Run-length Encoding Research Articles

Assessment of Compressed and Decompressed ECG Databases for Telecardiology Applying a Convolution Neural Network

Incalculable numbers of patients in hospitals as a result of COVID-19 made the screening of heart patients arduous. Patients who need regular heart monitoring were affected the most. Telecardiology is used for regular remote heart monitoring of such patients. However, the resultant huge electrocardiogram (ECG) data obtained through regular monitoring affects available storage space and transmission bandwidth. These signals can take less space if stored or sent in a compressed form. To recover them at the receiver end, they are decompressed. We have combined telecardiology with automatic ECG arrhythmia classification using CNN and proposed an algorithm named TELecardiology using a Deep Convolution Neural Network (TELDCNN). Discrete cosine transform (DCT), 16-bit quantization, and run length encoding (RLE) were used for compression, and a convolution neural network (CNN) was applied for classification. The database was formed by combining real-time signals (taken from a designed ECG device) with an online database from Physionet. Four kinds of databases were considered and classified. The attained compression ratio was 2.56, and the classification accuracies for compressed and decompressed databases were 0.966 and 0.990, respectively. Comparing the classification performance of compressed and decompressed databases shows that the decompressed signals can classify the arrhythmias more appropriately than their compressed-only form, although at the cost of increased computational time.

A new real-time lossless data compression algorithm for ECG and PPG signals

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.

Grayscale and color images encryption in a DRPE assisted by SPHIT, RLE, QR code and two chaos logistic maps

A non-linear encryption approach for compressed grayscale and color images is proposed in Double Random Phase Encoding (DRPE) setup using two compression algorithms: Set Partitioning in Hierarchical Trees (SPIHT) and Run-length encoding (RLE). The compressed image is then segmented and embedded in multiple Quick Response (QR) codes, which are combined again into a single image. Further, this single image is scrambled by two-coupled chaos logistic maps, at which the scrambled image is now ready for DRPE encryption. The use of two-coupled chaos logistic increases the security level of this proposed DRPE encryption from various attacks. The proposed DRPE encryption is extended to handed color images, which are segregated into three color-channels (Red, Green and Blue), where each channel is individually encrypted. For grayscale images, the proposed system will have three security key codes while for color images it has nine security key codes. The robustness and invulnerability of the proposed scheme against various attacks is demonstrated through cryptoanalysis and computer simulation results.

A Lossless Compression and Encryption Method for Remote Sensing Image Using LWT, Rubik’s Cube and 2D-CCM

This paper proposes a lossless encryption–compression algorithm for large-scale remote sensing images. Firstly, the red, green and blue components of color image are compressed by a lossless predictive encoding. Then, the lifting wavelet transform (LWT) is used to decompose the encoding results. And a new Rubik’s cube transformation is introduced to scramble the decomposed coefficients, which uses the chaotic sequence generated by 2D Cubic–Chebyshev map (2D-CCM). The initial values of 2D-CCM are obtained from the chi-square test values of the three components, which leads to the algorithm related to the plaintext image. After that, the scrambling coefficients are thresholding, and the position sequences generated in the process are encrypted and compressed by the proposed encrypted run-length encoding (E-RLE). The processed coefficients are further compressed by Huffman encoding. At the end, the final results are obtained by a novel helix diffusion which is related to the chaotic sequence. Experimental results show that, this algorithm achieves higher lossless compression ratio with lower time complexity, and the encryption scheme has higher security.

Pengaruh Peningkatan Kualitas Citra Menggunakan Modifikasi Kontras Pada Kompresi Data RLE

Data compression is needed so that the need for storage media and data transfer time becomes more efficient. This study compressed image data using the Run-Length Encoding (RLE) method. The test data is the original image (gray scale) and the image results of improving image quality (image enhancement) using contrast modification. Modification of contrast using contrast stretching methods. Through experiments wanting to know the extent to which the RLE method works less effectively for images with complex color intensity. The image of contrast modification results has a more complex color intensity or more varied pixel value. Obtained the number of pairs (p, q) RLE in the image of contrast modification results is less than the original image, with the pair ratio (p, q) RLE ranges from 0.64% to 1.59%. Although this image has a more varied pixel value than its original image, it can produce a compression ratio of the number of pairs (p, q) RLE.

Electrocardiogram signal compression using tunable-Q wavelet transform and meta-heuristic optimization techniques

Electrocardiogram (ECG) signals are the biomedical signals commonly used in the prognosis of cardiovascular diseases. ECG recordings need to be stored and transferred when telemedicine-based healthcare systems are required. These data are stored in a digitized format at higher bits per sample that requires ample space for storage. Therefore, this motivated us to develop efficient compression methodologies for ECG signals. In this regard, this work proposes compression techniques using the optimized tunable-Q wavelet transform (TQWT). For this purpose, TQWT parameters are optimized using several meta-heuristic optimization algorithms such as variants of PSO, ABC and its hybrid with PSO, GWO and its hybrid with PSO, and Sparse PSO. These hybrid methods and Sparse PSO have been utilized for the first time to optimize TQWT. Subsequently, thresholding and quantization are performed by using a dead-zone quantizer (DZQ). The quantized coefficients are encoded by utilizing a lossless compression technique run-length encoding (RLE). The proposed algorithms have been examined on the MIT-BIH arrhythmia database. It is clear from the results that significant compression has been achieved when compared to existing techniques. The performance of the proposed algorithms has been evaluated in terms of various evaluation parameters that are compression ratio (CR), percent-root-mean square difference (PRD), signal-to-noise ratio (SNR), and quality score (QS).

Combining 3D run-length encoding coding and searching techniques for medical image compression

<p><span>The field of image compression became a mandatory tool to face the increasing and advancing production of medical images, besides the inevitable need for smaller size of medical images in telemedicine systems. In spite of its simplicity, run-length encoding (RLE) technique is a considerably effective and practical tool in the field of lossless image compression. Such that, it is widely recommended for 2D space that utilizes common searching techniques like linear and zigzag. This paper adopts a new algorithm taking advantage of the potential simplicity of the run-length algorithm to contribute a volumetric RLE approach for binary medical data in the 3D form. The proposed volumetric-RLE (VRLE) algorithm differs from the 2D RLE approach utilizing correlations of intra-slice only, which is used for compressing binary medical data utilizing voxel-correlations of inter-slice. Furthermore, several forms of scanning are used to extending proposed technique like Hilbert and Perimeter, which determines the best possible procedure of scanning suitable for data morphology considering the segmented organ. This work employs proposed algorithm on four image datasets to get as sufficient as possible evaluation. Experimental results and benchmarking illustrate that the performance of the proposed technique surpasses other state-of-the-art techniques with 1:30 enhancement on average.</span></p>

Reversible data hiding in encrypted DICOM images using sorted binary sequences of pixels

In this paper, a novel reversible data hiding method for encrypted DICOM images is proposed. The method utilizes binary decomposition of the input data paired with a sorting process of the obtained binary sequences to ensure efficient data embedding in each predefined data block for specific most significant bit (MSB) planes while exploiting the properties of run-length encoding. The proposed scheme is lossless, and based on the receiver's privileges, allows separate or simultaneous recovery of the original and embedded data. The proposed method was originally designed for 16-bit DICOM images of monochrome photometric interpretation, however it can be used with any type of data while providing satisfactory results. A computer-based analysis has been carried out and simulation results for DICOM and non-DICOM images are shown at the end of this paper.

Memory-efficient sensor data compression

We treat scalar data compression in sensor network nodes in streaming mode (compressing data points as they arrive, no pre-compression buffering). Several experimental algorithms based on linear predictive coding (LPC) combined with run length encoding (RLE) are considered. In entropy coding stage we evaluated (a) variable-length coding with dynamic prefixes generated with MTF-transform, (b) adaptive width binary coding, and (c) adaptive Golomb-Rice coding. We provide a comparison of known and experimental compression algorithms on 75 sensor data sources. Compression ratios achieved in the tests are about 1.5/4/1000000 (min/med/max), with compression context size about 10 bytes.

Data reduction based on machine learning algorithms for fog computing in IoT smart agriculture

Smart agriculture applications that analyse and manage agricultural yield using IoT systems may suffer from intermittent operation due to cloud disconnections commonly occurring in rural areas. A fog computing solution enables the IoT system to process data faster and deal with intermittent connectivity. However, the fog needs to send a high volume of data to the cloud and this can cause link congestion with unusable data traffic. Here we propose an approach to collect and store data in a fog-based smart agriculture environment and different data reduction methods. Sixteen techniques for data reduction are investigated; eight machine learning (ML) methods combined with run-length encoding, and eight combined with Huffman encoding. Our experiment uses two real data sets, where the first contains air temperature and humidity values, and the second has soil moisture and temperature conditions. The fog filters cluster the unlabelled data using unsupervised machine learning algorithms that group data into categories according to their value ranges in all experiments. Supervised learning classification methods are also used to predict the class of data samples from these categories. After that, the fog filter compresses the identified categories using two data compression techniques, run-length encoding (RLE) and the Huffman encoding, preserving the data time series nature. Our results reveal that a k-means combined with RLE method achieved the highest reduction, where the fog needed to store and transmit only 3%–6% of the original data generated by sensors. • It is a challenge to manage a massive amount of data generated by sensors in IoT. • Combining machine learning with data compression results in a larger data reduction. • Depending on the context, the fog needs to decide which classifier should use.

Hybrid compression technique for image hiding using Huffman, RLE and DWT

All kinds of information should be compressed before being sent over the web to save time & expense. The primary goal of this article is to create and construct a systematic and safe technique for encryption data that can be utilized to carry out a crisp compacting steganography technique. The approach may be used to reduce the volume of data sent with each transfer, allowing for faster delivery when utilizing sluggish web or taking up less space on a drive. The study offers a densification with steno approach that may be utilized to hide information with substantial privacy and complete invisible by combining RSA encryption technique with steganography expertise such as RLE (Run-length encoding), Huffman coding, DWT (Discrete Wavelet Transform) with LSB cryptographic technology. The very first process is based on RSA's ability to decode and encode private texts. The next process is compressing, which will include Huffman encoding based approaches to compress confidential stuff using efficient steganography technologies, RLE, which is a traditional approach to encrypt information, or DWT based on hatchbacks covering imagery by loss encryption in order to decrease the covering picture's size. The encrypted information will be implanted in the compressed cover picture using LSB (Least Significant Bit). When compared to various developed countries (Chen et al., 2021; Lakshmanan, 2020; Song et al., 2011; Yang et al., 2021; Ali et al., 2020) of the architecture that uses simulation results, this technique has a good ranking efficiency and a methodical system.

Compression Methodologies for Columnar Database Optimization

Today’s life is completely dependent on data. Conventional relational databases take longer to respond to queries because they are built for row-wise data storage and retrieval. Due to their efficient read and write operations to and from hard discs, which reduce the time it takes for queries to produce results, columnar databases have recently overtaken traditional databases. To execute Business Intelligence and create decision-making systems, vast amounts of data gathered from various sources are required in data warehouses, where columnar databases are primarily created. Since the data are stacked closely together, and the seek time is reduced, columnar databases perform queries more quickly. With aggregation queries to remove unnecessary data, they allow several compression techniques for faster data access. To optimise the efficiency of columnar databases, various compression approaches, including NULL Suppression, Dictionary Encoding, Run Length Encoding, Bit Vector Encoding, and Lempel Ziv Encoding, are discussed in this work. Database operations are conducted on the compressed data to demonstrate the decrease in memory needs and speed improvements.

A Telemedicine based on EEG Signal Compression and Transmission

As a result of RLE and DWT, an effective technique for compressing and transmitting EEG signals was developed in this study. With low percent root-mean-square difference (PRD) values, this algorithm's compression ratio (CR) is high. The life database had 50 EEG patient records. In clinical and research contexts, EEG signals are often recorded at sample rates between 250 and 2000 Hz. New EEG data-collection devices, on the other hand, may record at sampling rates exceeding 20,000 Hz. Time domain (TD) and frequency domain (FD) analysis of EEG data utilizing DWT retains the essential and major features of EEG signals. The thresholding and quantization of EEG signal coefficients are the next steps in implementing this suggested technique, followed by encoding the signals utilizing RLE, which improves CR substantially. A stable method for compressing EEG signals and transmission based on DWT (discrete wavelet transform) and RLE (run length encoding) is presented in this paper in order to improve and increase the compression of the EEG signals. According to the proposed model, CR, PRD, PRDN (normalized percentage root mean square difference), QS (quality score), and SNR (signal to noise ratio) are averaged over 50 records of EEG data and range from 44.0% to 0.36 percent to 5.87 percent to 143 percent to 3.53 percent to 59 percent, respectively.

ECG Data Compression Using Modified Run Length Encoding of Wavelet Coefficients for Holter Monitoring

ObjectiveIn cardiac patient-care, compression of long-term ECG data is essential to minimize the data storage requirement and transmission cost. Hence, this paper presents a novel electrocardiogram data compression technique which utilizes modified run-length encoding of wavelet coefficients. MethodFirst, wavelet transform is applied to the ECG data which decomposes it and packs maximum energy to less number of transform coefficients. The wavelet transform coefficients are quantized using dead-zone quantization. It discards small valued coefficients lying in the dead-zone interval while other coefficients are kept at the formulated quantized output interval. Among all the quantized coefficients, an average value is assigned to those coefficients for which energy packing efficiency is less than 99.99%. The obtained coefficients are encoded using modified run-length coding. It offers higher compression ratio than conventional run-length coding without any loss of information. ResultsCompression performance of the proposed technique is evaluated using different ECG records taken from the MIT-BIH arrhythmia database. The average compression performance in terms of compression ratio, percent root mean square difference, normalized percent mean square difference, and signal to noise ratio are 17.18, 3.92, 6.36, and 28.27 dB respectively for 48 ECG records. ConclusionThe compression results obtained by the proposed technique is better than techniques recently introduced by others. The proposed technique can be utilized for compression of ECG records of Holter monitoring.

A spatial image compression algorithm based on run length encoding

Image compression is vital for many areas such as communication and storage of data that is rapidly growing nowadays. In this paper, a spatial lossy compression algorithm for gray scale images is presented. It exploits the inter-pixel and the psycho-visual data redundancies in images. The proposed technique finds paths of connected pixels that fluctuate in value within some small threshold. The path is calculated by looking at the 4-neighbors of a pixel then choosing the best one based on two conditions; the first is that the selected pixel must not be included in another path and the second is that the difference between the first pixel in the path and the selected pixel is within the specified threshold value. A path starts with a given pixel and consists of the locations of the subsequently selected pixels. Run-length encoding scheme is applied on paths to harvest the inter-pixel redundancy. After applying the proposed algorithm on several test images, a promising quality vs. compression ratio results have been achieved.

Fast First-Move Queries through Run-Length Encoding

We introduce a novel preprocessing-based algorithm to solve the problem of determining the first arc of a shortest path in sparse graphs. Our algorithm achieves query running times on the 100 nanosecond scale, being significantly faster than state-of-the-art first-move oracles from the literature. Space consumption is competitive, due to a compression approach that rearranges rows and columns in a first-move matrix and then performs run length encoding (RLE) on the contents of the matrix.

Effects of Using Static Methods with Contourlet Transformation on Speech Compression

Compression of speech signal is an essential field in signal processing. Speech compression is very important in today’s world, due to the limited bandwidth transmission and storage capacity. This paper explores a Contourlet transformation based methodology for the compression of the speech signal. In this methodology, the speech signal is analysed using Contourlet transformation coefficients with statistic methods as threshold values, such as Interquartile Filter (IQR), Average Absolute Deviation (AAD), Median Absolute Deviation (MAD) and standard deviation (STD), followed by the application of (Run length encoding) They are exploited for recording speech in different times (5, 30, and 120 seconds). A comparative study of performance of different transforms is made in terms of (Signal to Noise Ratio,Peak Signal to Noise Ratio,Normalized Cross-Correlation, Normalized Cross-Correlation) and the compression ratio (CR). The best stable result of implementing our algorithm for compressing speech is at level1 with AAD or MAD, adopting Matlab 2013a language.

Lossless ECG Encoder with Multi-Channel Adaptive Fuzzy Predictor and Enhanced Entropy Encoding in Wireless Body Sensor Networks

With the advancement of wireless technology, Wireless Body Sensor Networks, such as Electrocardiograms (ECGs) will serve as state-of-the-art method for electronic-healthcare systems and applications. Like other digital communications, however, ECGs highlight power consumption as the main design constraint and bottleneck as it affects device lifespan and data accuracy. Hence, power reduction and power management techniques and schemes have been developed to eliminate this constraint such as hardware optimization, source and channel coding, signal conditioning, and resolution control. This paper proposes a lossless ECG encoder that combines existing data compression techniques specifically the adaptive fuzzy predictor based on fuzzy decision making and an enhanced entropy encoding that utilizes algorithms in both run length encoding (RLE) and incremental prefix encoding. Simulation results that the proposed scheme outperforms the entropy encoding using Huffman, RLE, and predictive encoding schemes in compression ratio (CR), with the enhanced entropy encoding leading the pre-existing compression techniques by 24.0907 on RLE and 23.6580 on Huffman.

Audio compression using transforms and high order entropy encoding

<span>Digital audio is required to transmit large sizes of audio information through the most common communication systems; in turn this leads to more challenges in both storage and archieving. In this paper, an efficient audio compressive scheme is proposed, it depends on combined transform coding scheme; it is consist of i) bi-orthogonal (tab 9/7) wavelet transform to decompose the audio signal into low & multi high sub-bands, ii) then the produced sub-bands passed through DCT to de-correlate the signal, iii) the product of the combined transform stage is passed through progressive hierarchical quantization, then traditional run-length encoding (RLE), iv) and finally LZW coding to generate the output mate bitstream. The measures Peak signal-to-noise ratio (PSNR) and compression ratio (CR) were used to conduct a comparative analysis for the performance of the whole system. Many audio test samples were utilized to test the performance behavior; the used samples have various sizes and vary in features. The simulation results appear the efficiency of these combined transforms when using LZW within the domain of data compression. The compression results are encouraging and show a remarkable reduction in audio file size with good fidelity.</span>

Image compression methods for efficient storage and transmission

This paper presents a detailed and comprehensive review of image compression methods, emphasizing their role in optimizing both storage and transmission efficiency across various domains, from everyday use in social media to specialized applications like medical imaging and satellite data processing. We systematically explore both traditional and contemporary image compression techniques, categorizing them into lossless and lossy methods, transform-based approaches, and the latest advancements in machine learning-based compression. Lossless compression techniques, including Run-Length Encoding (RLE), Huffman Coding, Lempel-Ziv-Welch (LZW), and the Portable Network Graphics (PNG) format, are discussed for their ability to preserve image quality perfectly, albeit at the cost of relatively lower compression ratios. Conversely, lossy compression methods, such as JPEG and fractal compression, offer significant file size reduction by discarding non-essential data, while still maintaining acceptable visual quality for many practical applications. We further delve into transform-based approaches like Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), which form the backbone of popular standards such as JPEG and JPEG 2000, enabling more efficient data representation in the frequency domain. Additionally, the study highlights emerging machine learning and deep learning techniques, such as autoencoders and Generative Adversarial Networks (GANs), that are pushing the boundaries of image compression by achieving unprecedented compression ratios while minimizing perceptual loss in image quality. Through a comparative analysis, we evaluate these methods based on multiple performance metrics, including compression ratio, computational complexity, image fidelity (measured via Peak Signal-to-Noise Ratio, PSNR, and Structural Similarity Index, SSIM), and their practical applications across different industries. Our findings suggest that while traditional methods such as JPEG, PNG, and JPEG 2000 remain widely adopted due to their simplicity and efficiency, emerging techniques driven by deep learning show great potential in adapting to specific image characteristics, achieving higher compression ratios, and better preserving image quality under extreme compression. Finally, this paper identifies key challenges and trends in the field, such as the increasing computational demands of advanced techniques, the need for adaptive compression strategies, and the importance of standardization for broad industry adoption. We conclude that while traditional methods will continue to play a significant role, the future of image compression lies in the integration of machine learning and content-aware technologies that dynamically optimize compression performance across diverse image types and application contexts.