Run-length Coding Research Articles

Performance analysis of modified wavelet difference reduction methods in image compression and transmission

The wavelet difference reduction (WDR) method, a variant of run-length coding, finds its significance in data transmission applications. Over time, numerous enhanced iterations of WDR methods have emerged. Notably, the Adaptive Scalable WDR method exhibits superior coding gains, as evidenced by the peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM), when compared to its predecessors. This paper conducts an exhaustive examination, encompassing both coding performance and time complexity, of various WDR methods vis-à-vis the conventional image compression algorithm SPIHT. Furthermore, it delves into the performance assessment of diverse coding techniques in the realm of encoding arbitrary-shaped objects. The analysis underscores that modified WDR variants demonstrate remarkable prowess in compression, rendering them invaluable for rapid transmission in bandwidth-constrained networks. To substantiate these findings, coding results (measured in terms of PSNR) are derived from the application of these algorithms to standard test images, MRI images, and video still images. The results reveal coding gains ranging from 0.5 dB to 1 dB for regular resolution images and a substantial 2 dB to 12 dB for scalable resolution scenarios, in comparison to traditional coding approaches. Consequently, this analysis underscores the convenience and superiority of modified WDR methods, not only for still images but also for encoding and transmitting arbitrary-shaped objects.

Subject Review: Various Techniques for Image Compression

Due to the quick development of multimedia technology, now can see the increasing amount of multimedia data that is being generated or transmitted over the internet. digital image is one form of multimedia data, that can take different formats and different sizes. And here comes the main objective of image compression in transmitting or storing the data in an effective manner by decreasing the redundant or irrelevant information without significant loss of visual quality in image data. Compression of the images is utilized in various services, including TV broadcasts, satellite imagery, martial communications, webinars, medical imaging, weather reporting, etc. This paper provides a survey of the main techniques in image compression, covering both lossy & lossless approaches. The techniques that depend on lossy compression lose some of the image details during compression, while lossless compression techniques keep the image information without losing. Vector and scalar Quantization, transform coding, block truncation Coding, etc. are all examples of lossy approaches. Run length coding, entropy encoding, statistical coding, etc. are all examples of lossless techniques. This paper will assist the researchers in learning about these techniques and choosing appropriate techniques for their work.

Comprehensive Dataset Building and Recognition of Isolated Handwritten Kannada Characters Using Machine Learning Models

In this work, an attempt is made to build a dataset for handwritten Kannada characters and also to recognize the isolated Kannada vowels, consonants, modifiers, and ottaksharas. The dataset is collected from 500 writers of varying ages, gender, qualification, and profession. This dataset will be used to recognize the handwritten kagunitas, ottaksharas, and other base characters, where the existing works have addressed very less on the recognition of kagunitas and ottaksharas. There are no datasets for the same. Hence, a dataset for handwritten 85 characters is built using an unsupervised machine learning technique, that is, K-means hierarchical clustering with run length count features. An accuracy of 80% was achieved with the unsupervised method. The dataset consists of 130,981 samples for 85 classes; these classes are further divided into upper, lower, and middle zones based on the position of the character in the dialect. After the dataset was built, support vector machine model with histogram of oriented gradients features was used for recognition and an accuracy of 99.0%, 88.6%, and 92.2% was obtained for the upper, middle, and lower zones, respectively, to increase the recognition rate, the convolutional neural network model is fine-tuned with raw input, and an accuracy of 100%, 96.15%, and 95.38% was obtained for the upper, middle, and lower zones, respectively. With the ResNet18 model, an accuracy of 99.88%, 98.92, and 97.55% was obtained for each of the zones, respectively. The dataset will be made available online for the researchers to carry out their research on handwritten characters, kagunitas, and word recognition with segmentation.

Retraction Note: Augmented Recurrence Hopping Based Run-Length Coding for Test Data Compression Applications

Retraction Note: Augmented Recurrence Hopping Based Run-Length Coding for Test Data Compression Applications

Improving Healthcare Applications Security Using Blockchain

Nowadays, the Internet of Medical Things (IoMT) technology is growing and leading the revolution in the global healthcare field. Exchanged information through IoMT permits attackers to hack or modify the patient’s data. Hence, it is of critical importance to ensure the security and privacy of this information. The standard privacy techniques are not secured enough, so this paper introduces blockchain technology that is used for securing data. Blockchain is used with the smart contract to secure private patient records. This paper presents how a patient may send his vital signs to the physician through the Internet without meeting with the latter in person. These vital signs are collected from the IoMT system that we developed before. In the proposed method, each medical record is stored in the block and connected to the previous block by a hashing function. In order to secure the new block, the SHA256 algorithm is used. We modified the SHA256 algorithm by using run-length code in compressing data. If any hacker attempts to attack any medical record, he must change all previous blocks. In order to preserve the rights of the doctor and patient, a smart contract is built into the blockchain system. When the transaction begins, the smart contract withdraws the money from the patient’s wallet and stores it in the smart contract. When the physician sends the treatment to the patient, the smart contract transfers the money to the physician. This paper shows that all recent work implements Blockchain 2 into the security system. This paper also shows that our security system can create a new block with O (n + d) time complexity. As a result, our system can create one hundred blocks in two minutes. Additionally, our system can deposit the money from the patient’s wallet into the physician’s wallet promptly. This paper also shows that our method performs better than all subsequent versions of the original blockchain.

Speech Coding Using Discrete Cosine Transform and Chaotic Map

Recently, data of multimedia performs an exponentially blowing tendency, saturating daily life of humans. Various modalities of data, includes images, texts and video, plays important role in different aspects and has wide. However, the key problem of utilizing data of large scale is cost of processing and massive storage. Therefore, for efficient communications and for economical storage requires effective techniques of data compression to reduce the volume of data. Speech coding is a main problem in the area of digital speech processing. The process of converting the voice signals into a more compressed form is speech coding. In this work, we demonstrate that a DCT with a chaotic system combined with run-length coding can be utilized to implement speech coding of very low bit-rate with high reconstruction quality. Experimental result show that compression ratio is about 13% when implemented on Librispeech dataset.

A VLSI-Based Hybrid ECG Compression Scheme for Wearable Sensor Node

During smart long-term monitoring of any biomedical signal in wireless body area networks, wearable sensor nodes generate and transmit a large amount of data, increasing transmission power consumption. In order to reduce data storage and power consumption, a lossless data compression technique for an electrocardiogram signal monitoring system is presented in this letter. For this, a hybrid lossless compression algorithm based on Run-length coding and Golomb&#x2013;Rice coding is proposed to enhance the bit compressing rate. The lossless encoding scheme is implemented on the MIT-BIH arrhythmia database, achieving a compression ratio of 2.91. A VLSI-based architecture of the data compression algorithm is implemented in 90nm CMOS technology that consumes power of 18.78 <inline-formula><tex-math notation="LaTeX">$\mu \text{W}$</tex-math></inline-formula> at 100 MHz operating frequency and 1.2 V supply voltage, occupying an area of 0.0051 <inline-formula><tex-math notation="LaTeX">$\text{mm}^{2}$</tex-math></inline-formula>.

PASTE: FPGA-based Portable At-speed Scan Test Equipment

The increasing integration and complexity of chips driven by Moore’s Law has placed higher demands on chip verification and testing efforts. Automated Test Equipments (ATE) in conjunction with design for testability can effectively screen out faulty chips, but ATE is too costly and can be functionally redundant to be practical for small and medium-sized chip design companies and scientific research in universities. Therefore, in recent years, there has been a lot of research work on FPGA-based low-cost test equipment, but few studies have used the scan test method and have not been able to realize the at-speed test of the chip. In this paper, we propose a portable, low-cost and configurable FPGA-based chip scan test method, which can realize both normal and at-speed test, and can effectively filter the stuck-at faults and transition faults of the chip. We also optimize the test code size via run-length code compression algorithm and achieve a 68.16% compression rate for the test code, which effectively reduces the overall data storage and transmission overhead.

Adaptive Multicale Transformation Run-Length Code-Based Test Data Compression in Benchmark Circuits

Test data volume reduction and power consumption during testing time outlines are two main problems for Very Large Scale Integration (VLSI) gadgets. Most the code-based arrangements have been utilized to diminish test data volume, although the most notable way that test data volume is high. The switching action that happens between the test carriers leads would expand power consumption. This work presents a compression/decompression methodology for limiting the amount of test data that should be kept on a tester and conveyed to each center in a System on a Chip (SOC) during a test utilizing the Adaptive Multiscale Transformation Run Length Code (AMT-RLC). The data transmission capacity between the tester and the SOC is a block when testing modern SOCs with a few centers to check how long the test can run. The proposed work keeps the tester in the SOC and the test vectors in a compacted memory structure. A modest quantity of On-chip equipment is needed to release different test vectors. Due to its variouscharacteristics that decrease the test data volume, the Adaptive Multiscale Transformation Run Length Code is picked for a center test vector framework. These attributes ensure that the code words overhead is decoded (and applied to the center's output exhibit grouping commitment) utilizing an exact cell cluster that occupies less space. The suggested strategy may provide test data compression with much-reduced Area overhead for the decoder and low power consumption, according to the results. The abstract shall be running continuously (not structured) and shall not include reference citations. Abbreviations should be defined in full the first time they appear. Abbreviations could be then used, quoted in-between parentheses.

Image Compression Software Design for Zhurong Mars Exploration Rover

Because of the limited number of information transmission channels and high bit error rate in Mars image transmission, a method of image compression software design and realization of Mars rover was proposed. The characteristics of rover image data management were analyzed, and the three capabilities that the rover image compression software needed to have were concluded: uniform file management capability, high-efficiency compression capability, and high fault tolerance capability. For uniform file management capability, a file system for uniform management of multiple types of image data was proposed. To meet the high efficiency and high fault tolerance requirements of the compression algorithm, based on run-length coding algorithm of first-1 bit, the three important processes of multi-load image unification processing, color image preprocessing, and segmented bit-plane image coding were expanded, to improve compression performance and fault tolerance. Then a complete set of image compression software design schemes was formed, using FPGA+DSP, and the complete set of software was realized. Finally a simulation verification system was used to verify the compression effect. Verification results show the effectiveness of the algorithm. This software has been verified by China’s Zhurong Mars rover and can effectively meet the needs of Mars exploration missions. The software design can be used as a reference for image compression in subsequent ultra-long-distance deep space explorations.

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 reversible information hiding algorithm in AMBTC domain based on human vision system

Compression is a common processing method of digital image, and the compression of carrier image usually affects the performance of hidden information. Therefore, it is necessary to research on the preprocessing method to improve some performance of hidden information in the compressed domain. Aiming at the problem of tampering attack on compressed domain image and the security of secret information preprocessing in the previous information hiding algorithms, we propose a reversible information hiding algorithm in AMBTC domain based on human vision system. Firstly, RGB color image is converted to YCbCr image, AMBTC coding is used to generate high and low average value sequence in brightness component. Secondly, the secret information is compressed by run length coding and scrambled. Thirdly, the histogram shift technique is used to embed the information into the mean sequence and generate the reserved information at the same time. Fourth, the color component is divided into normal block and absolute smooth fast, the specific position of the bit plane of the normal block is deleted, and the reserved information is embedded. The experimental results show that, compared with other algorithms, the proposed algorithm has better invisibility and robustness, and after a certain attack, the extracted secret information still has high recognizability.

On a hybrid lossless compression technique for three-dimensional medical images.

In the last two decades, incredible progress in various medical imaging modalities and sensing techniques have been made, leading to the proliferation of three‐dimensional (3D) imagery. Byproduct of such great progress is the production of huge volume of medical images and this big data place a burden on automatic image processing methods for diagnostic assistance processes. Moreover, large amount of medical imaging data needs to be transmitted with no loss of information for the purpose of telemedicine, remote diagnosis etc. In this work, we consider a hybrid lossless compression technique with object‐based features for three‐dimensional (3D) medical images. Our approach utilizes two phases as follows: first we determine the volume of interest (VOI) for a given 3D medical imagery using selective bounding volume (SBV) method, and second the obtained VOI is encoded using a hybrid lossless algorithm using Lembel‐Ziv‐Welch Coding (LZW) followed by arithmetic coding (L to A). Experimental results show that our proposed 3D medical image compression method is comparable with other existing standard lossless encoding methods such as Huffman Coding, Run Length Coding, LZW, and Arithmetic Coding and obtains superior results overall.

Аdaptive combined image coding with prediction of arithmetic code volume

The problem of increasing the efficiency of coding of halftone images in the space of bit planes of differences in pixel values obtained using differential coding (DPCM – Differential pulse-code modulation) is considered. For a compact representation of DPCM pixel values, it is proposed to use a combined compression encoder that implements arithmetic coding and run-length coding. An arithmetic encoder provides high compression ratios, but has high computational complexity and significant encoding overhead. This makes it effective primarily for compressing the mean-value bit-planes of DPCM pixel values. Run-length coding is extremely simple and outperforms arithmetic coding in compressing long sequences of repetitive symbols that often occur in the upper bit planes of DPCM pixel values. For DPCM bit planes of pixel values of any image, a combination of simple run length coders and complex arithmetic coders can be selected that provides the maximum compression ratio for each bit plane and all planes in general with the least computational complexity. As a result, each image has its own effective combined encoder structure, which depends on the distribution of bits in the bit planes of the DPCM pixel values. To adapt the structure of the combined encoder to the distribution of bits in the bit planes of DPCM pixel values, the article proposes to use prediction of the volume of arithmetic code based on entropy and comparison of the obtained predicted value with the volume of run length code. The entropy is calculated based on the values of the number of repetitions of ones and zero symbols, which are obtained as intermediate results of the run length encoding. This does not require additional computational costs. It was found that in comparison with the adaptation of the combined encoder structure using direct determination of the arithmetic code volume of each bit plane of DPCM pixel values, the proposed encoder structure provides a significant reduction in computational complexity while maintaining high image compression ratios.

Tunable Q-wavelet based ECG data compression with validation using cardiac arrhythmia patterns

The acquisition of longer electrocardiogram (ECG) signals is an essential step of the recent medical screening and diagnostic procedures. Considering the need of an efficient ECG data management system, this paper presents a novel ECG data compression technique based on the tunable Q-wavelet transform which provides adjustable parameters to achieve good compression performance. The tunable Q-wavelet transform compacts the maximum energy of the signal to fewer transform coefficients. Dead-zone quantization is used to discard the small valued transform coefficients. Further, transform coefficients are rounded-off to nearest integer values and encoded using run-length coding. ECG records of the MIT-BIH arrhythmia database are used to evaluate the performance of the proposed compression technique. The average compression performance obtained in terms of CR, PRD, PRD1, QS, QS1 and SNR are 20.61, 4.43, 6.37, 5.88, 3.46, and 55.93 dB respectively. The proposed technique offers better compression performance than many existing techniques. The proposed technique is also validated using cardiac arrhythmia patterns classification. In the validation phase, tunable Q-wavelet transform based features of 14,878 original cardiac patterns are used to train the support vector machine classifier while testing is performed using features of 26,219 original and reconstructed cardiac patterns separately. It examines the effect of the proposed compression technique on cardiac arrhythmia classification. In the validation part, the overall classification accuracy, sensitivity, and specificity obtained for the proposed compression technique are 98.35%, 95.77%, and 99.19% respectively. It denotes that the proposed technique compresses ECG signal efficiently with preserving diagnostic information very well.

Improvement of data compression technology for power dispatching based on run length encoding

Abstract Data compression is of great significance to the storage of power dispatch data. The compressed data not only saves a lot of space, but also improves the speed of data retrieval. Therefore, this paper proposes an improved algorithm based on run length encoding to compress power dispatch data. In the case of high data similarity, run length coding and dictionary coding have good compression effects, so this article classifies power dispatch data, and uses an improved run length coding algorithm to compress data with high similarity. For similarity Low data is compressed using the Lz4 algorithm that comes with HBase, and a Base-128 Varints encoding the number of occurrences of elements in the run-length encoding is proposed to further improve the compression rate of similar data. This improved algorithm is proven in experiments In some scenarios, the compression rate is indeed improved.

Intelligent Power Grid Video Surveillance Technology Based on Efficient Compression Algorithm Using Robust Particle Swarm Optimization

Companies that produce energy transmit it to any or all households via a power grid, which is a regulated power transmission hub that acts as a middleman. When a power grid fails, the whole area it serves is blacked out. To ensure smooth and effective functioning, a power grid monitoring system is required. Computer vision is among the most commonly utilized and active research applications in the world of video surveillance. Though a lot has been accomplished in the field of power grid surveillance, a more effective compression method is still required for large quantities of grid surveillance video data to be archived compactly and sent efficiently. Video compression has become increasingly essential with the advent of contemporary video processing algorithms. An algorithm’s efficacy in a power grid monitoring system depends on the rate at which video data is sent. A novel compression technique for video inputs from power grid monitoring equipment is described in this study. Due to a lack of redundancy in visual input, traditional techniques are unable to fulfill the current demand standards for modern technology. As a result, the volume of data that needs to be saved and handled in live time grows. Encoding frames and decreasing duplication in surveillance video using texture information similarity, the proposed technique overcomes the aforementioned problems by Robust Particle Swarm Optimization (RPSO) based run-length coding approach. Our solution surpasses other current and relevant existing algorithms based on experimental findings and assessments of different surveillance video sequences utilizing varied parameters. A massive collection of surveillance films was compressed at a 50% higher rate using the suggested approach than with existing methods.

Quantized ternary pattern and singular value decomposition for the efficient mining of sequences in SRSI images

The growth and development of particular region over time can be witnessed by remote sensing images. Although such raw images have less possibility to derive the insights, Serial Remote Sensing Images (SRSI) has the large potential to discover the patterns. The evolution of spatial patterns in various areas including urban development, expansion of vegetation cover and agriculture is the evidence for the utilization of SRSI accumulation. The application of conventional sequential pattern-mining algorithms on the SRSI images results in high computational complexity. This issue can be resolved by grouping the pixels and mining sequence patterns. A one-pass framework is introduced to compress and hide the data in the marked stream without any loss. In this paper, we proposed a Quantized ternary pattern based pixel grouping and Singular Value Decomposition—Run Length Coding based pattern mining. The algorithms are experimented using a dataset, namely, the Cropland data layer dataset. The proposed algorithm is efficient in terms of mining time and sequence pattern generation.

Error-control algorithm of voice messages transmission

High-speed and high-quality communications are required to ensure a smooth process of timely receipt and processing of information. The speed of obtaining information depends on the speed of response and timely management decisions. The development of information and telecommunication technologies has led to an increasing integration of communication and voice messaging systems implementing information processes in the control system. In communication systems where it is important to ensure the integrity, availability and confidentiality of transmitted information when low-bandwidth communication channels are used, voice information is encoded by special compression algorithms. Prefix codes are common in various fields of information technology. Many information compression algorithms are based on them, and they are used by different protocols. The use of run-length coding will reduce the speed of a transmitted signal and increase the throughput.

Hybrid encoding for test data compression

Main disputes of digital integrated circuits testing are increasing test data volume and test power. The proposed encoding schemes are a combination of nine coded and selective pattern compression, Alternate Variable Run length code to reduce test data volume. The test cubes are divided into multiples of 8, 16, 32, and 64 blocks to upsurge the relationship among the successive test patterns which offers enriched test data reduction. The test data blocks are encoded with two methods in order to reduce test data volume. In the first method, the test sets are encoded using nine coded with selective pattern coding to expand the test data density. In the second method, the test sets are encoded using nine coded with Alternate variable run length laterally with selective pattern coding to improve the test data compression. Investigational results show that the proposed first and second approaches offer a maximum of 76% and 83% of compression ratio respectively for ISCAS’89 benchmark circuits.