Lempel-Ziv-Welch Research Articles

Compression algorithm of road traffic data in time series based on temporal correlation

The numerous applications of urban traffic detection technology in road traffic data acquisition bring new challenges for transportation and storage of road traffic big data. The travel demand and travel time of travel participants present certain specific regularity; thus, a compression algorithm for road traffic data in time series based on temporal correlation was proposed in this study. First, the temporal correlation of the road traffic data in time series was analysed. Second, the reference sequences of road traffic characteristics were constructed to acquire the base data under different modes. Third, the training data under the same mode were extracted to acquire the difference data between training and base data. Then the optimal threshold of the difference data was trained. Fourth, the optimal threshold was introduced into the difference data between real-time and base data in time series, combining with Lempel-Ziv-Welch (LZW) encoding to achieve the compression of difference data. Finally, the reconstruction of real-time road traffic data in time series was accomplished based on LZW decoding technology. Six typical road segments in Beijing were adopted for case studies. The final results prove the feasibility of the algorithm, and that the reconstructed data can achieve high accuracy.

A Codec Architecture for the Compression of Short Data Blocks

This work proposes a lossless data compression algorithm for short data blocks. The proposed compression scheme combines a modified move-to-front algorithm with Huffman coding. This algorithm is applicable in storage systems where the data compression is performed on block level with short block sizes, in particular, in non-volatile memories. For block sizes in the range of 1[Formula: see text]kB, it provides a compression gain comparable to the Lempel–Ziv–Welch algorithm. Moreover, encoder and decoder architectures are proposed that have low memory requirements and provide fast data encoding and decoding.

Improved Lempel-Ziv-Welch’s Error Detection and Correction Scheme using Redundant Residue Number System (RRNS)

The greatest difficulty of compressing data is the assurance of the security, integrity, and accuracy of the data in storage in volatile media or transmission in network communication channels. Various methods have been proposed for dealing with the accuracy and consistency of compressed and encrypted data using error detection and correction mechanisms. The Redundant Residue Number System (RRNS) which is a trait of Residue Number System (RNS) is one of the available methods for detecting and correcting errors which involves the addition of extra moduli called redundant moduli. In this paper, Residue Number System (RNS) is efficiently applied to the Lempel-Ziv-Welch (LZW) compression algorithm resulting in new LZW-RNS compression scheme using the traditional moduli set, and two redundant moduli added resulting in the moduli set {2^n-1,〖 2〗^n,〖 2〗^n+1,〖 2〗^2n-3,〖 2〗^2n+1} for the purposes of error detection and correction. This is done by constraining the data or information within the legitimate range of the dynamic range provided by the non-redundant moduli. Simulation with MatLab shows the efficiency and fault tolerance of the proposed scheme than the traditional LZW compression method and other related known state of the art schemes.

Block Construct BCH LZW Algorithm for Medical Image Compression

Block Construct BCH LZW Algorithm for Medical Image Compression

Efficient Real-Time Lossless EMG Data Transmission to Monitor Pre-Term Delivery in a Medical Information System

An estimated 15 million babies are born prematurely every year worldwide, and suffer from disabilities. Appropriate care of these pre-term babies immediately after birth through telemedicine monitoring is vital. However, problems associated with a limited bandwidth and network overload due to the excessive size of the electromyography (EMG) signal impede the practical application of such medical information systems. Therefore, this research proposes an EMG uterine monitoring transmission solution (EUMTS), a lossless efficient real-time EMG transmission solution that solves such problems through efficient EMG data lossless compression. EMG data samples obtained from the Physionet PhysioBank database were used. Solution performance comparisons were conducted using Lempel-Ziv Welch (LZW) and Huffman methods, in addition to related researches. The LZW and Huffman methods showed CRs of 1.87 and 1.90, respectively, compared to 3.61 for the proposed algorithm. This was relatively high compared to related researches, even when considering that those researches were lossy whereas the proposed research was lossless. The results also showed that the proposed algorithm contributes to a reduction in battery consumption by reducing the wake-up time by 1470.6 ms. Therefore, EUMTS will contribute to providing an efficient wireless transmission environment for the prediction of pre-term delivery, enabling immediate interventions by medical professionals. Another novel point of EUMTS is that it is a lossless algorithm, which will prevent any misjudgement by clinicians because the data will not be distorted. Pre-term babies may receive point-of-care immediately after birth, preventing exposure to the development of disabilities.

Empirical and Statistical Evaluation of the Effectiveness of Four Lossless Data Compression Algorithms

Data compression is the process of reducing the size of a file to effectively reduce storage space and communication cost. The evolvement in technology and digital age has led to an unparalleled usage of digital files in this current decade. The usage of data has resulted to an increase in the amount of data being transmitted via various channels of data communication which has prompted the need to look into the current lossless data compression algorithms to check for their level of effectiveness so as to maximally reduce the bandwidth requirement in communication and transfer of data. Four lossless data compression algorithm: Lempel-Ziv Welch algorithm, Shannon-Fano algorithm, Adaptive Huffman algorithm and Run-Length encoding have been selected for implementation. The choice of these algorithms was based on their similarities, particularly in application areas. Their level of efficiency and effectiveness were evaluated using some set of predefined performance evaluation metrics namely compression ratio, compression factor, compression time, saving percentage, entropy and code efficiency. The algorithms implementation was done in the NetBeans Integrated Development Environment using Java as the programming language. Through the statistical analysis performed using Boxplot and ANOVA and comparison made on the four algorithms, Lempel Ziv Welch algorithm was the most efficient and effective based on the metrics used for evaluation. Keywords: Data compression, lossless, evaluation, entropy, algorithm

Comparative study of lossy and lossless data compression in distributed optical fiber sensing systems

Typical fully distributed optical fiber sensors (DOFS) with dozens of kilometers are equivalent to tens of thousands of point sensors along the whole monitoring line, which means tens of thousands of data will be generated for one pulse launching period. Therefore, in an all-day nonstop monitoring, large volumes of data are created thereby triggering the demand for large storage space and high speed for data transmission. In addition, when the monitoring length and channel numbers increase, the data also increase extensively. The task of mitigating large volumes of data accumulation, large storage capacity, and high-speed data transmission is, therefore, the aim of this paper. To demonstrate our idea, we carried out a comparative study of two lossless methods, Huffman and Lempel Ziv Welch (LZW), with a lossy data compression algorithm, fast wavelet transform (FWT) based on three distinctive DOFS sensing data, such as Φ-OTDR, P-OTDR, and B-OTDA. Our results demonstrated that FWT yielded the best compression ratio with good consumption time, irrespective of errors in signal construction of the three DOFS data. Our outcomes indicate the promising potentials of FWT which makes it more suitable, reliable, and convenient for real-time compression of the DOFS data. Finally, it was observed that differences in the DOFS data structure have some influence on both the compression ratio and computational cost.

A high capacity text steganography scheme based on LZW compression and color coding

In this paper, capacity and security issues of text steganography have been considered by employing LZW compression technique and color coding based approach. The proposed technique uses the forward mail platform to hide the secret data. This algorithm first compresses secret data and then hides the compressed secret data into the email addresses and also in the cover message of the email. The secret data bits are embedded in the message (or cover text) by making it colored using a color coding table. Experimental results show that the proposed method not only produces a high embedding capacity but also reduces computational complexity. Moreover, the security of the proposed method is significantly improved by employing stego keys. The superiority of the proposed method has been experimentally verified by comparing with recently developed existing techniques.

COMPARISON OF OPEN SOURCE COMPRESSION ALGORITHMS ON VHR REMOTE SENSING IMAGES FOR EFFICIENT STORAGE HIERARCHY

Abstract. High resolution level in satellite imagery came with its fundamental problem as big amount of telemetry data which is to be stored after the downlink operation. Moreover, later the post-processing and image enhancement steps after the image is acquired, the file sizes increase even more and then it gets a lot harder to store and consume much more time to transmit the data from one source to another; hence, it should be taken into account that to save even more space with file compression of the raw and various levels of processed data is a necessity for archiving stations to save more space. Lossless data compression algorithms that will be examined in this study aim to provide compression without any loss of data holding spectral information. Within this objective, well-known open source programs supporting related compression algorithms have been implemented on processed GeoTIFF images of Airbus Defence & Spaces SPOT 6 & 7 satellites having 1.5 m. of GSD, which were acquired and stored by ITU Center for Satellite Communications and Remote Sensing (ITU CSCRS), with the algorithms Lempel-Ziv-Welch (LZW), Lempel-Ziv-Markov chain Algorithm (LZMA & LZMA2), Lempel-Ziv-Oberhumer (LZO), Deflate & Deflate 64, Prediction by Partial Matching (PPMd or PPM2), Burrows-Wheeler Transform (BWT) in order to observe compression performances of these algorithms over sample datasets in terms of how much of the image data can be compressed by ensuring lossless compression.

A Real-Time Audit Mechanism Based on the Compression Technique

Log management and log auditing have become increasingly crucial for enterprises in this era of information and technology explosion. The log analysis technique is useful for discovering possible problems in business processes and preventing illegal-intrusion attempts and data-tampering attacks. Because of the complexity of the dynamically changing environment, auditing a tremendous number of data is a challenging issue. We provide a real-time audit mechanism to improve the aforementioned problems in log auditing. This mechanism was developed based on the Lempel-Ziv-Welch (LZW) compression technique to facilitate effective compression and provide reliable auditing log entries. The mechanism can be used to predict unusual activities when compressing the log data according to pre-defined auditing rules. Auditors using real-time and continuous monitoring can perceive instantly the most likely anomalies or exceptions that could cause problems. We also designed a user interface that allows auditors to define the various compression and audit parameters, using real log cases in the experiment to verify the feasibility and effectiveness of this proposed audit mechanism. In summary, this mechanism changes the log access method and improves the efficiency of log analysis. This mechanism greatly simplifies auditing so that auditors must only trace the sources and causes of the problems related to the detected anomalies. This greatly reduces the processing time of analytical audit procedures and the manual checking time, and improves the log audit efficiency.

Watermarking of ultrasound medical images in teleradiology using compressed watermark

The open accessibility of Internet-based medical images in teleradialogy face security threats due to the nonsecured communication media. This paper discusses the spatial domain watermarking of ultrasound medical images for content authentication, tamper detection, and lossless recovery. For this purpose, the image is divided into two main parts, the region of interest (ROI) and region of noninterest (RONI). The defined ROI and its hash value are combined as watermark, lossless compressed, and embedded into the RONI part of images at pixel's least significant bits (LSBs). The watermark lossless compression and embedding at pixel's LSBs preserve image diagnostic and perceptual qualities. Different lossless compression techniques including Lempel-Ziv-Welch (LZW) were tested for watermark compression. The performances of these techniques were compared based on more bit reduction and compression ratio. LZW was found better than others and used in tamper detection and recovery watermarking of medical images (TDARWMI) scheme development to be used for ROI authentication, tamper detection, localization, and lossless recovery. TDARWMI performance was compared and found to be better than other watermarking schemes.

ANALISIS PERBANDINGAN ALGORITMA LZW DAN HUFFMAN PADA KOMPRESI FILE GAMBAR BMP DAN PNG

File compression is one of important aspect in the development of information technology. The demands of providing information in a short time with large number and size of files makes compression techniques very important. There are a lot of algorithms that have been developed for the compression of files, including: LZW algorithm and Huffman Algorithm. This study discusses the comparative analysis between the two algorithms are against compressed image file format BMP and PNG. Analysis of this study aims to provide knowledge about what is the best algorithm between both of the algorithm to compress the image file format BMP and PNG. This analysis is comparing the ratio and the estimated time of each algorithm for image file format BMP and PNG. The author uses the Java programming language to implement both LZW and Huffman algorithm, and then analyze the process and results of compression on the file format BMP and PNG. After testing and analysis of experiments the compression process, it is concluded that the compression process BMP and PNG file format, Huffman algorithm can compress files faster than LZW algorithm, but the LZW algorithm can produce better compression ratio than Huffman algorithm.

Watermark Compression in Medical Image Watermarking Using Lempel-Ziv-Welch (LZW) Lossless Compression Technique.

In teleradiology, image contents may be altered due to noisy communication channels and hacker manipulation. Medical image data is very sensitive and cannot tolerate any illegal change. Illegally changed image-based analysis could result in wrong medical decision. Digital watermarking technique can be used to authenticate images and detect as well as recover illegal changes made to teleradiology images. Watermarking of medical images with heavy payload watermarks causes image perceptual degradation. The image perceptual degradation directly affects medical diagnosis. To maintain the image perceptual and diagnostic qualities standard during watermarking, the watermark should be lossless compressed. This paper focuses on watermarking of ultrasound medical images with Lempel-Ziv-Welch (LZW) lossless-compressed watermarks. The watermark lossless compression reduces watermark payload without data loss. In this research work, watermark is the combination of defined region of interest (ROI) and image watermarking secret key. The performance of the LZW compression technique was compared with other conventional compression methods based on compression ratio. LZW was found better and used for watermark lossless compression in ultrasound medical images watermarking. Tabulated results show the watermark bits reduction, image watermarking with effective tamper detection and lossless recovery.

An Image Steganography Algorithm for Hiding Data Based on HDWT, LZW and OPAP

Image steganography is the art of information hiding, which embeds a secret data into a cover image. However, high capacity of secret data and high quality of stego image are key issues in image steganography. In this paper, an image steganography technique based on Haar Discrete Wavelet Transform (HDWT), Lempel Ziv Welch (LZW) algorithm and Optimal Pixel Adjustment Process (OPAP) is proposed. The HDWT is used to increase the robustness of stego image against attacks. To increase the hidden capacity, LZW algorithm is performed on the secret message. The OPAP is then applied to reduce the embedding error between the cover image and stego image. The experimental results are evaluated by four standard images as covers, and with two types of secret messages. The results demonstrate high visual quality of stego image with large Hidden Capacity (HC) of secret data compared with recent techniques

Energy Efficient Hierarchical ClusteringBased Routing Protocol with TDMA forWireless Sensor Networks

A Wireless Sensor Network (WSN) is built from a network of devices referred to as nodes, which can sense the environment and are used to communicate the required information collected from monitored fields. The communication and collection of information is done with the help of wireless links, where each node is linked to one or a number of sensors. The nodes in turn are deployed in large numbers depending upon the application where it is used. In modern days the process is bidirectional where the sensor activity can be monitored. The longevity in network lifetime is necessary as they have limited battery power which is to be used efficiently. This has led to new horizons upon energy saving and reduced power consumption among sensor nodes. Hierarchical cluster-based routing protocols are considered as one of the most efficient routing protocols in wireless sensor networks (WSN) due to its higher energy efficiency, network scalability, and lower data retransmission. An energy efficient hierarchical routing protocol LEACH-EN based on LEACH-C is designed by introducing the concept of TDMA scheduling along with compression of data from each node using LZW compression technique. The proposed LEACH-EN seems to perform well as collision is avoided using TDMA and hence data can be transmitted faster.

Survey on Enhanced Compressed P-Coding for Energy Efficient Transmission in Mobile Ad Hoc Networks

of the important issue in MANETs is energy saving. Recent studies show that Network coding can reduces energy consumption by less transmission in MANETs. Encryption/decryption, transmission cost and transmission time are worked as source of energy consumption in MANETs. Network coding provides intrinsic security based on which encryption can be done quite efficiently, but to provide security for MANETs symmetric key algorithms are not sufficient. This paper introduce new scheme of energy saving called Enhanced Lightweight P-Coding (ELP) proposed for MANETs, with network coding which improves throughput, transparency, security and energy efficiency. The basic idea is, let the source compress the coded message (which is prefixed with its coded vector) using Enhanced LZW algorithm and hence due to compression and without knowing the permutation, eavesdropper cannot decode the message. Data compression technique can reduce transmission time that consumes less bandwidth and low power. Thus Enhanced lightweight P-Coding consist minimal energy consumption as compare to other encryption/decryption methods. Keywordsad hoc network, Energy saving, Lightweight Encryption/Decryption, Compression.

An Image Steganography Algorithm for Hiding Data Based on HDWT, LZW and OPAP

Image steganography is the art of information hiding, which embeds a secret data into a cover image. However, high capacity of secret data and high quality of stego image are key issues in image steganography. In this paper, an image steganography technique based on Haar Discrete Wavelet Transform (HDWT), Lempel Ziv Welch (LZW) algorithm and Optimal Pixel Adjustment Process (OPAP) is proposed. The HDWT is used to increase the robustness of stego image against attacks. To increase the hidden capacity, LZW algorithm is performed on the secret message. The OPAP is then applied to reduce the embedding error between the cover image and stego image. The experimental results are evaluated by four standard images as covers, and with two types of secret messages. The results demonstrate high visual quality of stego image with large Hidden Capacity (HC) of secret data compared with recent techniques

An Energy-Saving Routing Algorithm for Wireless Body Sensor Network using Data Compression Technique

A wireless sensor network (WSN) is used to monitor the environmental condition. There consist of N number of sensor nodes which are connected to each other and they are clustered in to different groups. There have a cluster head (CH) which is used to manage the cluster members (CM). when the sensor node is active, energy will be reduced vitally in order to increase the life time of the sensor node different application has be implemented .In this method Energy consumption will been reduced by implementing leach-c protocol and LZW compression technique so the lifetime of the sensor node is increased.

A Hardware Implementation of Real Time Lossless Data Compression and Decompression Circuits

This paper presents a hardware implementation of real time data compression and decompression circuits based on the LZW algorithm. LZW is a dictionary based data compression, which has the advantage of fast speed, high compression, and small resource occupation. In compression circuit, the design creatively utilizes two dictionaries alternately to improve efficiency and compressing rate. In decompression circuit, an integrated State machine control module is adopted to save hardware resource. Through hardware description and language programming, the circuits finally reach function simulation and timing simulation. The width of data sample is 12bits, and the dictionary storage capacity is 1K. The simulation results show the compression and decompression circuits have complete function. Compared to software method, hardware implementation can save more storage and compressing time. It has a high practical value in the future.

Secured Telemedicine Using Whole Image as Watermark with Tamper Localization and Recovery Capabilities

Region of interest (ROI) is the most informative part of a medical image and mostly has been used as a major part of watermark. Various shapes ROIs selection have been reported in region-based watermarking techniques. In region-based watermarking schemes an image region of non-interest (RONI) is the second important part of the image and is used mostly for watermark encapsulation. In online healthcare systems the ROI wrong selection by missing some important portions of the image to be part of ROI can create problem at the destination. This paper discusses the complete medical image availability in original at destination using the whole image as a watermark for authentication, tamper localization and lossless recovery (WITALLOR). The WITALLOR watermarking scheme ensures the complete image security without of ROI selection at the source point as compared to the other region-based watermarking techniques. The complete image is compressed using the Lempel-Ziv-Welch (LZW) lossless compression technique to get the watermark in reduced number of bits. Bits reduction occurs to a number that can be completely encapsulated into image. The watermark is randomly encapsulated at the least significant bits (LSBs) of the image without caring of the ROI and RONI to keep the image perceptual degradation negligible. After communication, the watermark is retrieved, decompressed and used for authentication of the whole image, tamper detection, localization and lossless recovery. WITALLOR scheme is capable of any number of tampers detection and recovery at any part of the image. The complete authentic image gives the opportunity to conduct an image based analysis of medical problem without restriction to a fixed ROI.