Open Access
Compression Using Huffman Coding
Mamta Sharma
- 01 Jan 2010
TL;DR: Huffman algorithm is analyzed and compared with other common compression techniques like Arithmetic, LZW and Run Length Encoding to make storing easier for large amount of data.
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Abstract: Data compression is also called as source coding. It is the process of encoding information using fewer bits than an uncoded representation is also making a use of specific encoding schemes. Compression is a technology for reducing the quantity of data used to represent any content without excessively reducing the quality of the picture. It also reduces the number of bits required to store and/or transmit digital media. Compression is a technique that makes storing easier for large amount of data. There are various techniques available for compression in my paper work , I have analyzed Huffman algorithm and compare it with other common compression techniques like Arithmetic, LZW and Run Length Encoding.
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Citations
Lossless Image Compression Techniques: A State-of-the-Art Survey
Md. Atiqur Rahman,Mohamed Hamada +1 more
TL;DR: This paper presents a detailed analysis of run-length, entropy and dictionary based lossless image compression algorithms with a common numeric example for a clear comparison and measures the performance of the state-of-the-art techniques.
76
TADOC: Text Analytics Directly on Compression
Feng Zhang,Jidong Zhai,Xipeng Shen,Dalin Wang,Zheng Chen,Onur Mutlu,Wenguang Chen,Xiaoyong Du +7 more
TL;DR: Experiments show that TADOC can save 90.8% storage space and 87.9% memory usage, while halving data processing times, on six data analytics tasks of various complexities.
64
Evaluation of Huffman and Arithmetic Algorithms for Multimedia Compression Standards
TL;DR: This paper has implemented and tested Huffman and arithmetic algorithms, and implemented results show that compression ratio of arithmetic coding is better than Huffman coding, while the performance of the Huff man coding is higher than Arithmetic coding.
60
Various Image Compression Techniques: Lossy and Lossless
TL;DR: The purpose of the image compression is to decrease the redundancy and irrelevance of image data to be capable to record or send data in an effective form, which decreases the time of transmit in the network and raises the transmission speed.
Review on techniques and file formats of image compression
TL;DR: This paper presents formats that use to reduce redundant information in an image, unnecessary pixels and non-visual redundancy, as well as a brief description of the main technologies and traditional format that commonly used in image compression.
49
References
•Book
Introduction to Algorithms
Thomas H. Cormen,Charles E. Leiserson,Ronald L. Rivest +2 more
- 01 Jan 1990
TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
24.8K
A universal algorithm for sequential data compression
Jacob Ziv,A. Lempel +1 more
TL;DR: The compression ratio achieved by the proposed universal code uniformly approaches the lower bounds on the compression ratios attainable by block-to-variable codes and variable- to-block codes designed to match a completely specified source.
•Book
Introduction to Algorithms, Second Edition
Ronald L. Rivest,Charles E. Leiserson,Thomas H. Cormen,Clifford Stein +3 more
- 01 Jan 2001
TL;DR: The complexity class P is formally defined as the set of concrete decision problems that are polynomial-time solvable, and encodings are used to map abstract problems to concrete problems.
2.9K
A Technique for High-Performance Data Compression
TL;DR: A new compression algorithm is introduced that is based on principles not found in existing commercial methods in that it dynamically adapts to the redundancy characteristics of the data being compressed, and serves to illustrate system problems inherent in using any compression scheme.
2.6K
Data compression
TL;DR: A variety of data compression methods are surveyed, from the work of Shannon, Fano, and Huffman in the late 1940s to a technique developed in 1986, which has important application in the areas of file storage and distributed systems.
613
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