Relevant bibliographies by topics / Lempel-Ziv-Welch (LZW) Algorithm

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Academic literature on the topic 'Lempel-Ziv-Welch (LZW) Algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 September 2023

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Journal articles on the topic "Lempel-Ziv-Welch (LZW) Algorithm"

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Watchanupaporn, Orawan, and Worasait Suwannik. "LZW Chromosome Encoding in Estimation of Distribution Algorithms." International Journal of Applied Evolutionary Computation 4, no. 4 (2013): 41–61. http://dx.doi.org/10.4018/ijaec.2013100103.

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Estimation of distribution algorithm (EDA) can solve more complicated problems than its predecessor (Genetic Algorithm). EDA uses various methods to probabilistically model a group of highly fit individuals. Calculating the model in sophisticated EDA is very time consuming. To reduce the model building time, the authors propose compressed chromosome encoding. A chromosome is encoded using a format that can be decompressed by the Lempel-Ziv-Welch (LZW) algorithm. The authors combined LZW encoding with various EDAs and termed the class of algorithms Lempel-Ziv-Welch Estimation of Distribution Algorithms (LZWEDA). Experimental results show that LZWEDA significantly outperforms the original EDA. Finally, the authors analyze how LZW encoding transforms a fitness landscape.
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Suharso, Aries, Jejen Zaelani, and Didi Juardi. "KOMPRESI FILE MENGGUNAKAN ALGORITMA LEMPEL ZIV WELCH (LZW)." Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika 17, no. 2 (2020): 372–80. http://dx.doi.org/10.33751/komputasi.v17i2.2147.

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In the field of information technology, data communication is closely related to file delivery. The size of the file is sometimes a constraint in the delivery process. Large files will take longer delivery times compared to files with smaller sizes. Therefore, to handle the problem, one of them by means of compression. This study uses an experimental method with a waterfall development model with analysis, design, coding, and testing. This application applies the Ziv Welch Lempel (LZW) algorithm. The Ziv Welch Lemp Algorithm (LZW) is included in the lossless compression technique, which is a compression technique that does not change the original data. The result of a compression assessment used the Lempel Ziv Welch (LZW) algorithm shows the average rate of compression ratio and for all types of text files by 51.04% with an average of 2.56 seconds, for an image file type of 37.26% with an average time of 0.44 seconds. Based on the average percentage of the compression ratio for all file types tested using the LZW algorithm (Lemp Ziv Welch) is 40.40% with an average time required is 1.81 seconds.
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Fauzan, Mohamad Nurkamal, Muhammad Alif, and Cahyo Prianto3. "Comparison of Huffman Algorithm and Lempel Ziv Welch Algorithm in Text File Compression." IT Journal Research and Development 7, no. 2 (2022): 155–69. http://dx.doi.org/10.25299/itjrd.2023.10437.

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The development of data storage hardware is very rapidly over time. In line with the development of storage hardware, the amount of digital data shared on the internet is increasing every day. That way no matter how big the size of the storage device we have, of course, it will only be a matter of time until that storage space is exhausted. Therefore, in terms of maximizing storage space, a technique called compression emerged. This study focuses on a comparative analysis of 2 Lossless compression technique algorithms, namely the Huffman algorithm and Lempel Ziv Welch (LZW). A number of test files with different file types are applied to both algorithms that are compared. The performance of the algorithm is determined based on the comparison of space saving and compression time. The test results showed that the Lempel Ziv Welch (LZW) algorithm was superior to Huffman’s algorithm in .txt file type compression and .csv, the average space savings produced were 63.85% and 77.56%. The degree of compression speed that each algorithm produces is directly proportional to the file size.
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Priya, C., T. Kesavamurthy, and M. Uma Priya. "An Efficient Lossless Medical Image Compression Using Hybrid Algorithm." Advanced Materials Research 984-985 (July 2014): 1276–81. http://dx.doi.org/10.4028/www.scientific.net/amr.984-985.1276.

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Recently many new algorithms for image compression based on wavelets have been developed.This paper gives a detailed explanation of SPIHT algorithm with the combination of Lempel Ziv Welch compression technique for image compression by MATLAB implementation. Set partitioning in Hierarchical trees (SPIHT) is one of the most efficient algorithm known today. Pyramid structures have been created by the SPIHT algorithm based on a wavelet decomposition of an image. Lempel Ziv Welch is a universal lossless data compression algorithm guarantees that the original information can be exactly reproduced from the compressed data.The proposed methods have better compression ratio, computational speed and good reconstruction quality of the image. To analysis the proposed lossless methods here, calculate the performance metrics as Compression ratio, Mean square error, Peak signal to Noise ratio. Key Words-LempelZivWelch (LZW),SPIHT,Wavelet
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Ma, Shaowen. "Comparison of image compression techniques using Huffman and Lempel-Ziv-Welch algorithms." Applied and Computational Engineering 5, no. 1 (2023): 793–801. http://dx.doi.org/10.54254/2755-2721/5/20230705.

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Image compression technology is very popular in the field of image analysis because the compressed image is convenient for storage and transmission. In this paper, the Huffman algorithm and Lempel-Ziv-Welch (LZW) algorithm are introduced. They are widely used in the field of image compression, and the compressed image results of the two algorithms are calculated and compared. Based on the four dimensions of Compression Ratio (CR), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Bits Per Pixel (BPP), the applicable conditions of the two algorithms in compressing small image files are analysed. The results illustrated that when the source image files are less than 300kb, the Compression Ratio (CR) of Huffman algorithm was better than that of LZW algorithm. However, for Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Bits Per Pixel (BPP), which are used to represent the compressed images qualities, LZW algorithm gave more satisfactory results.
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Zhang, Feng Yuan, Li Li Wen, Xiao Lu Jia, Zhao Li, Meng Li Chen, and Cheng Chen. "LZW Algorithm Research and Amelioration Based on Pointer Trace." Applied Mechanics and Materials 121-126 (October 2011): 4498–502. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4498.

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LZW (Lempel Ziv Welch) algorithm is a dictionary compression algorithm with excellent performance. The algorithm, which has important advantages including strong universal ability and a dynamically formed dictionary in coding and decoding, is used extensively in lossless data compression field. This paper implements an LZW algorithm based on a tree-like data structure in C and offers two optimization schemes, including using pointer-trace and Variable-length code. The results show that these schemes extremely improve the compression efficiency with reduced compression time cost and provide an effective guarantee for real time transmission.
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Fitra Wijayanto, Erick, Muhammad Zarlis, and Zakarias Situmorang. "Increase the PSNR Of Image Using LZW and AES Algorithm With MLSB on Steganography." International Journal of Engineering & Technology 7, no. 2.5 (2018): 119. http://dx.doi.org/10.14419/ijet.v7i2.5.13965.

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There are many research has done a hybridization approach of text message insertion that has been compressed with Lempel-Ziv-Welch (LZW) algorithm and has also been encrypted. The text messages in ciphertext form are inserted into the image file using LSB (Least Significant Bit) method. The results of this study indicate that the value of Peak Signal to Noise Ratio (PSNR) lower than the LSB method of 0.94 times with a ratio of 20.33%, with Kekre's method of 10.04%. To improve the value of PSNR stego image of insertion, in this research is inserted audio samples using 5 bits to reduce the amount of data insertion, so it can get the value of MSE stego image low. Prior to insertion, the text file is compressed with the Lempel-Ziv-Welch (LZW) algorithm and encrypted with the Advanced Encryption Standard (AES) algorithm. Then, the insertion of compression and encrypted text files is done with the Modified Least Significant Bit (MLSB) algorithm. To performa test reliability of steganography, the image stego image is done by calculating Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR). At extraction process with MLSB algorithm, decryption with AES algorithm and decompression with LZW algorithm. The experimental results show that the MSE values obtained are lower and the proposed PSNR method is better with (α) 1,044 times than the Kaur method, et al. The result of embed text file extraction from the stego image works well resulting in encrypted and uncompressed text files.
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Sudirman, Sudirman. "Enkripsi Citra Bitmap Menggunakan Algoritma Kompresi Lampel-Ziv-Welch (LZW)." ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA 4, no. 1 (2020): 9. http://dx.doi.org/10.30829/algoritma.v4i1.7242.

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<p><em>The use of data in the form of image files has become more widespread in various fields. Therefore, security of image files from unauthorized persons is important. Various techniques to reduce file size, including securing files, have been developed, one of which is compression. Compression technique becomes important in processing large images such as Bitmap images. Ziv Welch seal is a loseless compression algorithm that can compress images without any loss of pixel elements in them or the resulting compression image is identical to the original image. The image will be encrypted first and then performed the compression technique using the Ziv Welch Lempel algorithm. The resulting compression is no longer in the form of images but in the form of files with the * .mat extension. The different file formats from images to non-image files can help avoid cryptanalysis</em></p>
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King, G. R. Gnana, C. Christopher Seldev, and N. Albert Singh. "A Novel Compression Technique for Compound Images Using Parallel Lempel-Ziv-Welch Algorithm." Applied Mechanics and Materials 626 (August 2014): 44–51. http://dx.doi.org/10.4028/www.scientific.net/amm.626.44.

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Compound image is a combination of natural images, text, and graphics.This paper presents a compression technique for improving coding efficiency. The algorithm first decomposes the compound images by using 3 level biorthogonal wavelet transform and then the transformed image was further compressed by Parallel dictionary based LZW algorithm called PDLZW.In PDLZW algorithm instead of using a unique fixed word width dictionary a hierarchical variable word width dictionary set containing several dictionaries of small address space and increases the word widths used for compression and decompression algorithms. The experimental results show that the PSNR value is increased and the Mean Square error value was improved.
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Shirakol, Shrikanth, Akshata Koparde, Sandhya ., Shravan Kulkarni, and Yogesh Kini. "Performance optimization of dual stage algorithm for lossless data compression and decompression." International Journal of Engineering & Technology 7, no. 2.21 (2018): 127. http://dx.doi.org/10.14419/ijet.v7i2.21.11849.

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In this paper, an optimized dual stage architecture is proposed which is the combination of Lempel-Ziv-Welch (LZW) Algorithm at the first phase and Arithmetic Coding being the later part of Architecture. LZW Algorithm is a lossless compression algorithm and code here for each character is available in the dictionary which reduces 5-bits per cycle as compared to ASCII. In arithmetic coding the numbers are represented by an interval of real numbers from zero to one according to their probabilities. It is an entropy coding and is lossless in nature. The text information is allowed to pass through the proposed architecture and it gets compressed to the higher rate.
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Dissertations / Theses on the topic "Lempel-Ziv-Welch (LZW) Algorithm"

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Meira, Moab Mariz. "Classificação de Texturas usando o Algoritmo Lempel- Ziv-Welch." Universidade Federal da Paraí­ba, 2008. http://tede.biblioteca.ufpb.br:8080/handle/tede/6085.

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Made available in DSpace on 2015-05-14T12:36:37Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 1769968 bytes, checksum: 0ecd162fc2e21d4f6f321e359943426b (MD5) Previous issue date: 2008-02-29<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>Este trabalho apresenta um novo e eficiente método de classificação de texturas usando o algoritmo de compressão sem perdas Lempel-Ziv-Welch (LZW). Na fase de aprendizagem, o LZW constrói dicionários para as estruturas horizontal e vertical de cada classe de textura. Na fase de classificação, amostras de texturas são codificadas com o LZW no modo estático, usando os dicionários construídos na fase anterior. Uma amostra é associada à classe cujo dicionário conduz à melhor taxa de codificação. O classificador foi avaliado para vários tamanhos do conjunto de treinamento e das amostras de treinamento, e sob diferentes condições de iluminação das texturas. O método proposto atinge 100% de acerto em alguns experimentos usando amostras de texturas do álbum Brodatz. Comparações diretas com outros trabalhos indicam a superioridade do método sobre outros métodos de alto desempenho.
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Basavaraja, S. V. "Robust Techniques Of Language Modeling For Spoken Language Identification." Thesis, 2007. http://etd.iisc.ac.in/handle/2005/571.

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Language Identification (LID) is the task of automatically identifying the language of speech signal uttered by an unknown speaker. An N language LID task is to classify an input speech utterance, spoken by an unknown speaker and of unknown text, as belonging to one of the N languages L1, L2, . . , LN. We present a new approach to spoken language modeling for language identification using the Lempel-Ziv-Welch (LZW) algorithm, with which we try to overcome the limitations of n-gram stochastic models by automatically identifying the valid set of variable length patterns from the training data. However, since several patterns in a language pattern table are also shared by other language pattern tables, confusability prevailed in the LID task. To overcome this, three pruning techniques are proposed to make these pattern tables more language specific. For LID with limited training data, we present another language modeling technique, which compensates for language specific patterns missing in the language specific LZW pattern table. We develop two new discriminative measures for LID based on the LZW algorithm, viz., (i) Compression Ratio Score (LZW-CRS) and (ii) Weighted Discriminant Score (LZW-WDS). It is shown that for a 6-language LID task of the OGI-TS database, the new model (LZW-WDS) significantly outperforms the conventional bigram approach. With regard to the front end of the LID system, we develop a modified technique to model for Acoustic Sub-Word Units (ASWU) and explore its effectiveness. The segmentation of speech signal is done using an acoustic criterion (ML-segmentation). However, we believe that consistency and discriminability among speech units is the key issue for the success of ASWU based speech processing. We develop a new procedure for clustering and modeling the segments using sub-word GMMs. Because of the flexibility in choosing the labels for the sub-word units, we do an iterative re-clustering and modeling of the segments. Using a consistency measure of labeling the acoustic segments, the convergence of iterations is demonstrated. We show that the performance of new ASWU based front-end and the new LZW based back-end for LID outperforms the earlier reported PSWR based LID.
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Basavaraja, S. V. "Robust Techniques Of Language Modeling For Spoken Language Identification." Thesis, 2007. http://hdl.handle.net/2005/571.

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Language Identification (LID) is the task of automatically identifying the language of speech signal uttered by an unknown speaker. An N language LID task is to classify an input speech utterance, spoken by an unknown speaker and of unknown text, as belonging to one of the N languages L1, L2, . . , LN. We present a new approach to spoken language modeling for language identification using the Lempel-Ziv-Welch (LZW) algorithm, with which we try to overcome the limitations of n-gram stochastic models by automatically identifying the valid set of variable length patterns from the training data. However, since several patterns in a language pattern table are also shared by other language pattern tables, confusability prevailed in the LID task. To overcome this, three pruning techniques are proposed to make these pattern tables more language specific. For LID with limited training data, we present another language modeling technique, which compensates for language specific patterns missing in the language specific LZW pattern table. We develop two new discriminative measures for LID based on the LZW algorithm, viz., (i) Compression Ratio Score (LZW-CRS) and (ii) Weighted Discriminant Score (LZW-WDS). It is shown that for a 6-language LID task of the OGI-TS database, the new model (LZW-WDS) significantly outperforms the conventional bigram approach. With regard to the front end of the LID system, we develop a modified technique to model for Acoustic Sub-Word Units (ASWU) and explore its effectiveness. The segmentation of speech signal is done using an acoustic criterion (ML-segmentation). However, we believe that consistency and discriminability among speech units is the key issue for the success of ASWU based speech processing. We develop a new procedure for clustering and modeling the segments using sub-word GMMs. Because of the flexibility in choosing the labels for the sub-word units, we do an iterative re-clustering and modeling of the segments. Using a consistency measure of labeling the acoustic segments, the convergence of iterations is demonstrated. We show that the performance of new ASWU based front-end and the new LZW based back-end for LID outperforms the earlier reported PSWR based LID.
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Conference papers on the topic "Lempel-Ziv-Welch (LZW) Algorithm"

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Shujun Li, Chengqing Li, and Jay C. C. Kuo. "On the security of a secure Lempel-Ziv-Welch (LZW) algorithm." In 2011 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2011. http://dx.doi.org/10.1109/icme.2011.6011939.

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Jiantao Zhou, Oscar C. Au, Xiaopeng Fan, and Peter Hon-Wah Wong. "Secure Lempel-Ziv-Welch (LZW) algorithm with random dictionary insertion and permutation." In 2008 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2008. http://dx.doi.org/10.1109/icme.2008.4607417.

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Shukla, Sankalp, Maniram Ahirwar, Ritu Gupta, Sarthak Jain, and Dheeraj Singh Rajput. "Audio Compression Algorithm using Discrete Cosine Transform (DCT) and Lempel-Ziv-Welch (LZW) Encoding Method." In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). IEEE, 2019. http://dx.doi.org/10.1109/comitcon.2019.8862228.

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PM., Nishad, and R. Manika Chezian. "Behavioral Study of Data Structures on Lempel Ziv Welch (LZW) Data Compression Algorithm and ITS Computational Complexity." In 2014 International Conference on Intelligent Computing Applications (ICICA). IEEE, 2014. http://dx.doi.org/10.1109/icica.2014.64.

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