minimumredundancy coding (also known as Huffman coding) is one of the enduring techniques of data compression. Many efforts have been made to improve the efficiency of minimumredundancy coding, the majority based on...
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minimumredundancy coding (also known as Huffman coding) is one of the enduring techniques of data compression. Many efforts have been made to improve the efficiency of minimumredundancy coding, the majority based on the use of improved representations for explicit Huffman trees. In this paper, we examine how minimumredundancy coding can be implemented efficiently by divorcing coding from a code tree, with emphasis on the situation when n is large, perhaps on the order of 10(6). We review techniques for devising minimum redundancy codes, and consider in detail how encoding and decoding should be accomplished. In particular, we describe a modified decoding method that allows improved decoding speed, requiring just a few machine operations per output symbol (rather than for each decoded bit), and uses just a few hundred bytes of memory above and beyond the space required to store an enumeration of the source alphabet.
minimumredundancy coding (also known as Huffman code) is one of the most well-known algorithm of data compression. Many efforts have been made to improve the efficiency of it. Most of them are based on the assumption...
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ISBN:
(纸本)9781479959709
minimumredundancy coding (also known as Huffman code) is one of the most well-known algorithm of data compression. Many efforts have been made to improve the efficiency of it. Most of them are based on the assumption that the input alphabet has been already sorted. In this paper, we propose an algorithm of calculating the minimum-redundancycodes directly with unsorted alphabet. It consumes only O(nlog(n/k)) time in the worst cases, where n is the alphabet size and k is the longest codeword length. It is fast because only a part of the symbols requires to be sorted before the final minimum redundancy code is generated. The theoretical analysis and numerical simulation results show that this algorithm achieves a substantial improvement upon the best previous O(nlogn) algorithms for this problem.
An algorithm for constructing an optimal prefix code of n eqmprobable words over r unequal cost coding letters is given. The discussion is in terms of rooted labeled trees. The algorithm consists of two parts. The fir...
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