LZ based Compression Benchmark on PE Files

The explosion of data, both in motion and in rest, along with the popularity of cloud computing, have resulted in the need for better in-line compression solutions. Current Huffman coding modes are optimal for a single metric: compression ratio (quality) or rate (performance). The ratio-focused mode, for example, further compresses the data by 15% as compared to the rate-focused mode, but takes 20% longer to execute.In this paper, we show how to balance the tradeoff between compression ratio and rate, without modifying existing standards and legacy decompression implementations. We present two Huffman encoding heuristics that achieve close-to-optimal compression rate and ratio (within 2%). Our proposed heuristics are practical - the first is better suited for hardware while the second for software implementations. We believe that such in-line compression heuristics can help enable the continued advancement of cloud computing and storage.

Background and objectiveShort-read sequencing is becoming the standard of practice for the study of structural variants associated with disease. However, with the growth of sequence data largely surpassing reasonable storage capability, the biomedical community is challenged with the management, transfer, archiving, and storage of sequence data.MethodsWe developed Hierarchical mUlti-reference Genome cOmpression (HUGO), a novel compression algorithm for aligned reads in the sorted Sequence Alignment/Map (SAM) format. We first aligned short reads against a reference genome and stored exactly mapped reads for compression. For the inexact mapped or unmapped reads, we realigned them against different reference genomes using an adaptive scheme by gradually shortening the read length. Regarding the base quality value, we offer lossy and lossless compression mechanisms. The lossy compression mechanism for the base quality values uses k-means clustering, where a user can adjust the balance between decompression quality and compression rate. The lossless compression can be produced by setting k (the number of clusters) to the number of different quality values.ResultsThe proposed method produced a compression ratio in the range 0.5–0.65, which corresponds to 35–50% storage savings based on experimental datasets. The proposed approach achieved 15% more storage savings over CRAM and comparable compression ratio with Samcomp (CRAM and Samcomp are two of the state-of-the-art genome compression algorithms). The software is freely available at https://sourceforge.net/projects/hierachicaldnac/with a General Public License (GPL) license.LimitationOur method requires having different reference genomes and prolongs the execution time for additional alignments.ConclusionsThe proposed multi-reference-based compression algorithm for aligned reads outperforms existing single-reference based algorithms.

INTRODUCTION: Genetic data plays a key role in the healthcare area in specific, but they are typically very large in size. Many research shows that absence of DNA information at the right time is one of the major causes of error in the healthcare area. The more genomics information that analysts secure, the better the prospects for individual and general wellbeing. Persevering and retrieving genetic information in the right form within the given time is a big challenge in the field of Healthcare. Effectively, pre-birth DNA tests screen for formative variations from the norm. Before long, patients will have their blood sequenced to detect any nonhuman DNA that may flag an irresistible illness. Later on, somebody managing malignancy will most likely track the movement of the sickness by having the DNA and RNA of single cells from various tissues sequenced every day. DNA sequencing of whole population will give a complete and better prediction of population wellbeing.OBJECTIVES: Hereditary data is growing exponentially; hence it is hard to deal with the consistently developing hereditary database. The human genome in its base configuration occupies almost thirty terabyte of storage space. Computational assets are constrained. Not just storage, transmission abilities and run time memory is likewise constrained. Data Compression is a test when the hereditary information is exponentially expanding. It is critical to save the integrity of hereditary information while packing it. Hence the main objective of this paper is to develop a lossless DNA compression algorithm that not only gives better compression but also help in retrieval of Information for efficient use in the area of Healthcare.METHODS: In this paper a lossless hereditary data compression method is being proposed. The proposed calculation works in a horizontal mode and utilization a reference based substitution technique for compression. The principle thought of this paper is in the kind of similarity scanned. All the predominant hereditary Compression methods search for similarity within the chromosome. These calculations either pursue flat mode or vertical mode for accomplishing compression. But whichever method the existing genetic compression algorithms use, they are all based on searching similarities within the chromosome i.e. they exploit only inter chromosomal similarities. The current studies focus will show that compression ratio achieved by analyzing individual chromosome is always less than the method in which we analyze and compress intra chromosomal similarities.RESULTS: This study shows that by simply using exactly matching repeats amongst all the chromosomes of the same genome, not only the compression ratio is improving but also a detailed study of all the similarities and differences between two genomes of the same species can be conducted.CONCLUSION: In this study, a new compression algorithm is being proposed for compressing DNA. Along with Inter chromosomal similarities, Intra chromosomal similarities are considered for this method. The results clearly shows that intra chromosomal matches are bigger and more than inter chromosomal matches which helps us to achieve better compression ratio.

Cache-block compression is a highly effective technique for both reducing accesses to lower levels in the memory hierarchy (cache compression) and minimizing data transfers (link compression). While many effective cache-block compression algorithms have been proposed, the design of these algorithms is largely ad hoc and manual and relies on human recognition of patterns. In this article, we take an entirely different approach. We introduce a class of “byte-select” compression algorithms, as well as an automated methodology for generating compression algorithms in this class. We argue that, based on upper bounds within the class, the study of this class of byte-select algorithms has potential to yield algorithms with better performance than existing cache-block compression algorithms. The upper bound we establish on the compression ratio is 2X that of any existing algorithm. We then offer a generalized representation of a subset of byte-select compression algorithms and search through the resulting space guided by a set of training data traces. Using this automated process, we find efficient and effective algorithms for various hardware applications. We find that the resulting algorithms exploit novel patterns that can inform future algorithm designs. The generated byte-select algorithms are evaluated against a separate set of traces and evaluations show that Byte-Select has a 23% higher compression ratio on average. While no previous algorithm performs best for all our data sets which include CPU and GPU applications, our generated algorithms do. Using an automated hardware generator for these algorithms, we show that their decompression and compression latency is one and two cycles respectively, much lower than any existing algorithm with a competitive compression ratio.

This study presents insights into the computational complexity of fractal image compression (FIC) algorithms. Unlike JPEG, a fractal encoder necessitates more CPU time in contrast to the decoder. The study examines various factors that impact the encoder and its computational cost. Many researchers have dedicated themselves to the field of fractal encoding to overcome the computational cost of the FIC algorithm. Here, this study offers a look over the approaches in the aspect of time complexity. The automated baseline fractal compression algorithm is studied to demonstrate the understanding of delay in the encoder. The study establishes how various approaches trade‐off between the quality of decoder, compression ratio, and CPU time. The experiment section shows the bargain between fidelity criteria of the baseline algorithm.

Lossy /lossless image compression technique results will either improve the quality of image with reduced compression ratio or degrades the image with better compression ratio. But on dealing with the combined lossy/lossless techniques, they provide the output with better image quality and high compression rate, however, the techniques failed to obtain better efficiency due to less predictive nature. Hence, to acquire an image having better compression rate with improved predictive nature, a novel framework is introduced in our proposed work. It uses a Deep Residual Network Bi-Layer Enfolded Compressor (DRN-BLEC) to enhance the image quality. In order to obtain better compression rate, the Mexican Meyer Hat Wavelet Transform (MMHWT) is used. Similarly, the well knowledge quantization is achieved by utilizing DRN for learning; it is then followed by the Lloyds quantization technique that groups the feature values by quantizing it, which in turn boost-up the resolution of the image. Consequently with DRN, the process of encoding and decoding doesn’t requires to be done separately since two residual layer of the DRN are trained to encode and decode the images, which thereby reduces the time required for compression process. Thus a compressed image with high resolution is obtained without redundancy, moreover with high compression ratio. The architecture of the DRN-BLEC is implemented in MATLAB and the respective result structure is validated.

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.

With the development of modern computer technology, the growth of purchase funds for storage equipment is far behind the growth of data. How to effectively use the limited storage resources to store data becomes the focus of our research and the data compression technology is the key to solve this problem. The traditional compression technology on the CPU platform could not meet the requirements for processing massive data in the compression rate and the energy cost. The GPU provides a new solution for its strong parallel computation ability. In this paper we present an implementation of Parallel Matching Lempel-Ziv-Storer-Szymanski (PMLZSS), a high speed lossless data compression algorithm by using CUDA framework. The basic idea of our implementation of the PMLZSS algorithm on GPUs is the introduction of a paralleled matrix matching. The data needed to be compressed are divided into multiple dictionary strings and pre-read strings as the vertical axis and horizontal axis of the matrices, respectively. All of the matrices are paralleled matched in the different blocks. Compared with the traditional serial CPU platform LZSS compression algorithm and BZIP2 compression algorithm, the experimental data shows that on the premise of the basic compression rate unchanged, relative to the serial LZSS, the compression speed of PMLZSS is improved about 16x, while to the BZIP2, about 12x.

PurposeCone‐beam computerized tomography (CBCT) is routinely performed for verification of patient position in radiotherapy. It produced a large amount of data which require a method to compress them for efficient storage. In this study three video compression algorithms were introduced and their performance was evaluated based on real patient data.Materials and methodsAt first CBCT images in multiple sets of a patient were transferred from reconstruction workstation or exported from treatment planning system. Then CBCT images were sorted according to imaging time (time‐prioritized sequence) or imaging location (location‐prioritized sequence). Next, this sequence was processed by a video compression algorithm and resulted in a movie. Three representative video compression algorithms (Motion JPEG 2000, Motion JPEG AVI, and MPEG‐4) were employed and their compression performance was evaluated based on the CBCT data of 30 patients.ResultsAmong three video compression algorithms, Motion JPEG 2000 has the least compression ratio since it is a lossless compression algorithm. Motion JPEG AVI and MPEG‐4 have higher compression ratios than Motion JPEG 2000 but come with certain image losses. For MPEG‐4, location‐prioritized sequences show higher compression ratio than time‐prioritized sequences. Based on the results achieved on the clinical target verification application, the registration accuracy of CBCT after decompression was comparable to that of the original CBCT.ConclusionsVideo compression algorithms could provide a higher compression ratio comparing to static image compression algorithm. Although the loss of CBCT image due to compression its impact on registration accuracy of patient positioning is almost negligible. Video compression method is an effective way to substantially reduce the size of CBCT images for storage.

Speed flame propagation in Otto cycle engines is one of the principal characteristics of fuel and is fundamental in defining the ignition advance. The greater the propagation speed the less the negative work required to compress the mixture before the piston reaches the top dead center and the higher the cycle’s efficiency. This paper presents experimental results of time measurements of the fuel’s ignition and the maximum pressure rating in the combustion chamber of a Cooperative Fuel Research engine specially instrumented. The combustion duration measurements of oxygenated and non-oxygenated fuels were taken as a function of the compression ratio (8:1, 10:1 and 12:1) and lambda (λ). The speed flame propagation in the combustion chamber is significantly changed with the change of the lambda different compression ratios. The VNG has a maximum in the speed flame propagation in the stoichiometric region (λ = 1.0) in all compression rates in this study. Similar behavior occurs with ethanol and gasohol, but only in compression ratio 12:1. Ethanol and gasohol have the higher rate of flame propagation for all compression ratios measured as compared to the non-oxygenated (isooctane) and oxygenated fuels (MTBE and TAEE).

A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. MC-EEG is represented in suitable multiway (multidimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multiway forms of MC-EEG. A compression algorithm is built based on the principle of “lossy plus residual coding,” consisting of a matrix/tensor decomposition-based coder in the lossy layer followed by arithmetic coding in the residual layer. This approach guarantees a specifiable maximum absolute error between original and reconstructed signals. The compression algorithm is applied to three different scalp EEG datasets and an intracranial EEG dataset, each with different sampling rate and resolution. The proposed algorithm achieves attractive compression ratios compared to compressing individual channels separately. For similar compression ratios, the proposed algorithm achieves nearly fivefold lower average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm.

Due to advancements in healthcare monitoring systems, the Internet of Things concepts are proficiently utilized in the medical field to detect and diagnose the physical health problems. The compression of more substantial medical information is a significant issue that requires ample data storage space and takes longer transmission time. Though several compression algorithms are actualized in past cases, there is an absence of an upgraded approach to achieve improved signal compression without influencing signal quality. Hence a proficient signal compression algorithm is proposed in our work to provide an enhanced electrocardiogram (ECG) signal compression without any data loss and to acquire increased compression ratio (CR) and zero construction error. In this proposed approach, the input ECG signal dataset from the MIT‐BIH arrhythmia database gets influenced by noise because of the electrical measuring gadget. Hence, preprocessing is done by the proposed multi‐scoop notch filter (MSNF) to denoise this signal by removing the specified noise frequency range of around (1‐50) Hz. This proposed MSNF is designed with adaptiveness that has achieved the enhanced denoising by adjusting the notch frequency. In addition, to extricate the sophisticated ECG signal features, Fast Fourier Transform is being utilized, and that performs and decomposes the signal elegantly and obtains characteristics of the signal in the frequency domain. After feature extraction, optimal signal compression is performed by our proposed priority‐based convolutional auto‐encoder (PCAE) that provides better compression with almost zero reconstruction error by encoding the signals into lower‐dimensional vectors in convolutional layers which are again reconstructed using a decoding approach. The experimental results are then assessed using the performance metrics that include signal to noise ratio (SNR), CR, and percentage root‐mean‐square difference (PRD). The attained results are 1.83% as average PRD value, average SNR is about 33 dB, and average CR is about 35.2% whereas the traditional CAE approach has average values of 2.05% PRD, 23.45 dB SNR, and 32.2% CR.

Sketch based coding of grey level images

To address the issue of insufficient real-time capability in existing Internet search engines within the Internet of Things environment, this research investigates the architecture of Internet of Things search systems. It proposes a data dynamic prediction algorithm tailored for the process of entity information search in the Internet of Things. The study is based on the design of a data compression algorithm for the Internet of Things entity information search process using the Rotating Gate Compression Algorithm. The algorithm employs the Least Squares Support Vector Machine to dynamically predict changes in entity node states in the Internet of Things, aiming to reduce sensor node resource consumption and achieve real-time search. Finally, the research introduces an Internet of Things entity information search system based on the data dynamic algorithm. Performance test results indicate that the segmented compression algorithm designed in the study can enhance compression accuracy and compression rate. As compression accuracy increases, errors also correspondingly increase. The prediction algorithm designed in the study shows a decrease in node energy consumption as reporting cycles increase, reaching 0.2 at 5 cycles. At the 5-cycle point, the prediction errors on two research datasets are 0.5 and 7.8, respectively. The optimized data dynamic prediction algorithm in the study effectively reduces node data transmission, lowers node energy consumption, and accurately predicts node state changes to meet user search demands.

Electrocardiogram (ECG) signals are frequently acquired nowadays to detect various heart diseases. Nowadays, IoT-enabled wearable devices are in demand for distant or telemedicine-based healthcare applications. However, the acquisition process of ECG signals generates a huge amount of data, which negatively impacts the storage and transmission efficiency of these devices. As a result, an efficient compression algorithm is needed for effective ECG data management. Therefore, a compression algorithm for 2D ECG signals is proposed that employs the 1D Cohen-Daubechies-Feauveau 9/7 wavelet transform on 2D ECG signals. The proposed method effectively improves compression performance by increasing sparsity among the transform coefficients. Following that, obtained coefficients are quantized, and significant ones are retained using the target-based reconstruction error. The adaptive Huffman encoding is used to further enhance the compression once the quantized coefficients have been encoded. The experimental work is tested on MIT-BIH arrhythmia database, and the effect of different anomalies on compression performance is also assessed. The compression efficacy is evaluated in comparison to existing compression methods, and other wavelet transforms such as sym2, sym4, haar, db5, coif4, and beta wavelets. The proposed algorithm's performance is assessed in terms of quality score, percent root-mean-square difference, signal-to-noise ratio, and compression ratio. These factors were averaged out to get values of 30.23, 5.07, 26.78dB, and 7.21, respectively. Results are evident that the proposed method can significantly improve storage efficiency and may also improve bandwidth utilization during real-time data transfer.