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Relevant bibliographies by topics / Huffman coding

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Academic literature on the topic 'Huffman coding'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 April 2022

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Journal articles on the topic "Huffman coding"

1

Moffat, Alistair. "Huffman Coding." ACM Computing Surveys 52, no. 4 (September 18, 2019): 1–35. http://dx.doi.org/10.1145/3342555.

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Dr. Atul Suryavanshi. "Optimized Adaptive Huffmann Coding For Paper Reduction in OFDM Systems." International Journal of New Practices in Management and Engineering 2, no. 04 (December 31, 2013): 13–18. http://dx.doi.org/10.17762/ijnpme.v2i04.23.

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The main defect of OFDM systems is its high peak-to-average power ratio (PAPR). To decrease PAPR, Adaptive Huffman coding is essential. Encoding is transferred by two encoding techniques Huffman coding and Adaptive Huffman coding at the transmitter side. Mapping is done by QAM 16 and PSK 16.The PAPR results of Huffman and adaptive Huffman coding with QAM 16 and PSK 16 is compared. Simulation results shows that the Adaptive Huffman coding along with QAM 16 produces fruitful results in comparison with Huffman coding and adaptive Huffman coding with PSK 16.

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Abdul Mannan, M., and M. Kaykobad. "Block huffman coding." Computers & Mathematics with Applications 46, no. 10-11 (November 2003): 1581–87. http://dx.doi.org/10.1016/s0898-1221(03)90193-3.

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Fraenkel, A. S. "Bidirectional Huffman Coding." Computer Journal 33, no. 4 (April 1, 1990): 296–307. http://dx.doi.org/10.1093/comjnl/33.4.296.

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Knuth, Donald E. "Dynamic huffman coding." Journal of Algorithms 6, no. 2 (June 1985): 163–80. http://dx.doi.org/10.1016/0196-6774(85)90036-7.

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Yang, Xiao Bo, and Bang Ze Chen. "Huffman Tree Visualization." Advanced Materials Research 468-471 (February 2012): 1883–86. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.1883.

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Huffman tree is also called the optimal binary tree, is a kind of weighted shortest path length of the binary tree; Huffman coding is a coding method, which is used for a lossless data compression entropy coding ( right encoding ) optimal coding method. The realization of Huffman tree visualization is of great significance, this paper uses the object-oriented method, using a complete binary tree of Huffman tree visualization, visual image display of the Huffman coding process.

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Amarunnishad, T. M., V. K. Govindan, and Abraham T. Mathew. "Block Truncation Coding with Huffman Coding." Journal of Medical Imaging and Health Informatics 1, no. 2 (June 1, 2011): 170–76. http://dx.doi.org/10.1166/jmihi.2011.1021.

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Sridhara, Deepak. "Efficient coding of information: Huffman coding." Resonance 11, no. 2 (February 2006): 51–73. http://dx.doi.org/10.1007/bf02837275.

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Lakhani, G. "Modified JPEG huffman coding." IEEE Transactions on Image Processing 12, no. 2 (February 2003): 159–69. http://dx.doi.org/10.1109/tip.2003.809001.

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Mohr, August. "Huffman coding as metaphor." Kybernetes 42, no. 9/10 (November 11, 2013): 1431–38. http://dx.doi.org/10.1108/k-10-2012-0068.

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Dissertations / Theses on the topic "Huffman coding"

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Kilic, Suha. "Modification of Huffman Coding." Thesis, Monterey, California. Naval Postgraduate School, 1985. http://hdl.handle.net/10945/21449.

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Zou, Xin. "Compression and Decompression of Color MRI Image by Huffman Coding." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17029.

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MRI image (Magnetic Resonance Imaging) as a universal body checkup method in modern medicine. It can help doctors to analyze the condition of patients as soon as possible. As the medical images, the MRI images have high quality and a large amount of data, which requires more transmission time and larger storage capacity. To reduce transmission time and storage capacity, the compression and decompression technology is applied. Now most MRI images are colour, but most theses still use gray MRI images to research. Compressed color MRI images is a new research area. In this thesis, some basic theories of the compression technoloy and medical technology were firstly introduced, then basic strcture and kernel algorithm of Huffman coding were explained in detail. Finally, Huffman coding was implemented in MATLAB to compress and decompress the colour MRI images.The result of the experiment shows that the Huffman coding in colour MRI image compression can get high compression ratio and coding efficient.

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Machado, Lennon de Almeida. "Busca indexada de padrões em textos comprimidos." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-09062010-222653/.

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A busca de palavras em uma grande coleção de documentos é um problema muito recorrente nos dias de hoje, como a própria utilização dos conhecidos \"motores de busca\" revela. Para que as buscas sejam realizadas em tempo que independa do tamanho da coleção, é necessário que a coleção seja indexada uma única vez. O tamanho destes índices é tipicamente linear no tamanho da coleção de documentos. A compressão de dados é outro recurso bastante utilizado para lidar com o tamanho sempre crescente da coleção de documentos. A intenção deste estudo é aliar a indexação utilizada nas buscas à compressão de dados, verificando alternativas às soluções já propostas e visando melhorias no tempo de resposta das buscas e no consumo de memória utilizada nos índices. A análise das estruturas de índice com os algoritmos de compressão mostra que arquivo invertido por blocos em conjuntos com compressão Huffman por palavras é uma ótima opção para sistemas com restrição de consumo de memória, pois proporciona acesso aleatório e busca comprimida. Neste trabalho também são propostas novas codificações livres de prefixo a fim de melhorar a compressão obtida e capaz de gerar códigos auto-sincronizados, ou seja, com acesso aleatório realmente viável. A vantagem destas novas codificações é que elas eliminam a necessidade de gerar a árvore de codificação Huffman através dos mapeamentos propostos, o que se traduz em economia de memória, codificação mais compacta e menor tempo de processamento. Os resultados obtidos mostram redução de 7% e 9% do tamanho dos arquivos comprimidos com tempos de compressão e descompressão melhores e menor consumo de memória.
Pattern matching over a big document collection is a very recurrent problem nowadays, as the growing use of the search engines reveal. In order to accomplish the search in a period of time independent from the collection size, it is necessary to index the collecion only one time. The index size is typically linear in the size of document collection. Data compression is another powerful resource to manage the ever growing size of the document collection. The objective in this assignment is to ally the indexed search to data compression, verifying alternatives to the current solutions, seeking improvement in search time and memory usage. The analysis on the index structures and compression algorithms indicates that joining the block inverted les with Huffman word-based compression is an interesting solution because it provides random access and compressed search. New prefix free codes are proposed in this assignment in order to enhance the compression and facilitate the generation of self-sinchronized codes, furthermore, with a truly viable random access. The advantage in this new codes is that they eliminate the need of generating the Huffman-code tree through the proposed mappings, which stands for economy of memory, compact encoding and shorter processing time. The results demonstrate gains of 7% and 9% in the compressed le size, with better compression and decompression times and lower memory consumption.

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Kailasanathan, Chandrapal. "Securing digital images." Access electronically, 2003. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20041026.150935/index.html.

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Lúdik, Michal. "Porovnání hlasových a audio kodeků." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219793.

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This thesis deals with description of human hearing, audio and speech codecs, description of objective measure of quality and practical comparison of codecs. Chapter about audio codecs consists of description of lossless codec FLAC and lossy codecs MP3 and Ogg Vorbis. In chapter about speech codecs is description of linear predictive coding and G.729 and OPUS codecs. Evaluation of quality consists of description of segmental signal-to- noise ratio and perceptual evaluation of quality – WSS and PESQ. Last chapter deals with description od practical part of this thesis, that is comparison of memory and time consumption of audio codecs and perceptual evaluation of speech codecs quality.

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Friedrich, Tomáš. "Komprese DNA sekvencí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237222.

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The increasing volume of biological data requires finding new ways to save these data in genetic banks. The target of this work is design and implementation of a novel algorithm for compression of DNA sequences. The algorithm is based on aligning DNA sequences agains a reference sequence and storing only diferencies between sequence and reference model. The work contains basic prerequisities from molecular biology which are needed for understanding of algorithm details. Next aligment algorithms and common compress schemes suitable for storing of diferencies agains reference sequence are described. The work continues with a description of implementation, which is follewed by derivation of time and space complexity and comparison with common compression algorithms. Further continuation of this thesis is discussed in conclusion.

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Románek, Karel. "Nezávislý datalogger s USB připojením." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219113.

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This thesis treats concept of autonomous temperature, relative humidity and pressure USB datalogger. Also there is explained datalogger function, hardware design with respect on device consumption and design of chassis. Furthermore, there is described communication protocol for control and reading out data by the PC. Furthermore, there are described firmware drivers for some used components and modules for USB communication, RTC and data compression. Lastly there is described software which is used for datalogger configuration and data read out.

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Had, Filip. "Komprese signálů EKG nasnímaných pomocí mobilního zařízení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316832.

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Signal compression is necessary part for ECG scanning, because of relatively big amount of data, which must be transmitted primarily wirelessly for analysis. Because of the wireless sending it is necessary to minimize the amount of data as much as possible. To minimize the amount of data, lossless or lossy compression algorithms are used. This work describes an algorithm SPITH and newly created experimental method, based on PNG, and their testing. This master’s thesis there is also a bank of ECG signals with parallel sensed accelerometer data. In the last part, modification of SPIHT algorithm, which uses accelerometer data, is described and realized.

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Krejčí, Michal. "Komprese dat." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217934.

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This thesis deals with lossless and losing methods of data compressions and their possible applications in the measurement engineering. In the first part of the thesis there is a theoretical elaboration which informs the reader about the basic terminology, the reasons of data compression, the usage of data compression in standard practice and the division of compression algorithms. The practical part of thesis deals with the realization of the compress algorithms in Matlab and LabWindows/CVI.

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Ondra, Josef. "Komprese signálů EKG s využitím vlnkové transformace." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217209.

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Signal compression is daily-used tool for memory capacities reduction and for fast data communication. Methods based on wavelet transform seem to be very effective nowadays. Signal decomposition with a suitable bank filters following with coefficients quantization represents one of the available technique. After packing quantized coefficients into one sequence, run length coding together with Huffman coding are implemented. This thesis focuses on compression effectiveness for the different wavelet transform and quantization settings.

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Books on the topic "Huffman coding"

1

Kilic, Suha. Modification of Huffman Coding. Monterey, California: Naval Postgraduate School, 1985.

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2

Manstetten, Dietrich. Schranken für die Redundanz des Huffman-Codes. Heidelberg: A. Hüthig, 1988.

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Handbook of Coding Theory. Amsterdam: Elsevier, 1998.

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Corapcioglu, Ahmet. Reduction in bandwidth and buffer size by using modified Huffman coding after dropping the less frequent source symbols. 1987.

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Book chapters on the topic "Huffman coding"

1

You, Yuli. "Huffman Coding." In Audio Coding, 161–70. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1754-6_9.

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2

Weik, Martin H. "Huffman coding." In Computer Science and Communications Dictionary, 738. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_8512.

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Moffat, Alistair. "Huffman Coding." In Encyclopedia of Algorithms, 938–42. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_633.

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Moffat, Alistair. "Huffman Coding." In Encyclopedia of Algorithms, 1–6. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27848-8_633-1.

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Klein, Shmuel Tomi, Shoham Saadia, and Dana Shapira. "Forward Looking Huffman Coding." In Computer Science – Theory and Applications, 203–14. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19955-5_18.

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Battiato, S., C. Bosco, A. Bruna, G. Di Blasi, and G. Gallo. "Statistical Modeling of Huffman Tables Coding." In Image Analysis and Processing – ICIAP 2005, 711–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11553595_87.

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Maurya, Indu, and S. K. Gupta. "Understandable Huffman Coding: A Case Study." In Advances in Intelligent Systems and Computing, 147–58. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0589-4_14.

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Lee, Chin-Feng, Chin-Chen Chang, and Qun-Feng Zeng. "VQ Compression Enhancer with Huffman Coding." In Advances in Intelligent Systems and Computing, 101–8. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6487-6_13.

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Kekre, H. B., Sanjay R. Sange, Gauri S. Sawant, and Ankit A. Lahoty. "Image Compression Using Halftoning and Huffman Coding." In Communications in Computer and Information Science, 221–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20209-4_31.

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Li, Ranran, Yongbin Zhao, Qing Xu, and Xiaolin Qi. "Research of Subnetting Based on Huffman Coding." In Cloud Computing and Security, 606–16. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00018-9_53.

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Conference papers on the topic "Huffman coding"

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Arshad, Rabia, Adeel Saleem, and Danista Khan. "Performance comparison of Huffman Coding and Double Huffman Coding." In 2016 Sixth International Conference on Innovative Computing Technology (INTECH). IEEE, 2016. http://dx.doi.org/10.1109/intech.2016.7845058.

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Fruchtman, Aharon, Yoav Gross, Shmuel T. Klein, and Dana Shapira. "Weighted Adaptive Huffman Coding." In 2020 Data Compression Conference (DCC). IEEE, 2020. http://dx.doi.org/10.1109/dcc47342.2020.00059.

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Brzuchalski, Grzegorz. "Huffman coding in advanced audio coding standard." In Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2012, edited by Ryszard S. Romaniuk. SPIE, 2012. http://dx.doi.org/10.1117/12.2000138.

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Klein, Shmuel T., Shoham Saadia, and Dana Shapira. "Better Than Optimal Huffman Coding?" In 2019 Data Compression Conference (DCC). IEEE, 2019. http://dx.doi.org/10.1109/dcc.2019.00094.

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Bookstein, Abraham, Shmuel T. Klein, and Timo Raita. "Is Huffman coding dead? (extended abstract)." In the 16th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 1993. http://dx.doi.org/10.1145/160688.160697.

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Klein, Shmuel T., and Dana Shapira. "Huffman Coding with Non-sorted Frequencies." In 2008 Data Compression Conference DCC. IEEE, 2008. http://dx.doi.org/10.1109/dcc.2008.73.

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Wang, Wei, and Wei Zhang. "Adaptive Spatial Modulation Using Huffman Coding." In GLOBECOM 2016 - 2016 IEEE Global Communications Conference. IEEE, 2016. http://dx.doi.org/10.1109/glocom.2016.7841569.

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Golin, Mordecai J., Claire Kenyon, and Neal E. Young. "Huffman coding with unequal letter costs." In the thiry-fourth annual ACM symposium. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/509907.510020.

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Baer, Michael B. "D-ary Bounded-Length Huffman Coding." In 2007 IEEE International Symposium on Information Theory. IEEE, 2007. http://dx.doi.org/10.1109/isit.2007.4557338.

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Tamir, Dan. "Delta-Huffman Coding of Unbounded Integers." In 2018 Data Compression Conference (DCC). IEEE, 2018. http://dx.doi.org/10.1109/dcc.2018.00081.

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