Lossless Data Compression Technique Research Articles

Energy-efficient algorithms for lossless data compression schemes in wireless sensor networks

Wireless sensor networks (WSNs) are reliant on limited power resources, primarily provided by small batteries within sensor nodes. Inefficient energy management within these networks can lead to premature battery depletion during data transmission between sensor nodes, significantly impacting network longevity. Data compression emerges as a viable strategy to mitigate energy consumption by reducing data size before transmission and employing various compression and decompression techniques. This work presents a comparative analysis of data compression algorithms tailored for WSNs. It studies and enhances two adaptive lossless data compression techniques, namely ‘Adaptive Lossless Data Compression’ (ALDC) and ‘Fast and Efficient, Lossless Adaptive Compression System’ (FELACS), as means to effectively manage energy consumption in wireless sensor networks. ALDC and FELACS algorithms encode differences between consecutive data readings, thereby reducing the number of bits required for encoding. ALDC employs Huffman coding, while FELACS leverages the Golomb-Rice coding method. Encoding data samples by using three Huffman tables interchangeably as an enhancement of the ALDC algorithm, resulted in an improvement in energy saving from 73 % to 77 %. Analysis of FELACS unveiled the impact of natural phenomena-induced anomalies on measured data, identified as outliers. The outliers disrupt data patterns and ranges, subsequently altering the optimal coding parameters for data samples, resulting in encoding and decoding errors. This study proposes a robust method for identifying and replacing outliers within sensor data, significantly enhancing compression performance. A reduction of variations in dataset patterns facilitated more accurate sampling and encoding of data. Consequently, fewer bits are required to encode data samples, rendering the algorithm energy-efficient and suitable for applications demanding error-free data recovery or meticulous error analysis. The proposed method was successfully applied to the modified ALDC algorithm, exhibiting efficient performance. An optimum block size of sampled data was discovered for Fishnet relative humidity deployment ensuring efficient transmission of environmental data real-world sensor network deployments like Fishnet, Lucerne, and Le Genepi. These findings underscore the potential for significant energy savings and improved data accuracy through adaptive lossless data compression techniques, making them valuable assets for applications with stringent energy constraints or demanding data integrity.

Data Compression-Aware Performance Analysis of Dask and Spark for Earth Observation Data Processing

High-performance computing is a good choice for handling Big Earth Observation data, allowing the processing of the data in a distributed and performance-efficient way using in-memory computing frameworks. The data compression technique reduces the amount of storage and network transfer time and improves processing performance. The article aims to investigate the effectiveness of widely used distributed data processing frameworks in conjunction with lossless data compression techniques, to find the optimal compression method and processing framework for specific earth observation workflows. Normalized Difference Vegetation Index has been evaluated for the territory of Armenia, obtaining data from the Sentinel satellite and considering the supported compression methods to compare the performance of in-memory Dask and Spark frameworks. Experiments show that the Zstandard compression method and the Dask framework are the best choices for such workflows.

A VLSI-Based Hybrid ECG Compression Scheme for Wearable Sensor Node

During smart long-term monitoring of any biomedical signal in wireless body area networks, wearable sensor nodes generate and transmit a large amount of data, increasing transmission power consumption. In order to reduce data storage and power consumption, a lossless data compression technique for an electrocardiogram signal monitoring system is presented in this letter. For this, a hybrid lossless compression algorithm based on Run-length coding and Golomb&#x2013;Rice coding is proposed to enhance the bit compressing rate. The lossless encoding scheme is implemented on the MIT-BIH arrhythmia database, achieving a compression ratio of 2.91. A VLSI-based architecture of the data compression algorithm is implemented in 90nm CMOS technology that consumes power of 18.78 <inline-formula><tex-math notation="LaTeX">$\mu \text{W}$</tex-math></inline-formula> at 100 MHz operating frequency and 1.2 V supply voltage, occupying an area of 0.0051 <inline-formula><tex-math notation="LaTeX">$\text{mm}^{2}$</tex-math></inline-formula>.

LZRR: LZ77 parsing with right reference

Lossless data compression has been widely studied in computer science. One of the most widely used lossless data compressions is Lempel-Ziv (LZ) 77 parsing, which achieves a high compression ratio. Bidirectional (a.k.a. macro) parsing is a lossless data compression technique that computes a sequence of phrases copied from another substring (target phrase) on either the left or the right position in an input string. Navarro et al. recently showed that a large gap may exist between the size of the smallest bidirectional parse of a given string and that of LZ77 parsing. In addition, finding the smallest bidirectional parse of a given text is NP-complete. Several variants of bidirectional parsing algorithm have been proposed thus far, but no prior work for bidirectional parsing algorithm has achieved compression that is at least as good as LZ77 parsing for any string. In this paper, we present the first practical bidirectional parsing algorithm named LZ77 parsing with right reference (LZRR), in which the number of LZRR phrases is theoretically guaranteed to be not larger than the number of LZ77 phrases. Experimental results using benchmark strings show that the number of LZRR phrases is approximately six percent smaller than that of LZ77 phrases.

Design of Effective Lossless Data Compression Technique for Multiple Genomic DNA Sequences

Design of Effective Lossless Data Compression Technique for Multiple Genomic DNA Sequences

Implementation of VLSI Based Efficient Lossless EEG Compression Architecture using Verilog HDL

If In reality, electroencephalography signal data transfer through the Wireless Network is a frequently used device related to stability and performance problems. This study, the lossless EEG compression circuit architecture of an efficient VLSI device is suggested to increase both the capability of EEG signal transmission and reliability over WBAN. A new lossless data compression technique consisting of even a proposed neural prediction, a casting a vote solution and a quadra probability compression codec has been applied to the proposed architecture. The mid equilibrium encoder consists of a static coding table of multiple Binary and Areas such as training encoders using the comparator and multiplexer’s fundamental components. Help boost the efficiency of the proposed system; a pipelining method was applied. Produce the suggested specification, a 0.18 m CMOS technology containing 8215 gates under 100 MHz clock speed under an operating environment. To test the usefulness of the suggested compression rate technique, for 25 channels, this results in a mean value of 2.35. This work accomplished a 9.6 %increase in compression rate and a 24.1 % decrease in processing costs relative to new competition equipment lossless EEG compressed designs, although keeping a constant interface simplicity.

A Review on Different Types of Lossless Data Compression Techniques

This paper provides different kinds of techniques for lossless data compression and comparison between them. By eliminating redundant bits, data compression decreases the file size. In order to reduce the capacity needed for that data, it decreases the redundant bits in data representation and thus uses the bandwidth effectively to reduce the communication cost. Compression of data saves file volume, network bandwidth and speeds up the transfer speed as well. Lossless and Lossy are the two techniques for data compression. Lossless compression maintains the data properly.

Design of Effective Lossless Data Compression Technique for Multiple Genomic DNA Sequences

In recent years, a massive amount of genomic DNA sequences are being created which leads to the development of new storing and archiving methods. There is a major challenge to process, store or transmit the huge volume of DNA sequences data. To lessen the number of bits needed to store and transmit data, data compression (DC) techniques are proposed. Recently, DC becomes more popular, and large number of techniques is proposed with applications in several domains. In this paper, a lossless compression technique named Arithmetic coding is employed to compress DNA sequences. In order to validate the performance of the proposed model, the artificial genome dataset is used and the results are investigated interms of different evaluation parameters. Experiments were performed on artificial datasets and the compression performance of Arithmetic coding is compared to Huffman coding, LZW coding, and LZMA techniques. From simulation results, it is clear that the Arithmetic coding achieves significantly better compression with a compression ratio of 0.261 at the bit rate of 2.16 bpc.

SPADE: Activity Prediction in Smart Homes Using Prefix Tree Based Context Generation

An automated smart home system is a key contributor to user assistance technology in modern civilization. Crucial merit of such a system is its ability to train itself through recorded data and recognize patterns in resident behaviors. Lack of sufficient prediction accuracy, exponential memory consumption, and extensive runtime prevent many of the current activity prediction approaches from being seamlessly integrated into consumer residences. This research introduces a sequence prediction algorithm which uses a prefix tree-based data model in order to learn and predict user actions. The algorithm applies episode discovery to detect correlated sensor events and learns the activities using a lossless data compression technique. This process assigns a probability of occurrence to sensor events and uses these probabilities to detect patterns in resident behavior. A complexity analysis of the algorithm is done to prove its efficiency in terms of memory usage and runtime. Using the presented technique, predictions are performed on popular datasets and contrasted with existing algorithms. The proposed algorithm achieves an 8.22% improvement in prediction accuracy over its predecessors, along with 66.69% better memory efficiency and 37% faster runtime.

Online data compression in the ALICE O2 facility

The ALICE Collaboration and the ALICE O2 project have carried out detailed studies for a new online computing facility planned to be deployed for Run 3 of the Large Hadron Collider (LHC) at CERN. Some of the main aspects of the data handling concept are partial reconstruction of raw data organized in so called time frames, and based on that information reduction of the data rate without significant loss in the physics information.A production solution for data compression has been in operation for the ALICE Time Projection Chamber (TPC) in the ALICE High Level Trigger online system since 2011. The solution is based on reconstruction of space points from raw data. These so called clusters are the input for reconstruction of particle trajectories. Clusters are stored instead of raw data after a transformation of required parameters into an optimized format and subsequent lossless data compression techniques. With this approach, a reduction of 4.4 has been achieved on average.For Run 3, not only a significantly higher reduction is required but also improvements in the implementation of the actual algorithms. The innermost operations of the processing loop effectively need to be called up to O(1011)/s to cope with the data rate. This can only be achieved in a parallel scheme and makes these operations candidates for optimization. The potential of template programming and static dispatch in a polymorphic implementation has been studied as an alternative to the commonly used dynamic dispatch at runtime.In this contribution we report on the development of a specific programming technique to efficiently combine compile time and runtime domains and present results for the speedup of the algorithm.

On Generalizations and Improvements to the Shannon-Fano Code

This paper examines the possibility of generalizing the Shannon-Fano code for cases where the output alphabet has more then 2 (n) symbols. This generalization is well-known for the famous Huffman code. Furthermore, we will be looking at possible improvements to the algorithm, as well as other entropy based lossless data compression techniques, based on the same ideas as the Shannon-Fano code. All algorithms discussed in the paper were implemented by us in C++, and we will be illustrating our hypotheses with test results, made on an average performance PC.

Modification of Adaptive Huffman Coding for use in encoding large alphabets

The paper presents the modification of Adaptive Huffman Coding method – lossless data compression technique used in data transmission. The modification was related to the process of adding a new character to the coding tree, namely, the author proposes to introduce two special nodes instead of single NYT (not yet transmitted) node as in the classic method. One of the nodes is responsible for indicating the place in the tree a new node is attached to. The other node is used for sending the signal indicating the appearance of a character which is not presented in the tree. The modified method was compared with existing methods of coding in terms of overall data compression ratio and performance. The proposed method may be used for large alphabets i.e. for encoding the whole words instead of separate characters, when new elements are added to the tree comparatively frequently.

Efficiency of adaptive fractal image compression with archetype classification and its modifications

The DCT-based JPEG image compression technique is a standard for lossy image compression. But the technique is not suitable at high compression ratios. Again, it is resolution dependent. Alternative compression techniques are introduced to reduce these problems. One such popular technique is fractal-based image compression. In this paper, efficiency of a fractal-based image compression technique is discussed that applies adaptive quadtree partitioning and archetype classification schemes. The adaptive quadtree partitioning scheme is dependent on image pixel context to share some self-similar structure of the image and improves the decoded image quality significantly. The archetype classification scheme tries to base the technique on finding better covering of range by domain that suites better in fractal image compression techniques. Then, modification of the same is also done to enhance the performance by applying dictionary-based loss-less data compression techniques, i.e. OLZW on the fractal compressed image that is basically a collection of affine transforms. The affine transform consists of a number of parameters that are compressed using OLZW technique. After that, the same experiment is carried out by a variant of OLZW, i.e. MOLZW. The experimental results are compared among existing and modified techniques. The modified versions yield not only better image quality but also high encoding speed than their counterparts.

USE OF NEW EFFICIENT LOSSLESS DATA COMPRESSION METHOD IN TRANSMITTING ENCRYPTED BAPTISTA SYMMETRIC CHAOTIC CRYPTOSYSTEM DATA

A new compression algorithm used to ensure a modified Baptista symmetric cryptosystem which is based on a chaotic dynamical system to be applicable is proposed. The Baptista symmetric cryptosystem able to produce various ciphers responding to the same message input. This modified Baptista type cryptosystem suffers from message expansion that goes against the conventional methodology of a symmetric cryptosystem. A new lossless data compression algorithm based on theideas from the Huffman coding for data transmission is proposed.This new compression mechanism does not face the problem of mapping elements from a domain which is much larger than its range.Our new algorithm circumvent this problem via a pre-defined codeword list. The purposed algorithm has fast encoding and decoding mechanism and proven analytically to be a lossless data compression technique.

An Efficient Compression Algorithm for Forthcoming New Species

Genomic repositories gradually increase individual and reference sequences, which shares long identical and near-identical strings of nucleotides. In this paper a lossless DNA data compression technique called Optimized Base Repeat Length DNA Compression (OBRLDNAComp) has been proposed, based upon redundancy of DNA sequences. For easy storage, retrieval time reducing and to find similarity within and between sequences compression is mandatory. OBRLDNAComp searches long identical and near-identical strings of nucleotides which are overlooked by other DNA specific compression algorithms. This technique is an optimal solution of longest possible exact repeat benefits towards compression ratio. It scans a sequence horizontally from left to right to find statistic of repeats then follow substitution technique to compress those repeats. The algorithm is straightforward and does not need any external reference file; it scans the individual file for compression and decompression. The achieved compression ratio 1.673 bpb outperforms many non-reference based compression methods.

Adaptive block-wise alphabet reduction scheme for lossless compression of images with sparse and locally sparse histograms

We propose a new adaptive block-wise lossless image compression algorithm, which is based on the so-called alphabet reduction scheme combined with an adaptive arithmetic coding (AC). This new encoding algorithm is particularly efficient for lossless compression of images with sparse and locally sparse histograms. AC is a very efficient technique for lossless data compression and produces a rate that is close to the entropy; however, a compression performance loss occurs when encoding images or blocks with a limited number of active symbols by comparison with the number of symbols in the nominal alphabet, which consists in the amplification of the zero frequency problem. Generally, most methods add one to the frequency count of each symbol from the nominal alphabet, which leads to a statistical model distortion, and therefore reduces the efficiency of the AC. The aim of this work is to overcome this drawback by assigning to each image block the smallest possible set including all the existing symbols called active symbols. This is an alternative of using the nominal alphabet when applying the conventional arithmetic encoders. We show experimentally that the proposed method outperforms several lossless image compression encoders and standards including the conventional arithmetic encoders, JPEG2000, and JPEG-LS.

The pseudo-distance technique for parallel lossless compression of color-mapped images

Data compression is a challenging process with important practical applications. Specialized techniques for lossy and lossless data compression have been the subject of numerous investigations during last several decades. Previously, we studied the use of the pseudo-distance technique (PDT) in lossless compression of color-mapped images and its parallel implementation. In this paper we present a new technique (PDT2) to improve compression gain of PDT. We also present a parallelized implementation of the new technique, which results in substantial gains in compression time while providing the desired compression efficiency. We demonstrate that on non-dithered images PDT2 outperforms PDT by 22.4% and PNG by 29.3%. On dithered images, PDT2 achieves compression gains of 7.1% over PDT and 23.8% over PNG. We also show that the parallel implementation of PDT2, while compromising compression less than 0.3%, achieves near linear speedup and utilization of Intel Hyper-Threading technology on supported systems improves speedup on average 18%.

Compressive Sensing Algorithm for Wireless Sensor Network Power Management

This paper presents lossless compression algorithm to minimize the power consumption of the wireless sensor network (WSN). The sensor and receiver node consumes more power when transmission of data is taking place. In addition, huge data are processed in the network which also consumes energy. The main contribution of this paper is to prolong the battery life of the sensors by using compressive sensing to lessen the transmission activity and data processing. CS is a lossless data compression technique that is used to represent the signals into sparse, linear encoding and transformation matrix, and sparse recovery. Discrete Fourier transform is used to get the nonzero coefficient of the signals. L1 minimization is used to recover the signal into its original form.

Comparative data compression techniques and multi-compression results

Data compression is very necessary in business data processing, because of the cost savings that it offers and the large volume of data manipulated in many business applications. It is a method or system for transmitting a digital image (i.e., an array of pixels) from a digital data source to a digital data receiver. More the size of the data be smaller, it provides better transmission speed and saves time. In this communication, we always want to transmit data efficiently and noise freely. This paper will provide some compression techniques for lossless text type data compression and comparative result of multiple and single compression, that will help to find out better compression output and to develop compression algorithms.

Simulation and Comparison of Various Lossless Data Compression Techniques based on Compression Ratio and Processing Delay

increasing need to store data in lesser memory several lossless compression techniques are developed. This paper intends to provide the performance analysis of lossless compression techniques over various parameters like compression ratio, delay in processing, size of image etc. It provides the relevant data about variations in them as well as to describe the possible causes for it. It describes the basic lossless techniques as Huffman encoding, run length encoding, arithmetic encoding and Lempel-ziv-welch encoding briefly with their effectiveness under varying parameters. Considering the simulation results of grayscale image compression achieved in MATLAB software, it also focused to propose the possible reasons behind differences in comparison.