Enhancing Computational Time of Lempel-Ziv-Welch-Based Text Compression with Chinese Remainder Theorem.

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
MB Ibrahim ◽  
KA Gbolagade
Keyword(s):  
Data Compression ◽  
Compression Ratio ◽  
Performance Metrics ◽  
Chinese Remainder Theorem ◽  
Computational Time ◽  
Compact Representation ◽  
Text Compression ◽  
Compression Time ◽  
Lzw Algorithm ◽  
Better Than

The science and art of data compression is presenting information in a compact form. This compact representation of information is generated by recognizing the use of structures that exist in the data. The Lempel-Ziv-Welch (LZW) algorithm is known to be one of the best compressors of text which achieve a high degree of compression. This is possible for text files with lots of redundancies. Thus, the greater the redundancies, the greater the compression achieved. In this paper, the LZW algorithm is further enhanced to achieve a higher degree of compression without compromising its performances through the introduction of an algorithm, called Chinese Remainder Theorem (CRT), is presented. Compression Time and Compression Ratio was used for performance metrics. Simulations was carried out using MATLAB for five (5) text files (of varying sizes) in determining the efficiency of the proposed CRT-LZW technique. This new technique has opened a new development of increasing the speed of compressing data than the traditional LZW. The results show that the CRT-LZW performs better than LZW in terms of computational time by 0.12s to 15.15s, while the compression ratio remains same with 2.56% respectively. The proposed compression time also performed better than some investigative papers implementing LZW-RNS by 0.12s to 2.86s and another by 0.12s to 0.14s. Keywords: Data Compression, Lempel-Ziv-Welch (LZW) algorithm, Enhancement, Chinese Remainder Theorem (CRT), Text files.

scholarly journals Comparison of Entropy and Dictionary Based Text Compression in English, German, French, Italian, Czech, Hungarian, Finnish, and Croatian

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1059
Author(s):  
Matea Ignatoski ◽  
Jonatan Lerga ◽  
Ljubiša Stanković ◽  
Miloš Daković
Keyword(s):  
Data Compression ◽  
Compression Ratio ◽  
Data Transmission ◽  
Storage Capacity ◽  
Text Compression ◽  
Video Content ◽  
Text File ◽  
Digital World ◽  
Arithmetic Algorithm ◽  
Lzw Algorithm

The rapid growth in the amount of data in the digital world leads to the need for data compression, and so forth, reducing the number of bits needed to represent a text file, an image, audio, or video content. Compressing data saves storage capacity and speeds up data transmission. In this paper, we focus on the text compression and provide a comparison of algorithms (in particular, entropy-based arithmetic and dictionary-based Lempel–Ziv–Welch (LZW) methods) for text compression in different languages (Croatian, Finnish, Hungarian, Czech, Italian, French, German, and English). The main goal is to answer a question: ”How does the language of a text affect the compression ratio?” The results indicated that the compression ratio is affected by the size of the language alphabet, and size or type of the text. For example, The European Green Deal was compressed by 75.79%, 76.17%, 77.33%, 76.84%, 73.25%, 74.63%, 75.14%, and 74.51% using the LZW algorithm, and by 72.54%, 71.47%, 72.87%, 73.43%, 69.62%, 69.94%, 72.42% and 72% using the arithmetic algorithm for the English, German, French, Italian, Czech, Hungarian, Finnish, and Croatian versions, respectively.

Comparison of Image Compression Methods for Image Transmission Over Wireless Sensor Network

2019 ◽  
Vol 16 (9) ◽  
pp. 3912-3916 ◽  
Author(s):  
Rekha Dalia ◽  
Rajeev Gupta
Keyword(s):  
Wireless Sensor Network ◽  
Image Compression ◽  
Sensor Network ◽  
Compression Ratio ◽  
Performance Metrics ◽  
Cost Effective ◽  
Image Transmission ◽  
Wireless Sensor ◽  
Compression Time ◽  
Image Compression Algorithm

Unlike conventional networks, in Wireless Sensor Network the nodes have constrained energy, memory and processing capabilities. These nodes deployed in a constrained environment monitor any changes in surrounding environment and transfer the changes to the cluster heads. Each node has its own memory, battery, and transceivers. Efficient utilization of these resources can result in the enhancement of network lifetime. In order to securely transfer the data in the form of images, an efficient and cost effective image compression algorithm is required. Hence, in this paper, a detailed review of image compression algorithms has been carried out. The selected algorithms are compared in terms of various performance metrics such as compression ratio, compression time, speed, type of data, etc. The results showed that algorithm proposed by Borici and Arber is the best in case of compression ratio, as it provides better compression ratio in comparison to other algorithms.

scholarly journals A Syllable-Based Technique for Uyghur Text Compression

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 172 ◽  
Author(s):  
Wayit Abliz ◽  
Hao Wu ◽  
Maihemuti Maimaiti ◽  
Jiamila Wushouer ◽  
Kahaerjiang Abiderexiti ◽  
...  
Keyword(s):  
Compression Ratio ◽  
Data Transmission ◽  
Text Compression ◽  
Compression Process ◽  
Short Text ◽  
Coding Scheme ◽  
Coding Schemes ◽  
Code Table ◽  
Compression Coding ◽  
Lzw Algorithm

To improve utilization of text storage resources and efficiency of data transmission, we proposed two syllable-based Uyghur text compression coding schemes. First, according to the statistics of syllable coverage of the corpus text, we constructed a 12-bit and 16-bit syllable code tables and added commonly used symbols—such as punctuation marks and ASCII characters—to the code tables. To enable the coding scheme to process Uyghur texts mixed with other language symbols, we introduced a flag code in the compression process to distinguish the Unicode encodings that were not in the code table. The experiments showed that the 12-bit coding scheme had an average compression ratio of 0.3 on Uyghur text less than 4 KB in size and that the 16-bit coding scheme had an average compression ratio of 0.5 on text less than 2 KB in size. Our compression schemes outperformed GZip, BZip2, and the LZW algorithm on short text and could be effectively applied to the compression of Uyghur short text for storage and applications.

scholarly journals Intensification of Lempel-ZIV-Welch Algorithm

2019 ◽  
Vol 8 (9S) ◽  
pp. 587-591
Keyword(s):  
Data Compression ◽  
Compression Ratio ◽  
Text Compression ◽  
Code Length ◽  
File Size ◽  
Compression Scheme ◽  
Compression Algorithms ◽  
Original Algorithm ◽  
Using Data

There is a necessity to reduce the consumption of exclusive resources. This is achieved using data compression. The data compression is one well known technique which can reduce the file size. A plethora of data compression algorithms are available which provides compression in various ratios. LZW is one of the powerful widely used algorithms. This paper attempts to propose and apply some enhancements to LZW, hence comes out with an efficient lossless text compression scheme that can compress a given file at better compression ratio. The paper proposes three approaches which practically enhances the original algorithm. These approaches try to gain better compression ratio. In approach1, it exploits the notion of using existing string code with odd code for a newly encounter string which is reverse of existing. In approach2 it uses a choice of code length for the current compression, so avoiding the problem of dictionary overflow. In approach3 it appends some selective set of frequently encountered string patterns. So the intensified LZW method provides better compression ratio with the inclusion of the above features.

scholarly journals Performance Evaluation of Data Compression Algorithms for IoT-Based Smart Water Network Management Applications

2020 ◽  
Vol 7 (2) ◽  
pp. 554-563
Author(s):  
Kazeem B. Adedeji
Keyword(s):  
Performance Evaluation ◽  
Water Supply ◽  
Network Management ◽  
Data Compression ◽  
Compression Ratio ◽  
Water Network ◽  
Compression Algorithms ◽  
Compression Factor ◽  
Smart Water ◽  
Lzw Algorithm

IoT-based smart water supply network management applications generate a huge volume of data from the installed sensing devices which are required to be processed (sometimes in-network), stored and transmitted to a remote centre for decision making. When the volume of data produced by diverse IoT smart sensing devices intensify, processing and storage of these data begin to be a serious issue. The large data size acquired from these applications increases the computational complexities, occupies the scarce bandwidth of data transmission and increases the storage space. Thus, data size reduction through the use of data compression algorithms is essential in IoT-based smart water network management applications. In this paper, the performance evaluation of four different data compression algorithms used for this purpose is presented. These algorithms, which include RLE, Huffman, LZW and Shanon-Fano encoding were realised using MATLAB software and tested on six water supply system data. The performance of each of these algorithms was evaluated based on their compression ratio, compression factor, percentage space savings, as well as the compression gain. The results obtained showed that the LZW algorithm shows better performance base on the compression ratio, compression factor, space savings and the compression gain. However, its execution time is relatively slow compared to the RLE and the two other algorithms investigated. Most importantly, the LZW algorithm has a significant reduction in the data sizes of the tested files than all other algorithms

scholarly journals Implementation of Text Compression using Adaptive Shannon-Fano Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 3984-3990
Keyword(s):  
Data Compression ◽  
Compression Ratio ◽  
Original Text ◽  
Text Compression ◽  
Test Results ◽  
Time Data ◽  
Compression Process ◽  
Text Input ◽  
Before And After ◽  
Black Box Testing

This study aims to implement the Shannon-fano Adaptive data compression algorithm on characters as input data. This study also investigates the data compression ratio, which is the ratio between the number of data bits before and after compression. The resulting program is tested by using black-box testing, measuring the number of character variants and the number of types of characters to the compression ratio, and testing the objective truth with the Mean Square Error (MSE) method. The description of the characteristics of the application made is done by processing data in the form of a collection of characters that have different types of characters, variants, and the number of characters. This research presents algorithm that support the steps of making adaptive Shannon-fano compression applications. The length of the character determines the variant value, compression ratio, and the number of input character types. Based on the results of test results, no error occurs according to the comparison of the original text input and the decompression results. A higher appearance frequency of a character causes a greater compression ratio of the resulting file; the analysis shows that a higher number of types of input characters causes a lower compression ratio, which proves that the proposed method in real-time data compression improves the effectiveness and efficiency of the compression process

A Compact Representation of Measured BRDFs Using Neural Processes

2022 ◽  
Vol 41 (2) ◽  
pp. 1-15
Author(s):  
Chuankun Zheng ◽  
Ruzhang Zheng ◽  
Rui Wang ◽  
Shuang Zhao ◽  
Hujun Bao
Keyword(s):  
Neural Networks ◽  
Compression Ratio ◽  
Continuous Functions ◽  
High Dimensional ◽  
Compact Representation ◽  
Practical Usefulness ◽  
Latent Space ◽  
Neural Processes ◽  
Low Dimensional ◽  
Better Than

In this article, we introduce a compact representation for measured BRDFs by leveraging Neural Processes (NPs). Unlike prior methods that express those BRDFs as discrete high-dimensional matrices or tensors, our technique considers measured BRDFs as continuous functions and works in corresponding function spaces . Specifically, provided the evaluations of a set of BRDFs, such as ones in MERL and EPFL datasets, our method learns a low-dimensional latent space as well as a few neural networks to encode and decode these measured BRDFs or new BRDFs into and from this space in a non-linear fashion. Leveraging this latent space and the flexibility offered by the NPs formulation, our encoded BRDFs are highly compact and offer a level of accuracy better than prior methods. We demonstrate the practical usefulness of our approach via two important applications, BRDF compression and editing. Additionally, we design two alternative post-trained decoders to, respectively, achieve better compression ratio for individual BRDFs and enable importance sampling of BRDFs.

Design and Implementation of Low Energy Wireless Network Nodes based on Hardware Compression Acceleration

2019 ◽  
Vol 12 ◽  
Author(s):  
Hui Yang ◽  
Anand Nayyar
Keyword(s):  
Energy Consumption ◽  
Data Compression ◽  
Energy Saving ◽  
Optimization Design ◽  
Hardware Acceleration ◽  
Transmission Efficiency ◽  
General Purpose ◽  
Storage Space ◽  
General Purpose Processor ◽  
Compression Time

: In the fast development of information, the information data is increasing in geometric multiples, and the speed of information transmission and storage space are required to be higher. In order to reduce the use of storage space and further improve the transmission efficiency of data, data need to be compressed. processing. In the process of data compression, it is very important to ensure the lossless nature of data, and lossless data compression algorithms appear. The gradual optimization design of the algorithm can often achieve the energy-saving optimization of data compression. Similarly, The effect of energy saving can also be obtained by improving the hardware structure of node. In this paper, a new structure is designed for sensor node, which adopts hardware acceleration, and the data compression module is separated from the node microprocessor.On the basis of the ASIC design of the algorithm, by introducing hardware acceleration, the energy consumption of the compressed data was successfully reduced, and the proportion of energy consumption and compression time saved by the general-purpose processor was as high as 98.4 % and 95.8 %, respectively. It greatly reduces the compression time and energy consumption.

scholarly journals Green–Haar wavelets method for generalized fractional differential equations

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Mujeeb ur Rehman ◽  
Dumitru Baleanu ◽  
Jehad Alzabut ◽  
Muhammad Ismail ◽  
Umer Saeed
Keyword(s):  
Differential Equations ◽  
Fractional Differential Equations ◽  
Haar Wavelet ◽  
Haar Wavelets ◽  
Computational Time ◽  
Operational Matrices ◽  
Numerical Techniques ◽  
Fractional Differential ◽  
Fractional Boundary Value Problems ◽  
Better Than

Abstract The objective of this paper is to present two numerical techniques for solving generalized fractional differential equations. We develop Haar wavelets operational matrices to approximate the solution of generalized Caputo–Katugampola fractional differential equations. Moreover, we introduce Green–Haar approach for a family of generalized fractional boundary value problems and compare the method with the classical Haar wavelets technique. In the context of error analysis, an upper bound for error is established to show the convergence of the method. Results of numerical experiments have been documented in a tabular and graphical format to elaborate the accuracy and efficiency of addressed methods. Further, we conclude that accuracy-wise Green–Haar approach is better than the conventional Haar wavelets approach as it takes less computational time compared to the Haar wavelet method.

scholarly journals CGCI-SIFT: A More Efficient and Compact Representation of Local Descriptor

2013 ◽  
Vol 13 (3) ◽  
pp. 132-141 ◽  
Author(s):  
Dongliang Su ◽  
Jian Wu ◽  
Zhiming Cui ◽  
Victor S. Sheng ◽  
Shengrong Gong
Keyword(s):  
Peripheral Region ◽  
Intensity Difference ◽  
Compact Representation ◽  
Interest Point ◽  
Local Descriptor ◽  
Geometric Transformations ◽  
Intensity Information ◽  
Matching Efficiency ◽  
Inner Region ◽  
Better Than

This paper proposes a novel invariant local descriptor, a combination of gradient histograms with contrast intensity (CGCI), for image matching and object recognition. Considering the different contributions of sub-regions inside a local interest region to an interest point, we divide the local interest region around the interest point into two main sub-regions: an inner region and a peripheral region. Then we describe the divided regions with gradient histogram information for the inner region and contrast intensity information for the peripheral region respectively. The contrast intensity information is defined as intensity difference between an interest point and other pixels in the local region. Our experimental results demonstrate that the proposed descriptor performs better than SIFT and its variants PCA-SIFT and SURF with various optical and geometric transformations. It also has better matching efficiency than SIFT and its variants PCA-SIFT and SURF, and has the potential to be used in a variety of realtime applications.

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