, Mutual Information.
