PENGEMBANGAN DAN ANALISIS KOMBINASI RUN LENGTH ENCODING DAN RELATIVE ENCODING UNTUK KOMPRESI CITRA

ObjectiveData compression is a useful process in tele-monitoring applications, in which lesser number of bits are needed to represent the same data. In this work, a run-time lossless compression of single-channel Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals is proposed, maintaining all dominant features. MethodsThe single-channel data are first quantized using optimal quantization level, so that fewer number of bits are needed to represent it, maintaining low quantization error. Then, second order delta encoding and run-length encoding (RLE) based data compression are proposed in this work. A new approach of using ‘buffer array’ along with RLE is also introduced, so that minimum bits are needed to store. ResultsThis algorithm was tested on various single-lead ECG and PPG signals available in Physionet. An average compression ratio (CR) was achieved of 6.52, 3.82, and 2.49 for 547 PTBDB ECG records, 48 MITDB ECG records, and 53 MIMIC-II PPG records, respectively. This algorithm was also performed on single-channel ECG, collected from 10 healthy volunteers using AD8232 ECG module, with 125 Hz sampling frequency and 10-bit data resolution, which resulted in average CR of 2.34. ConclusionThis algorithm was also performed on a smartphone device that provided user-friendly operation. The low computational complications and standalone operation of data collection, compression, and transmission encouraged its implementation for run-time operation. SignificanceA comparative study of the proposed work with previously published works proved this fact that this algorithm provided better performance in the area of run-time patient health monitoring applications.

Medical applications create an enormous amount of data. Medical data transmission via networks necessitates a huge bandwidth rate. Also, digital medical data necessitate enormous storage and archive. With the evolution of the Internet and multimedia designs, medical data is required to be transmitted in a rapid manner. One of the practical solutions to this issue is medical data compression. Data compression (DC) and transmission is important in the medical field. DC is used to transmit a large amount of data for minimizing the cost. DC is introduced to minimize the image for focusing on the removal of redundant data. DC is classified into two categories, namely, lossy and lossless techniques. DC is designed to reduce storage, bandwidth, and time consumed for transmission. Coding is utilized to remove unwanted data. The different DC algorithm is used to enhance the compression rate. Some of the medical data compression techniques are outlined to lessen data redundancy via specialized data coding and, as a result, can significantly minimize the constructive amount of medical data. In other words, medical data compression involves the procedure of encoding medical data in such a manner that less storage is essential to archive them over a network. The contemporary prototype of medical data compression is split into two stages, namely, designing and entropy coding. Selecting the appropriate prototype is paramount due to the reason that the more consistencies we identify, the more are the probabilities to minimize the series scope. Next, based on the understanding acquired via designing, unwanted data are eliminated by applying coding. Here, encoding is performed to eliminate dispensable data. As several DC techniques have been progressed, a requirement comes to light to assess the techniques, and an endeavor is made to review and classify different DC techniques based on three classifications, namely, coding schemes, data quality specifications, and application appropriateness. Some of the coding schemes for lossless data compression, to name a few, are run-length encoding, Huffman encoding, and LZW encoding. Also with the expeditious rise in high-speed data acquisition, bandwidth acquisition and storage have become the focal restrictions concerning DC techniques. We observed that it is impracticable to outline an exclusive lossless compression technique for different data types without a certain understanding of the series. It is also unfeasible to develop a disparate lossless compression algorithm for every potential series. The intelligent alternative is to devise comprehensive DC and to utilize such an algorithm to the series that can be handled, with a higher amount of precision. Some of the analyzed algorithms are component analysis, partial matching, state-space transitions, and tree sequence.

A prototype chip, called RD53A, has been designed by the RD53 collaboration to face the very high hit and trigger rate requirements (up to 3 GHz/cm2 and 1 MHz, respectively) of the High Luminosity LHC experiment upgrades. In this paper, an improved algorithm for data compression, capable of sustaining the very high data volume and proposed to be implemented in the periphery of the chip, is presented: it exploits Run Length Encoding (RLE) and Variable Length Coding (VLC) to compact chip pixel hit patterns. The compression and decompression algorithms are implemented with MATLAB, and the performance is calculated taking into account the RD53A data readout implementation and its chip simulation and verification framework (called VEPIX53). In all considered cases, the results show that the RLE and VLC combination achieves a data compression ratio between 1.57 and 1.62, resulting in a bitstream size reduction between 36.2% and 38.4% with respect to the rate of the current data transmission format.

Data compression is the process that is used to reduce the physical size of a block of information; data compression encodes information using fewer bits to help in reducing the consumption of expensive resources such as disk space or transmission bandwidth. The task of compression consist of two components, an encoding algorithm that takes a message and generates a compressed representation (hopefully with fewer bits), and a decoding algorithm that reconstructs the original message or some approximation of it from the compressed representation. Data Compression is divided into two (2) broad categories namely Lossless compression and lossy algorithms. This paper examined these compression techniques and provided a comparative analysis of three commonly used compression techniques (the Huffman, Lempel-Ziv and RunLength Encoding). The results revealed that compression algorithms can be proven to be more effective on notepad, text, web documents, PDF, Images and sound.

Lossless data compression algorithms can use statistical redundancy to represent data using a fewer number of bits in comparison to the original uncompressed data. Run-Length Encoding (RLE) is one of the simplest lossless compression algorithms in terms of understanding its principles and software implementation, as well as in terms of temporal and spatial complexity. If this principle is applied to individual bits of original uncompressed data without respecting the byte boundaries, this approach is referred to as bit-level Run-Length Encoding. Lightweight algorithm for lossless data compression proposed in this paper optimizes bit-level RLE data compression, uses special encoding of repeating data blocks, and, if necessary, combines it with delta data transformation or representation of data in its original form intending to increase compression efficiency compared to a conventional bit-level RLE approach. The advantage of the algorithm proposed in this paper is in its low time and memory consumption which are basic features of RLE, along with the simultaneous increase of compression ratio, compared to the classical bit-level RLE approach.

Data Compression may be defined as the science and art of the representation of information in a crisply condensed form. For decades, Data compression has been one of the critical enabling technologies for the ongoing digital multimedia revolution. There are a lot of data compression algorithms which are available to compress files of different formats. This paper provides a survey of different basic lossless data compression algorithms. Experimental results and comparisons of the lossless compression algorithms using Statistical compression techniques and Dictionary based compression techniques were performed on text data. Among the Statistical coding techniques, the algorithms such as Shannon-Fano Coding, Huffman coding, Adaptive Huffman coding, Run Length Encoding and Arithmetic coding are considered. Lempel Ziv scheme which is a dictionary based technique is divided into two families: one derived from LZ77 (LZ77, LZSS, LZH, LZB and LZR) and the other derived from LZ78 (LZ78, LZW, LZFG, LZC and LZT). A set of interesting conclusions are derived on this basis.

The Burrows-Wheeler Transformation (BWT) is a text transformation algorithm originally designed to improve the coherence in text data. This coherence can be exploited by compression algorithms such as run-length encoding or arithmetic coding. However, there is still a debate on its performance on images. Motivated by a theoretical analysis of the performance of BWT and MTF, we perform a detailed empirical study on the role of MTF in compressing images with the BWT. This research studies the compression performance of BWT on digital images using different predictors and context partitions. The major interest of the research is in finding efficient ways to make BWT suitable for lossless image compression.;This research studied three different approaches to improve the compression of image data by BWT. First, the idea of preprocessing the image data before sending it to the BWT compression scheme is studied by using different mapping and prediction schemes. Second, different variations of MTF were investigated to see which one works best for Image compression with BWT. Third, the concept of context partitioning for BWT output before it is forwarded to the next stage in the compression scheme.;For lossless image compression, this thesis proposes the removal of the MTF stage from the BWT compression pipeline and the usage of context partitioning method. The compression performance is further improved by using MED predictor on the image data along with the 8-bit mapping of the prediction residuals before it is processed by BWT.;This thesis proposes two schemes for BWT-based image coding, namely BLIC and BLICx, the later being based on the context-ordering property of the BWT. Our methods outperformed other text compression algorithms such as PPM, GZIP, direct BWT, and WinZip in compressing images. Final results showed that our methods performed better than the state of the art lossless image compression algorithms, such as JPEG-LS, JPEG2000, CALIC, EDP and PPAM on the natural images.

In this paper we propose a new approach for applying genetic programming to lossless data compression based on combining well-known lossless compression algorithms. The file to be compressed is divided into chunks of a predefined length, and GP is asked to find the best possible compression algorithm for each chunk in such a way to minimise the total length of the compressed file. This technique is referred to as ldquoGP-ziprdquo: The compression algorithms available to GP-zip (its function set) are: arithmetic coding (AC), Lempel-Ziv-Welch (LZW), unbounded prediction by partial matching (PPMD), run length encoding (RLE), and Boolean minimization. In addition, two transformation techniques are available: Burrows-Wheeler transformation (BWT) and move to front (MTF). In experimentation with this technique, we show that when the file to be compressed is composed of heterogeneous data fragments (as is the case, for example, in archive files), GP-zip is capable of achieving compression ratios that are superior to those obtained with well-known compression algorithms.

Analysis for loss less data compression delivers the relevant data about variations of them as well as to describe the possible causes for each algorithm and best performing data types. It describes the basic lossless techniques of data compression Huffman encodes, Arithmetic Encoding, and Lempel Ziv Encodings then briefly with their effectiveness under varying data types of Latin text, audio and video. These properties give the solution of which lossless compression algorithm more suitable compared to other from the Saving Percentage, compression ratio, time of compression and time of decompression with Low Bandwidth Network. Moreover here Lossless Data Compression Algorithms (LDCA) being implemented and tested Huffman compression, Arithmetic compression, and Lempel Ziv algorithms, the implemented result shows that LZW algorithm saves more size than that of the others two with text file, Huffman compression algorithm saves more file sizes and the time takes to compressed decompress is higher than that of other two for audio file type and finally Huffman performs greater on very huge data compressions that is due to much compressing capability.

The exponential growth in remote sensing, coupled with advancements in integrated circuits (IC) design and fabrication technology for communication, has prompted the progress of Wireless Sensor Networks (WSN). WSN comprises of sensor nodes and hubs fit for detecting, processing, and communicating remotely. Sensor nodes have limited resources such as memory, energy and computation capabilities restricting their ability to process large volume of data that is generated. Compressing the data before transmission will help alleviate the problem. Many data compression methods have been proposed but mainly for image processing and a vast majority of them are not pertinent on sensor nodes because of memory impediment, energy utilization and handling speed. To overcome this issue, authors in this research have chosen Run Length Encoding (RLE) and Adaptive Huffman Encoding (AHE) data compression techniques as they can be executed on sensor nodes. Both RLE and AHE are capable of balancing compression ratio and energy utilization. In this paper, a hybrid method comprising RLE and AHE, named as H-RLEAHE, is proposed and further investigated for sensor nodes. In order to verify the efficacy of the data compression algorithms, simulations were run, and the results compared with the compression techniques employing RLE, AHE, H-RLEAHE, and without the use of any compression approach for five distinct scenarios. The results demonstrate the RLE’s efficiency, as it surpasses alternative data compression methods in terms of energy efficiency, network speed, packet delivery rate, and residual energy throughout all iterations.

Data compression in environmental monitoring systems is indispensable, to save the power of the monitoring nodes and reduce the size of system database as well as increasing the efficiency of data throughput. In this article, a new lossless compression algorithm has been proposed based on combining of three famous compression methods which are: Delta encoding, Run-length encoding and Huffman encoding. The main advantage of the proposed algorithm is its simplicity, which makes it suitable for systems with very limited resources. The proposed algorithm has been applied to measured samples extracted from a research monitoring system. The results indicated that the compression ratio of the initial data by the proposed algorithm reaches 90%, which leads to a significant preservation of the storage space for the system.

This paper proposes a compression algorithm using octonary repetition tree (ORT) based on run length encoding (RLE). Generally, RLE is one type of lossless data compression method which has duplication problem as a major issue due to the usage of code word or flag. Hence, ORT is offered instead of using a flag or code word to overcome this issue. This method gives better performance by means of compression ratio, i.e. 99.75%. But, the functioning of ORT is not good in terms of compression speed. For that reason, physical- next generation secure computing (PHY-NGSC) is hybridized with ORT to raise the compression speed. It uses an MPI-open MP programming paradigm on ORT to improve the compression speed of encoder. The planned work achieves multiple levels of parallelism within an image such as MPI and open MP for parallelism across a group of pictures level and slice level, respectively. At the same time, wide range of data compression like multimedia, executive files and documents are possible in the proposed method. The performance of the proposed work is compared with other methods like accordian RLE, context adaptive variable length coding (CAVLC) and context-based arithmetic coding (CBAC) through the implementation in Matlab working platform.

The Run Length Encoding (RLE) algorithm substitutes long runs of identical symbols with the value of that symbol followed by the binary representation of the frequency of occurrences of that value. This lossless technique is effective for encoding images where many consecutive pixels have similar intensity values. One of the major problems of RLE for encoding runs of bits is that the encoded runs have their lengths represented as a fixed number of bits in order to simplify decoding. The number of bits assigned is equal to the number required to encode the maximum length run, which results in the addition of padding bits on runs whose lengths do not require as many bits for representation as the maximum length run. Due to this, the encoded output sometimes exceeds the size of the original input, especially for input data where in the runs can have a wide range of sizes. In this paper, we propose VaFLE, a general-purpose lossless data compression algorithm, where the number of bits allocated for representing the length of a given run is a function of the length of the run itself. The total size of an encoded run is independent of the maximum run length of the input data. In order to exploit the inherent data parallelism of RLE, VaFLE was also implemented in a multithreaded OpenMP environment. Our algorithm guarantees better compression rates of upto 3X more than standard RLE. The parallelized algorithm attains a speedup as high as 5X in grayscale and 4X in color images compared to the RLE approach.

Space systems demand some of the most precise and accurate technology. The body of space systems are subjected to many vibrations and shocks. To make sure their stable operation, these vibrations are monitored continuously at a higher sampling rate which results in large amount of data. The data is transmitted over a wireless network to the ground station. Therefore data compression becomes unavoidable, not only for efficient utilization of transmission bandwidth but also for reduced storage requirements. Even the smallest vibration is critical in space applications. So lossless compression techniques are preferred. In this work, a lossless compression technique is proposed where the input data is split into two sets which helps in attaining data redundancy and then the algorithm is applied. Modified Move to front(MTF) coding is done on this data, where positional values of a dictionary are transmitted instead of the sample value. Run length encoding(RLE) is done on the MTF coded data. Next all the successively repeating number patterns in the data set is recognized and RLE coded. The compression ratio obtained for data without and with noise are 4.15 and 2.75 respectively.

This paper considers implementation of audio compression using the lossless compression techniques like dynamic Huffman coding and Run Length Encoding (RLE). Audio file is firstly preprocessed to find sampling frequency and the encoded data bits in sample audio file. After that dynamic Huffman and RLE is applied. The design of dynamic Huffman coding technique involves evaluation of the probabilities of occurrence “on the fly”, as the ensemble is being transmitted and RLE is based on finding the runs of the data i.e. repeating strings and replacing it by single data element and its count. These techniques work with a common goal to obtain the utmost possible compression ratio and less Time Elapsed to compress. The competence of the proposed methods is verified by applying these techniques to variety of audio data. Stimulus behind this work is to offer a detail analysis of lossless compression methods and finding the one which is best suited for compression of multimedia data in cognitive radio environment.