Lossless text data compression is an important field as it significantly reduces storage requirement and communication cost. In this work, the focus is directed mainly to different file compression coding techniques and comparisons between them. Some memory efficient encoding schemes are analyzed and implemented in this work. They are: Shannon Fano Coding, Huffman Coding, Repeated Huffman Codin...

3 Probability Coding 10 3.1 Prefix Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.1.1 Relationship to Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2 Huffman Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2.1 Combining Messages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2.2 Minim...

without Frequency Counts Yoshiyuki Okada, Noriko Satoh, Kimitaka Murashita, and Shigeru Yoshida Holography and Color Imaging Lab., Fujitsu Laboratories Ltd. Email: (yokada,nsatoh,kimi,per241)@flab.fujitsu.co.jp

The development of an efficient compression scheme to process the Arabic language represents a difficult task. This paper employs the dynamic Huffman coding on data compression with variable length bit coding, on the Arabic language. Experimental tests have been performed on both Arabic and English text. A comparison is made to measure the efficiency of compressing data results on both Arabic a...

• Arithmetic Coding is universal • Arithmetic coding is suitable for small alphabet (binary sources) with highly skewed probabilities. • Huffman cannot allocate fractional bits to symbols: , but the algorithm will assign 1 bit. Huffman needs at least one bit per symbol. If, however, the alphabet is large and probabilities are not skewed, Huffman rate is pretty close to entropy (H). , 01 . 0 , 9...

In this paper, complementary Huffman coding techniques are proposed for test data compression/decompression of complex SOC designs during manufacturing testing. Instead of the compatible relationship between test data blocks, complementary features between test blocks are also exploited. Based on this property, more test data blocks can share the same codeword and the size of the modified Huffm...

The lossless entropy coding used in many image coding schemes often is overlooked as most research is based around the lossy stages of image compression. This paper examines the relative merits of using static Huffman coding with a compact optimal table verses more sophisticated adaptive arithmetic methods. For very low bit rate image compression, the computationally simple Huffman method is sh...

Lossless compression of a progression of symbols is a decisive part of data and signal compression. Huffman coding is lossless in nature; it is also generally utilized in lossy compression as the eventual step after decomposition and quantization of a signal. In signal compression, the disintegration and quantization part seldom manages to harvest a progression of completely autonomous symbols....

Greedy Algorithms: In an optimization problem, we are given an input and asked to compute a structure, subject to various constraints, in a manner that either minimizes cost or maximizes profit. Such problems arise in many applications of science and engineering. Given an optimization problem, we are often faced with the question of whether the problem can be solved efficiently (as opposed to a...