Run-length Encoding Research Articles

Reversible Data Hiding using Block-wise Histogram Shifting and Run-length Encoding

Histogram shifting-based Reversible Data Hiding (RDH) is a well-explored information security domain for secure message transmission. In this paper, we propose a novel RDH scheme that considers the block-wise histograms of the image. Most of the existing histogram shifting techniques will have additional overhead information to recover the overflow and/or the underflow pixels. In the new scheme, the meta-data that is required for a block is embedded within the same block in such a way that the receiver can perform image recovery and data extraction. As per the proposed data hiding process, all the blocks need not be used for data hiding, so we have used marker information to distinguish between the blocks which are used to hide data and the blocks which are not used for data hiding. Since the marker information needs to be embedded within the image, we have compressed the marker information using run-length encoding. The run-length encoded sequence is represented by an Elias gamma encoding procedure. The compression on the marker information ensures a better Embedding Rate (ER) for the proposed scheme. The proposed RDH scheme will be useful for secure message transmission also where we are also concerned about the restoration of the cover image. The proposed scheme's experimental analysis is conducted on the USC-SIPI image dataset maintained by the University of Southern California, and the results show that the proposed scheme performs better than the existing schemes.

Merge and Split approach incolor image Steganography using Run Length Encoding and LSB Techniques

The purpose of steganography is to communicate secret messages between the sender and intended recipient in such a way that no one suspects the very existence of the message. The techniques aim to protect the secret information from third parties by embedding them into other information such as text, audio signals, images, and video frames. In this paper we propose a novel approach of hiding secret messages in multiple images (a cover image)using run length encoding and LSB techniques and communicate the message to intended person over the communication channel by transmitting individual images.Experiments are performed on a set of color images and performance of the proposed system is presented.

Sparse Project VCF: efficient encoding of population genotype matrices.

SummaryVariant Call Format (VCF), the prevailing representation for germline genotypes in population sequencing, suffers rapid size growth as larger cohorts are sequenced and more rare variants are discovered. We present Sparse Project VCF (spVCF), an evolution of VCF with judicious entropy reduction and run-length encoding, delivering >10× size reduction for modern studies with practically minimal information loss. spVCF interoperates with VCF efficiently, including tabix-based random access. We demonstrate its effectiveness with the DiscovEHR and UK Biobank whole-exome sequencing cohorts.Availability and implementationApache-licensed reference implementation: github.com/mlin/spVCF.Supplementary information Supplementary data are available at Bioinformatics online.

A Signal Complexity-Based Approach for AM–FM Signal Modes Counting

Frequency modulated signals appear in many applied disciplines, including geology, communication, biology and acoustics. They are naturally multicomponent, i.e., they consist of multiple waveforms, with specific time-dependent frequency (instantaneous frequency). In most practical applications, the number of modes—which is unknown—is needed for correctly analyzing a signal; for instance for separating each individual component and for estimating its instantaneous frequency. Detecting the number of components is a challenging problem, especially in the case of interfering modes. The Rényi Entropy-based approach has proven to be suitable for signal modes counting, but it is limited to well separated components. This paper addresses this issue by introducing a new notion of signal complexity. Specifically, the spectrogram of a multicomponent signal is seen as a non-stationary process where interference alternates with non-interference. Complexity concerning the transition between consecutive spectrogram sections is evaluated by means of a modified Run Length Encoding. Based on a spectrogram time-frequency evolution law, complexity variations are studied for accurately estimating the number of components. The presented method is suitable for multicomponent signals with non-separable modes, as well as time-varying amplitudes, showing robustness to noise.

Privacy-preserving compression model for efficient IoMT ECG sharing

Electrocardiogram (ECG) signals are widely used in most remote IoMT systems. Continuous monitoring of patients is required, especially in a pandemic time where doctors recommend telemedicine. This means a massive amount of ECG data is generated, sent to cloud servers, and needs to be shared with legitimate professionals. Therefore, this paper proposes a novel privacy-preserving and efficient technique to reduce the burden on the network while ensuring the privacy of ECG. To ensure efficiency, we use a shallow neural network to learn/remember the ECG shape and represents that in a few neurons. To avoid any loss, the minor residuals between this representation and the original signal is measured and encoded to small footprint using Burrow–Wheeler transform (BWT), followed by move-to-front (MTF) and run-length encoding. To ensure the privacy, only representation neurons are encrypted using a SessionKey obtained from the health authority (HA) server along with SessionID every-time an ECG signal needs to be transmitted. Hence, health authority alone is able to link that SessionID to the patient. Whenever a doctor wants to diagnose an ECG of the patient, HA will share only those two parameters which allow the authorized doctor to see a specific ECG. The model is evaluated using raw ECG data collected from Physio-net. The results obtained are compared and analyzed with the widely used state of art techniques. The results show that the proposed technique outperforms the other techniques by an increase of 50% in size reduction and 60% in transmission time while ensuring the privacy.

A new technique for data compression

Data compression has become more important than ever, due to the increasing demand for internet use and the exchange of a huge amount of images, videos, audio and documents as well as the growing demand for electronic archiving by government departments that produce thousands of documents per day. In this paper, the proposed technique for document compression will be presented.The proposed technique is a lossless and completed technique it is consists of two parts the compression part and decompression part. The compression part contains of some basic stages such as: pre-processing, blocks processing, run length encoding(RLE), replace maximum values by unused values, minimize levels, delta encoding, compression of ones values, encryption. After the encryption process is complete, the outputs are stored in two separate binary files with the extension of bmp;one of them is the header file and is considered a key for the second file which contains the compressed data. This technique applied on twenty documents and compared with other methods compression such as RLE, jpeg, tiff and png. The experimental results showed that the proposed technique gives a higher compression ratio than the rest of the methods.

P.101 Neurotic personal characteristics of gifted adolescents

We propose a binary image encryption method in joint transform correlator (JTC) by aid of the run-length encoding (RLE) and Quick Response (QR) code, which enables lossless retrieval of the primary image. The binary image is encoded with RLE to obtain the highly compressed data, and then the compressed binary image is further scrambled using a chaos-based method. The compressed and scrambled binary image is then transformed into one QR code that will be finally encrypted in JTC. The proposed method successfully, for the first time to our best knowledge, encodes a binary image into a QR code with the identical size of it, and therefore may probe a new way for extending the application of QR code in optical security. Moreover, the preprocessing operations, including RLE, chaos scrambling and the QR code translation, append an additional security level on JTC. We present digital results that confirm our approach.

Geometrical Fusion Based on Chain Code Representation

In this paper, a new geometrical image fusion method introduced by using the representation of chain code for the image objects that been matched among the images to be fused. The input images are separated into number of objects. A matching method is applied to extract the matching objects from the overall objects that included in the origin images. Then the chain code string is calculated for each one. The run length encoding is applied on that code strings to reduce the space that required. The image resulted from the geometrical fusion is more informative which contain the more important features those included in the origin images.

Metaheuristic-based vector quantization approach: a new paradigm for neural network-based video compression

Video compression has great significance in the communication of motion pictures. Video compression techniques try to remove the different types of redundancy within or between video sequences. In the temporal domain, the video compression techniques remove the redundancies between the highly correlated consequence frames of the video. In the spatial domain, the video compression techniques remove the redundancies between the highly correlated consequence pixels (samples) in the same frame. Evolving neural-networks based video coding research efforts are focused on improving existing video codecs by performing better predictions that are incorporated within the same codec framework or holistic methods of end-to-end video compression schemes. Current neural network-based video compression adapts static codebook to achieve compression that leads to learning inability from new samples. This paper proposes a modified video compression model that adapts the genetic algorithm to build an optimal codebook for adaptive vector quantization that is used as an activation function inside the neural network’s hidden layer. Background subtraction algorithm is employed to extract motion objects within frames to generate the context-based initial codebook. Furthermore, Differential Pulse Code Modulation (DPCM) is utilized for lossless compression of significant wavelet coefficients; whereas low energy coefficients are lossy compressed using Learning Vector Quantization (LVQ) neural networks. Finally, Run Length Encoding (RLE) is engaged to encode the quantized coefficients to achieve a higher compression ratio. Experiments have proven the system’s ability to achieve higher compression ratio with acceptable efficiency measured by PSNR.

Fast image blending for high-quality panoramic images on mobile phones

This paper presents a fast image blending approach for combining a set of registered images into a composite mosaic with no visible seams and minimal texture distortion on mobile phones. A unique seam image is generated using two-pass nearest distance transform, which is independent on the order of input images and has good scalability. Each individual mask can be extracted from this seam image quickly. To promote blending speed and reduce memory usage in building high resolution image mosaics on mobile phones, the seam image and mask images are compressed using run-length encoding, and all the following mask operations are built on run-length encoding scheme. Moreover, single instruction multiple data instruction set is used in Gaussian and Laplacian pyramids construction to improve the blending speed further. The use of run-length encoding for masks processing leads to reduced memory requirements and a compact storage of the mask data, and the use of single instruction multiple data instruction set achieves better parallelism and faster execution speed on mobile phones.

An efficient machine tool control instruction compression method for networked numerical control systems

With the development of computer and information technology, function modules of computer numerical control (CNC) systems are moving up to network environment to simplify local machine tool controller architecture and increase function reusability. In this context, network-based CNC system generates machine control instructions (MCI) in network environment and send MCI to local machines. Compared with building a remote real-time communication channel between networked CNC and local machines, applying offline interpolator is more feasible under state-of-the-art hardware and software. However, the size of MCI generated by offline interpolator is large. Transmitting MCI without compression will be inefficient and increase lead time. Currently there has been few researches on the features and compression of offline MCI. In this paper, a simple and effective compression method for MCI named multichannel differential run-length encoding (MDRL) is proposed. Utilizing the limited variation and high repetition features of MCI, MDRL algorithm first calculates the differences of MCI, then encodes the amplitudes and repetition of difference values using fixed length codes. Compression performance discrepancy among different channels is controlled using compressed data chunks. The decompression process of MDRL only needs a few hundred of bytes of space and can process in real-time. Compression performance of MDRL is tested using three common manufacturing tasks under different system configurations. The results show that the mean compression ratio of MDRL is 3.41%-15.86%. The advantages of MDRL are further validated by comparing with three other data compression algorithms. By using MDRL, MCI transmission efficiency can be significantly increased for networked CNC systems to increase function reusability and shorten lead time.

Combination of Cryptography Algorithm Knapsack and Run Length Enconding (RLE) Compression in Treatment of Text File

The Knapsack algorithm cryptographic method is asymmetric cryptography in which the encryption key is different from the decryption key. In addition to text file security issues, the issue of the size of a text file is also a consideration. Large text files can be compressed by doing the compression process. Run Length Encoding (RLE) algorithm is an algorithm that reduces the size of a text file, if the text has a lot of repetition of characters. The combination of Knapsack and RLE algorithms can guarantee Text files cannot be seen by unauthorized users and can guarantee text files can be stored in low capacity media files. In this study, the authors made a combination of knapsack and RLE algorithm in text files. In the Knapsack algorithm there will be an increase in the size of the text file, this can be seen in the example of a case where the size of the plaintext (the original message) is 9 bytes, then after the encryption process the text file size becomes 7 bytes. Because of that the use of a combination of encryption and data compression is better because the file becomes smaller than the combination of data compression and encryption. Plaintext that has a lot of repetition of characters will be well compressed.

A Novel Bit plane Compression based Reversible Data Hiding Scheme with Arnold Transform

Reversible data hiding (RDH) is an active research area in the field of information security. The RDH scheme allows the transmission of a secret message by embedding it into a cover image, and the receiver can recover the original cover image along with the extraction of the secret message. In this paper, we propose a bit plane compression based RDH scheme to hide a sequence of secret message bits into a grayscale image. In the proposed method, a selected bit plane of the cover image will be compressed using run-length encoding (RLE) scheme. Further, the RLE sequence has been efficiently encoded as a binary sequence using Elias gamma encoding method. The Elias gamma encoded bit sequence concatenated with the secret message bits are used to replace the selected bit plane after performing a sequence of Arnold transform. The Arnold transform helps to find a new scrambled version of the bit plane which is very close to the original bit plane to ensure the visual quality of the stego image. The RLE is a lossless compression technique, therefore recovery of the original image is possible by the receiver. The experimental study of the proposed scheme on the images from standard image dataset (USC-SIPI image dataset) shows that the proposed scheme outperforms the existing scheme in terms of the visual quality of the stego image without compromising the data embedding rate.

Nonsubsampled Contourlet Transform based Video Compression using Huffman and Run Length Encoding for Multimedia Applications

Nonsubsampled Contourlet Transform based Video Compression using Huffman and Run Length Encoding for Multimedia Applications

FSMI: Fast computation of Shannon mutual information for information-theoretic mapping

Exploration tasks are embedded in many robotics applications, such as search and rescue and space exploration. Information-based exploration algorithms aim to find the most informative trajectories by maximizing an information-theoretic metric, such as the mutual information between the map and potential future measurements. Unfortunately, most existing information-based exploration algorithms are plagued by the computational difficulty of evaluating the Shannon mutual information metric. In this article, we consider the fundamental problem of evaluating Shannon mutual information between the map and a range measurement. First, we consider 2D environments. We propose a novel algorithm, called the fast Shannon mutual information (FSMI). The key insight behind the algorithm is that a certain integral can be computed analytically, leading to substantial computational savings. Second, we consider 3D environments, represented by efficient data structures, e.g., an OctoMap, such that the measurements are compressed by run-length encoding (RLE). We propose a novel algorithm, called FSMI-RLE, that efficiently evaluates the Shannon mutual information when the measurements are compressed using RLE. For both the FSMI and the FSMI-RLE, we also propose variants that make different assumptions on the sensor noise distribution for the purpose of further computational savings. We evaluate the proposed algorithms in extensive experiments. In particular, we show that the proposed algorithms outperform existing algorithms that compute Shannon mutual information as well as other algorithms that compute the Cauchy–Schwarz quadratic mutual information (CSQMI). In addition, we demonstrate the computation of Shannon mutual information on a 3D map for the first time.

Empirical Mode Decomposition and Wavelet Transform Based ECG Data Compression Scheme

ObjectiveIn health-care systems, compression is an essential tool to solve the storage and transmission problems. In this regard, this paper reports a new electrocardiogram (ECG) data compression scheme which employs sifting function based empirical mode decomposition (EMD) and discrete wavelet transform. MethodEMD based on sifting function is utilized to get the first intrinsic mode function (IMF). After EMD, the first IMF and four significant sifting functions are combined together. This combination is free from many irrelevant components of the signal. Discrete wavelet transform (DWT) with mother wavelet ‘bior4.4’ is applied to this combination. The transform coefficients obtained after DWT are passed through dead-zone quantization. It discards small transform coefficients lying around zero. Further, integer conversion of coefficients and run-length encoding are utilized to achieve a compressed form of ECG data. ResultsCompression performance of the proposed scheme is evaluated using 48 ECG records of the MIT-BIH arrhythmia database. In the comparison of compression results, it is observed that the proposed method exhibits better performance than many recent ECG compressors. A mean opinion score test is also conducted to evaluate the true quality of the reconstructed ECG signals. ConclusionThe proposed scheme offers better compression performance with preserving the key features of the signal very well.

A Comparative Analysis of Performance of Several Wavelet Based ECG Data Compression Methodologies

Compression of an electrocardiogram (ECG) signal has given much consideration to the researchers since the computer-aided analysis of ECG has come into being. In some critical cases, viz., astronauts, a person under cardiac surveillance, ambulatory patients and in Holter monitoring system, continuous ECG data recording and transmitting from one location to other location is required. However, the size of the recorded data becomes so voluminous, that its transmission of data becomes practically impossible. In this paper, ECG data compression using wavelet-based techniques are presented, such that: a) wavelet packet transform with run-length encoding (RLE), b) wavelet transform with Huffman encoding, c) wavelet transform with RLE and d) wavelet transform and Lempel ZivWelch (LZW). The results have been tested using MIT-BIH (Massachusetts Institute of Technology/Beth Israel Hospital) arrhythmia databases. The performances of these methodologies are examined in the quantitative and qualitative manner. From Tabular results, it can be observed that the methodology based on WT and LZW provides efficient results in terms of compression ratio (CR∼=20 to 30) and peak root mean square difference (PRD∼=0.01 to 1.8) both, hence the overall QS value is improved.

Lossless Compression of Medical Images based on the Differential Probability of Images

Lossless compression is crucial in the remote transmission of large-scale medical image and the retainment of complete medical diagnostic information. The lossless compression method of medical image based on differential probability of image is proposed in this study. The medical image with DICOM format was decorrelated by the differential method, and the difference matrix was optimally coded by the Huffman coding method to obtain the optimal compression effect. Experimental results obtained using the new method were compared with those using Lempel–Ziv–Welch, modified run–length encoding, and block–bit allocation methods to verify its effectiveness. For 2-D medical images, the lossless compression effect of the proposed method is the best when the object region is more than 20% of the image. For 3-D medical images, the proposed method has the highest compression ratio among the control methods. The proposed method can be directly used for lossless compression of DICOM images.

Selective Image Encryption Based on DCT, Hybrid Shift Coding and Randomly Generated Secret Key

Most of today’s techniques encrypt all of the image data, which consumes a tremendous amount of time and computational payload. This work introduces a selective image encryption technique that encrypts predetermined bulks of the original image data in order to reduce the encryption/decryption time and thecomputational complexity of processing the huge image data. This technique is applying a compression algorithm based on Discrete Cosine Transform (DCT). Two approaches are implemented based on color space conversion as a preprocessing for the compression phases YCbCr and RGB, where the resultant compressed sequence is selectively encrypted using randomly generated combined secret key.The results showed a significant reduction in image quality degradation when applying the system based on YCbCr over RGB, where the compression ratio was raised in some of the tested images to 50% for the same Peak Signal to Noise Ratio (PSNR). The usage of 1-D DCT reduced the transform time by 47:1 times comparedto the same transform using 2-D DCT. The values of the adaptive scalar quantization parameters were reduced to the half for the luminance (Y band) to preserve the visual quality, while the chrominance (Cb and Cr bands) were quantized by the predetermined quantization parameters. In the hybrid encoder horizontal zigzag,block scanning was applied to scan the image. The Detailed Coefficient (DC) coefficients are highly correlated in this arrangement- where DC are losslessly compressed by Differential Pulse Coding Modulation (DPCM) and theAccumulative Coefficients (AC) are compressed using Run Length Encoding (RLE). As a consequence, for the compression algorithm, the compression gain obtained was up to 95%. Three arrays are resulted from each band (DC coefficients, AC values, and AC runs), where the cipher is applied to some or all of those bulksselectively. This reduces the encryption decryption time significantly, where encrypting the DC coefficients provided the second best randomness and the least encryption/decryption time recorded (3 10-3 sec.) for the entire image. Although the compression algorithm consumes time but it is more efficient than the savedencryption time.

Using codes in place of DNA Sample in Databases to reduce Storage

Biological data mainly comprises of Deoxyribonucleic acid (DNA) and protein sequences. These arethe biomolecules that are present in all cells of human beings. Due to the self-replicating property ofDNA, it is a key constituent of genetic material that exists in all breathing creatures. This biomolecule(DNA) comprehends the genetic material obligatory for the operational and expansion of all personifiedlives. To save DNA data of a single person we require 10CD-Rom's. In this paper, A lossless three-phasecompression algorithm is presented for DNA sequences. In the first phase the dataset is segmentedhaving tetra groups and then the resultant genetic sequences are compressed in the form of uniquenumbers (e.g Array Index) and in the second phase binary code is generated on the bases of array indexnumbers and in the last phase the modified version of Run Length Encoding (RLE) is applied on thedataset.The newly proposed technique has been implemented and its performance is also measured on samples.It has achieved the best average compression ratio. After Storing different DNA Samples.