Huffman Based Code Generation Algorithms: Data Compression Perspectives

Habib, Ahsan; Islam, Monirul; Rahman, M. Shahidur

doi:10.3844/jcssp.2018.1599.1610

Cited by 7 publications

(8 citation statements)

References 17 publications

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“…In 2018, Habib et al [7] published a paper on tribit (octernary tree) and quadbit (hexadecimal tree) Huffman encoding and compared it with previously proposed dualbit (quaternary tree) and the Zopfli algorithm [8]. They did not coincide with our experimentation results.…”

Section: Introductionmentioning

confidence: 54%

N-ary Huffman Encoding using High-Degree Trees - A Performance Comparison

Xezonakis¹,

Leivadaros²

2021

IJCA

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In this paper we implement an n-ary Huffman Encoding and Decoding application using different degrees of tree structures. Our goal is to compare the performance of the algorithm in terms of compression ratio, decompression speed and weighted path length when using higher degree trees, compared to the 2-ary Huffman Code. The Huffman tree degrees that we compare are 2-ary, 3-ary, 4-ary, 5-ary, 6-ary, 7-ary, 8-ary and 16-mal. We also present the impact that branch prediction has on the performance of the n-ary Huffman Decoding.

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Section: Introductionmentioning

confidence: 54%

N-ary Huffman Encoding using High-Degree Trees - A Performance Comparison

Xezonakis¹,

Leivadaros²

2021

IJCA

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“…This research has analysed the performance of algorithms that have been made with Huffman-based techniques in terms of decoding speed and compression ratio. This study shows that the proposed decoding algorithm outperforms the Huffman-based technique in terms of speed while compression performance remains almost the same [3].…”

Section: Figure 1 Huffman Algorithm Flowchartmentioning

confidence: 72%

“…After assessing all the results, it was found that the Octonary and the Hexanary technique performed better than the existing techniques in terms of encoding and decoding speed. Use MPLS-TE for multimedia applications [3].…”

Section: Figure 1 Huffman Algorithm Flowchartmentioning

confidence: 99%

Feasibility Study of Using Huffman Code Calculator in Learning Achievement of Data Compression

2021

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In the world of education there is already a curriculum that must be carried out with learning outcomes that must be achieved by students, as well as lecturers who also have duties as facilitators in these achievements. With the Learning Outcomes, it makes students have the ability or competence in accordance with the requirements to meet the predefined study program profiles. One of the competencies that graduates of the Broadband Multimedia field must achieve is in the field of data processing including data compression. The most widely used data compression technique is the Huffman code. Some research on the Huffman code has been done a lot, and someone has made the Calculator application. The calculator is used to determine the code for each character. The code for each character is not fixed, depending on the frequency of appearance in a text. The more often the character appears, the shorter the code. In this study, it has proven that the feasibility of the Huffman Calculator as a tool for Learning Outcomes can be done where data compression with language variables, in this case English and Bahasa Indonesia to the compression ratio, produces the same compression ratio, which ranges from 1.51 to 1.64 for Bahasa Indonesia while English is 1.56 to 1.83, with the average entropy for English 4.35 and Bahasa Indonesia 4.41. Keywords: competence , data compression, huffman code, learning outcomes.

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“…As we know, the traditional Huffman can construct a binary tree based on the frequency of the symbols shown in Figure 4, but the tree is not balanced [21], [28] though. Meanwhile, consider the first sample text for the transliterated English from Table II.…”

Section: B Decoding Time Complexity For Regular Huffmanmentioning

confidence: 99%

“…It can generate an optimum codeword so the tree can follow the 2-bit process that helps to perform the decoding on O(log 4 n) in the opposite of O(log 2 n) for traditional binary Huffman. Habib et al, [21] further upgraded their research and proposed Octanary (tribitbased) and Hexanary (quadbit-based) approaches that outperform both encoding and decoding time for some existing methods Zopfli and quaternary.…”

mentioning

confidence: 99%

Method of Adjacent Distance Array Outperforms Conventional Huffman Codes to Decode Bengali Transliterated Text Swiftly

Sarker¹,

Rahman²

2022

IJCDS

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This research works on high symbolic Bengali text and transforms it into corresponding less symbolic English complying with the transliteration method. The Huffman-based approaches serve to compress retaining the original quality of the data. On the other hand, faster encoding and decoding is the most sophisticated sphere in data compression. We propose an adjacent distance array, a novel data structure based on the Huffman principle for encoding and decoding the character of transliterated text. The encoding and decoding algorithms have been explained for the introduced modus operandi and juxtaposed with conventional Huffman-based algorithms. Our research is outdoing than any regular Huffman-based algorithms, concentrating on the speed of the encoding and decoding manner discovered after estimating all decisions.

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Huffman Based Code Generation Algorithms: Data Compression Perspectives

Cited by 7 publications

References 17 publications

N-ary Huffman Encoding using High-Degree Trees - A Performance Comparison

N-ary Huffman Encoding using High-Degree Trees - A Performance Comparison

Feasibility Study of Using Huffman Code Calculator in Learning Achievement of Data Compression

Method of Adjacent Distance Array Outperforms Conventional Huffman Codes to Decode Bengali Transliterated Text Swiftly

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