Comparative Analysis Run-Length Encoding Algorithm and Fibonacci Code Algorithm on Image Compression

Due to the high sampling rate, the recorded Electrocardiograms (ECG) data are huge. For storing and transmitting ECG data, wide spaces and more bandwidth are therefore needed. The ECG data are also very important to preprocessing and compress so that it is distributed and processed with less bandwidth and less space effectively. This manuscript is aimed at creating an effective ECG compression method. The reported ECG data are processed first in the pre-processing unit (ProUnit) in this method. In this unit, ECG data have been standardized and segmented. The resulting ECG data would then be sent to the Compression Unit (CompUnit). The unit consists of an algorithm for lossy compression (LosyComp), with a lossless algorithm for compression (LossComp). The randomness ECG data is transformed into high randomness data by the failure compression algorithm. The data's high redundancy is then used with the LosyComp algorithm to reach a high compression ratio (CR) with no degradation. The LossComp algorithms recommended in this manuscript are the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). LossComp algorithms such as Arithmetic Encoding (Arithm) and Run Length Encoding (RLE) are also suggested. To evaluate the proposed method, we measure the Compression Time (CompTime), and Reconstruction Time (RecTime) (T), RMSE and CR. Simulation results suggest the highest output in compression ratio and in complexity by adding RLE after the DCT algorithm. The simulation findings indicate that the inclusion of RLE following the DCT algorithm increases performance in terms of CR and complexity. With CR = 55% with RMSE = 0:14 and above 94% with RMSE = 0:2, DCT as a LossComp algorithm was utilized initially, followed by RLE as a LossComp algorithm.

Objectives: To evaluate the proposed deep learning-based method for biometric template protection schemes with other existing image compression methods. Methods: In this study, four image compression methods such as JPEG_LS, Vector quantization (VQ), Run-length encoding (RLE), and the Autoencoder methods are implemented. The experimental results are compared with the different performance parameters that are applied over the CASIA I iris dataset possessing 756 images. Findings: The proposed autoencoder method algorithm offered enhanced results in terms of PSNR, SSIM, MSE, and CR when compared to other transform methods. The deep neural network-based autoencoder algorithm achieved the highest compression ratio of 89.05 percent, while the conventional algorithms achieved the highest image quality rate of 94.05 percent. Novelty: A novel autoencoder-based image compression model has been proposed in this study. The proposed Autoencoder (AE) method incorporates five stages like Initialization, Learning compression representations, Generation of chaotic sequence, Image encryption and decryption, and Image reconstruction. The generation of the chaotic sequence using the stochastic logistic map, as well as the learning compression representations contributes towards the novelty of this study. Keywords: Image compression; Autoencoder; Runlength encoding (RLE); Vector quantization

This paper proposes an image processing approach for compression of ECG signals based on 2D compression standards. This will explore both inter-beat and intra-beat redundancies that exist in the ECG signal leading to higher compression ratio (CR) as compared to 1D signal compression standards which explore only the inter-beat redundancies. The proposed method is twofold: In the first step, ECG signal is preprocessed and QRS detection is used to detect the peaks. In the second step, baseline wander is removed and a 2D array of data is obtained through the cut-and-align beat approach. Further beat reordering is done to arrange the ECG array depending upon the similarities available in the adjacent beats. Then ECG signal is compressed by first applying the lossless compression scheme called the 2D Run Length Encoding (RLE), and then a variant of discrete wavelet transform (DWT) called set partitioning in hierarchical trees (SPIHT) is applied to further compress the ECG signal. The proposed method is evaluated on the selected data from MITs Beth Israel Hospital, and it was conceded that this method surpasses some of the prevailing methods in the literature by attaining a higher compression ratio (CR) and moderate percentage-root-mean-square difference (PRD).

Color Image compression has become an important technology to decrease memory space and to reduce transmission time. The present study provides comparative values between RGB and YCbCr color spaces with respect to PSNR, execution time and compression ratio. Fractal coding is a valuable method which utilizes for compression of color image. This method is most suitable for irregular shape of images. Long encoding times still remain as main disadvantage of this compression technique. A hybrid fractal coding technique based on YCbCr scheme and Discrete Cosine Transform (DCT) with quadtree and isosceles triangle segmentation is proposed in the present study. The study needs six phases, first, conversion of input RGB image to YCbCr scheme; second, the resulting image (YCbCr) is partitioned separately based on quadtree approach and isosceles triangle segmentation approach; third, DCT is applied to every block of the partition, fourth, scanning of every block value and zigzag manner is applied to prevent zero co-efficient; fifth, both quad tree and isosceles triangle segmentation approaches are applied separately for having partitioned resulting image; sixth, to compress the image, Run Length Encoding (RLE) approach is utilized. Test results demonstrate that proposed strategy gives higher compression ratio, PSNR value with high processing speed without degrading the image quality.Keywords: Affine Transformation, Discrete Cosine Transform (DCT), Fractal Compression, Isosceles Triangle Segmentation, Iterated Function System (IFS), Partitioned Iterated Function System (PIFS), Quadtree Partition, Run Length Encoding (RLE), YCbCr Transform

Data compression plays a significant role in Body Sensor Networks (BSN). This is true since the sensors in BSNs have limited battery power and memory; sensor data needs to be transmitted regularly, and in lossless manner to provide prompt, accurate feedback. The paper evaluates lossless data compression algorithms including Run Length Encoding (RLE), Lempel Zev Welch (LZW), and Huffman on data from wearable devices and compares them in terms of Compression Ratio, Compression Factor, Savings Percentage and Compression Time. It also evaluates a data deduplication technique used for Low Bandwidth File Systems (LBFS), Two Thresholds Two Divisors (TTTD) algorithm, to determine if it is suitable for BSN data. First, through experiments s we arrive at a set of parameter values that give compression ratio above 50 on BSN data. Next, based on performance evaluation results of TTTD and classical compression algorithms including RLW, LAW, and Huffman, it proposes a technique to combine multiple algorithms in sequence. Upon comparison of the performance, it is found that the new algorithm, TTTD-H, which executes TTTD and Huffman in sequence, significantly improves the compression factor against both TTTD and Huffman. Performance evaluation has been carried out in two sets of BSN data.

This paper presents a 3 in 1 standalone FPGA system which can perform color image blur detection in parallel with compression and encryption. Both blur detection and compression are based on the 3-level Haar wavelet transform, which is used as a common building block to save the resources. The compression is based on performing the hard thresholding scheme followed by the Run Length Encoding (RLE) technique. The encryption is based on the 128-bit Advanced Encryption Standard (AES), which is considered one of the most secure algorithms. Moreover, the modified Lorenz chaotic system is combined with the AES to perform the Cipher Block Chaining (CBC) mode. The proposed system is realized using HDL and implemented using Xilinx on XC5VLX50T FPGA. The system has utilized only 25&#x0025; of the available slices. Furthermore, the system can achieve a throughput of 3.458 Gbps, which is suitable for real-time applications. To validate the compression performance, the system has been tested with all the standard <inline-formula> <tex-math notation="LaTeX">$256\times 256$ </tex-math></inline-formula> images. It is shown that depending on the amount of details in the image, the system can achieve 30dB PSNR at compression ratios in the range of (0.08-0.38). The proposed system can be integrated with digital cameras to process the captured images on-the-fly prior to transmission or storage. Based on the application, the blurred images can be either marked for future enhancement or simply filtered out.

SummaryThe ability to design smarter, more predictive healthcare solutions for use in the community (at work and at home) and in healthcare facilities has been greatly enhanced by recent developments in the Internet of Health Things (IoHT) and cyber physical systems (CPS). These data collected by such medical sensors will be sent in a large quantity to the fog gateway of IoHT networks. These data should be forwarded to far cloud for further analysis and processing. Therefore, sending all of these data over the IoHT network to the data center will impose a significant burden on the IoHT network. This paper proposed a new lossless electroencephalogram (EEG) compression technique (NeLECoT) for fog computing‐based IoHT networks. It encodes the data of the patient at the fog gateway prior to sending it to the data center, thereby reducing the data's size. In the fog node, the NeLECoT combines three efficient techniques: clustering based on density‐based spatial clustering of applications with noise (DBSCAN), RLE (run length encoding), and Huffman encoding (HE). The clustering based on DBSCAN separates a massive volume of captured data into small groups of closely related (or similar) data. RLE encodes clustered EEG data, and the resulting file is encoded with HE. The fog gateway then transmits the encoded file to the cloud. Numerous simulation experiments were carried out, and the findings demonstrated that the suggested NeLECoT achieved better results than the competing techniques in terms of transmitted data size, compression ratio, compression power, compression time, decompression time, and average compression power.

Multimedia is highly competitive world, one of the properties that is reflected is speed of download and upload of multimedia elements: text, sound, pictures, animation. This paper presents CRUSH algorithm which is a lossless compression algorithm. CRUSH algorithm can be used to compress files. CRUSH method is fast and simple with time complexity O(n) where n is the number of elements being compressed.Furthermore, compressed file is independent from algorithm and unnecessary data structures. As the paper will show comparison with other compression algorithms like Shannon&amp;ndash;Fano code, Huffman coding, Run Length Encoding, Arithmetic Coding, Lempel-Ziv-Welch (LZW), Run Length Encoding (RLE), Burrows-Wheeler Transform.Move-to-Front (MTF) Transform, Haar, wavelet tree, Delta Encoding, Rice &amp;amp;Golomb Coding, Tunstall coding, DEFLATE algorithm, Run-Length Golomb-Rice (RLGR).

We present a method to speed up the dynamic program algorithms used for solving the HMM decoding and training problems for discrete time-independent HMMs. We discuss the application of our method to Viterbi’s decoding and training algorithms [21], as well as to the forward-backward and Baum-Welch [4] algorithms. Our approach is based on identifying repeated substrings in the observed input sequence. We describe three algorithms based alternatively on byte pair encoding (BPE) [19], run length encoding (RLE) and Lempel-Ziv (LZ78) parsing [12]. Compared to Viterbi’s algorithm, we achieve a speedup of Ω(r) using BPE, a speedup of \(\Omega(\frac{r}{\log r})\) using RLE, and a speedup of \(\Omega(\frac{\log n}{k})\) using LZ78, where k is the number of hidden states, n is the length of the observed sequence and r is its compression ratio (under each compression scheme). Our experimental results demonstrate that our new algorithms are indeed faster in practice. Furthermore, unlike Viterbi’s algorithm, our algorithms are highly parallelizable.

The increase of data usage causes problems in data storage, indirectly making the need for data storage also to increase. One alternative solution that can be done is to compress the file so that the file becomes smaller in size so it saves storage space. The algorithm used in this research is the Run Length Encoding algorithm, the Even Rodeh Code algorithm, and the Variable Length Binary Encoding algorithm which are the types of lossless compression. The algorithm will calculate its performance based on Compression Ratio, Ratio of Compression, Redundancy, Compression Time, and Decompression Time. The file that will be used in the data compression process is the file extension *.txt. This study used homogeneous strings (strings that have the same character) and heterogeneous strings (strings that have different characters) in testing the algorithm. In the compression process with a homogeneous string, the combination of the Run Length Encoding algorithm with the Variable Length Binary Encoding algorithm is better than the combination of the Run Length Encoding algorithm and the Even Rodeh Code algorithm with a Compression Ratio of 18.84% and a decompression time of 0.01295 ms. While the compression process on heterogeneous strings from the combination of the Run Length Encoding algorithm with the Even Rodeh Code algorithm is better than the combination of the Run Length Encoding algorithm with Variable Length Binary Encoding algorithms with Compression Ratio of an average of 52.45% and fewer decompression times of 4.93002 ms

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.

Huffman coding is an important part of image compression technology, the image compression platform is based on GUI, and Huffman is also widely used. This paper introduces the basic principle of the Huffman algorithm, compares it with arithmetic coding and run length encoding, and expounds on the application of these three algorithms in JPEG compression. The AC algorithm combined block-based, fine texture models and adaptive arithmetic coding in the given an example. The RLE algorithm used automatic threshold, direction judgment, and selective value counts to improve its compression efficiency. JPEG algorithm adopted an adaptive quantization table to reduce the distortion rate of image compression. This paper proves the possibility of a better compression rate and distortion rate of the hybrid compression algorithm by demonstrating the improved example of the basic image compression algorithm. In the future, the improved basic algorithms can be combined based on the original JPEG algorithm, and different algorithms can be integrated on the GUI to face different use environments.

Exchange of information through the medium of the internet allows information can be accessed by unauthorized parties. Techniques for securing information that is commonly used is Cryptography. This research will use a combination of two classic algorithms which are Columnar Transposition Cipher and a Caesar Cipher with a Linear Congruential Generator modification. Information in the form of images often has some of the same pixel value components. For saving the memory then the writers use the technique of compression with Lempel Ziv Welch algorithm, which is a dictionary-based compression algorithm. Stages of the process used in this study are that the pictures will be encrypted using the algorithm of Columnar Transposition Cipher, next step the file that has been encrypted will be encrypted with the Caesar Cipher algorithm using a combination of and Linear Congruential generators are modified. Then the image file that has been encrypted will be compressed using the second algorithm Lempel Ziv Welch. For the implementation of programming language C # systems used. The results of this study showed that the combination of Columnar Transposition Cipher and a Caesar Cipher with a Linear Congruential Generators could keep the confidentiality, integrity, and security of data. Lempel Ziv Welch algorithm produces the value of the average compression ratio (RC) 67.05%, in Compression Ratio (CR) aspect the value is 1.49, and in Space Saving (SS) parameter, the percentage is 32.93%.

In a recent era of modern technology, there are many problems for storage, retrieval and transmission of data. Data compression is necessary due to rapid growth of digital media and the subsequent need for reduce storage size and transmit the data in an effective and efficient manner over the networks. It reduces the transmission traffic on internet also. Data compression try to reduce the number of bits required to store digitally. The various data and image compression algorithms are widely use to reduce the original data bits into lesser number of bits. Lossless data and image compression is a special class of data compression. This algorithm involves in reducing numbers of bits by identifying and eliminating statistical data redundancy in input data. It is very simple and effective method. It provides good lossless compression of input data. This is useful on data that contains many consecutive runs of the same values. This paper presents the implementation of Run Length Encoding for data compression.

One of the main challenges of modern computer systems is to overcome the ever more prominent limitations of disk I/O and memory bandwidth, which today are thousands-fold slower than computational speeds. In this paper, we investigate reducing memory bandwidth and overall I/O and memory access times by using multithreaded compression and decompression of large datasets. Since the goal is to achieve a significant overall speedup of I/O, both level of compression achieved and efficiency of the compression and decompression algorithms, are of importance. Several compression methods for efficient disk access for large seismic datasets are implemented and empirically tested on on several modern CPUs and GPUs, including the Intel i7 and NVIDIA c2050 GPU. To reduce I/O time, both lossless and lossy symmetrical compression algorithms as well as hardware alternatives, are tested. Results show that I/O speedup may double by using an SSD vs. HDD disk on larger seismic datasets. Lossy methods investigated include variations of DCT-based methods in several dimensions, and combining these with lossless compression methods such as RLE (Run-Length Encoding) and Huffman encoding. Our best compression rate (0.16%) and speedups (6 for HDD and 3.2 for SSD) are achieved by using DCT in 3D and combining this with a modified RLE for lossy methods. It has an average error of 0.46% which is very acceptable for seismic applications. A simple predictive model for the execution time is also developed and shows an error of maximum 5% vs. our obtained results. It should thus be a good tool for predicting when to take advantage of multithreaded compression. This model and other techniques developed in this paper should also be applicable to several other data intensive applications.