Huffman Coding Research Articles

Comparative Analysis of Huffman Coding Implementations for Efficient Data Communication Using Greedy and Divide-and-Conquer Techniques

Efficient data compression techniques are required to minimize storage and processing overhead due to modern systems' growing amount of data. Huffman Coding is a lossless compression technique that maintains data integrity by assigning shorter bit codes to characters appearing frequently, reducing size. Our analysis focuses on two implementation methodologies: greedy technique and divide and conquer. To find efficient solutions, divide-and-conquer algorithms partition problems into smaller components. In contrast, greedy algorithms strive to attain the utmost attainable result at each level. Our extensive investigation centers on the timing and space intricacies of diverse methodologies, enabling a comparative analysis that underscores their respective merits and drawbacks.

Lossless Recompression of Vector Quantization Index Table for Texture Images Based on Adaptive Huffman Coding Through Multi-Type Processing

With the development of the information age, all walks of life are inseparable from the internet. Every day, huge amounts of data are transmitted and stored on the internet. Therefore, to improve transmission efficiency and reduce storage occupancy, compression technology is becoming increasingly important. Based on different application scenarios, it is divided into lossless data compression and lossy data compression, which allows a certain degree of compression. Vector quantization (VQ) is a widely used lossy compression technology. Building upon VQ compression technology, we propose a lossless compression scheme for the VQ index table. In other words, our work aims to recompress VQ compression technology and restore it to the VQ compression carrier without loss. It is worth noting that our method specifically targets texture images. By leveraging the spatial symmetry inherent in these images, our approach generates high-frequency symbols through difference calculations, which facilitates the use of adaptive Huffman coding for efficient compression. Experimental results show that our scheme has better compression performance than other schemes.

Integration of BWT scrambling and data compression in an innovative system enhances protection and versatile management of sensor feeds (SEC)

The proposed Sensor Data Encryption with Compression (SEC) system is presented as an innovative solution for sensor data processing, aiming to achieve optimal efficiency while improving security and adaptability by integrating robust cryptography with advanced compression techniques. The system's objective is to create a reliable framework for managing sensitive sensor data by effectively tackling challenges in data processing through a balanced combination of strong cryptography and sophisticated compression methods. The SEC system utilizes advanced encryption and compression techniques, including the scrambling of Burrows-Wheeler Transform (BWT), Move-to-Front (MTF) encoding, Run-Length Encoding (RLE), and Huffman coding, to attain exceptional compression efficiency while maintaining data integrity. The practical implementation of the SEC system shows an impressive 85% enhancement in data compression efficiency, as evidenced by reduced Bits per Character (BPC) across various sensor data inputs, along with improved resistance to cryptanalysis due to an increased unicity distance. Consequently, the SEC system demonstrates itself to be a robust and efficient solution, achieving superior data compression and enhanced cryptographic resilience, thereby addressing critical challenges in sensor data processing.

Improvement of Lossless Text Compression Methods using a Hybrid Method by the Integration of RLE, LZW and Huffman Coding Algorithms

In the field of computer science, data compression is essential in the process of data transfer because it reduces file size without losing information. Issues that depend on the characteristics of the text can greatly affect the effectiveness of any compression algorithm. Consequently, each traditional compression algorithm has its own strengths and limitations. Therefore, a highly efficient compression method that achieves higher compression ratio is needed. In this paper, based on the sequential implementation of Huffman, RLE and LZW algorithms, a hybrid data compression method has been proposed. The algorithms have been individually evaluated and compared in terms of compression ratio and compressed file size. We conclude that in terms of compression ratio and compressed file size, LZW is better than Huffman and RLE. The results show that the proposed hybrid algorithm achieves the highest average compression ratio is 2.42 and the lowest average compressed file size is 1138.5 or less compared to the individual implementations of the algorithms. Thus, it can be concluded that the proposed hybrid method enhances lossless text compression in terms of compression ratio and file size according to compression metrics.

Ncorpi[formula omitted]N : A [formula omitted] software for N-body integration in collisional and fragmenting systems

NcorpiON is a general purpose N-body software initially developed for the time-efficient integration of collisional and fragmenting systems of planetesimals or moonlets orbiting a central mass. It features a fragmentation model, based on crater scaling and ejecta models, able to realistically simulate a violent impact.The user of NcorpiON can choose between four different built-in modules to compute self-gravity and detect collisions. One of these makes use of a mesh-based algorithm to treat mutual interactions in O(N) time. Another module, much more efficient than the standard Barnes–Hut tree code, is a O(N) tree-based algorithm called FalcON. It relies on fast multipole expansion for gravity computation and we adapted it to collision detection as well. Computational time is reduced by building the tree structure using a three-dimensional Hilbert curve. For the same precision in mutual gravity computation, NcorpiON is found to be up to 25 times faster than the famous software REBOUND.NcorpiON is written entirely in the C language and only needs a C compiler to run. A python add-on, that requires only basic python libraries, produces animations of the simulations from the output files. NcorpiON can communicate with REBOUND’s webGL viewer via MPI for 3D visualization. The name NcorpiON, reminding of a scorpion, comes from the French N-corps, meaning N-body, and from the mathematical notation O(N), due to the running time of the software being almost linear in the total number N of bodies. NcorpiON detects collisions and computes mutual gravity faster than REBOUND, and unlike other N-body integrators, it can resolve a collision by fragmentation. The fast multipole expansions are implemented up to order eight to allow for a high precision in mutual gravity computation.

A Comparative Study of Text-Based Lossless Compression

Lossless data compression is a critical technique used to reduce file sizes without any loss of information during the encoding and decoding processes. This study presents a comparative analysis of two widely-used lossless compression algorithms: Huffman Encoding and Lempel-Ziv-Welch (LZW). The primary objective is to evaluate the performance of these algorithms in terms of compression ratio, compression time, decompression time, and space savings. The analysis was conducted on 100 files of varying sizes. The results demonstrate that the LZW algorithm outperforms Huffman Encoding, offering superior compression ratios, faster compression and decompression times, and greater disk space savings. These findings highlight the effectiveness of LZW for efficient data compression in practical applications.

DermCompressNet: integrated CD-ConvNet and discrete cosine transform for dermoscopic images compression

AbstractTelemedicine has a critical role in healthcare by supporting the information exchange between the patients and the physicians as well as between the physicians for consultation. Such technology has urgent requirements for storage reduction and efficient use of the transmission channel bandwidth. Such requirements can be achieved efficiently via developing accurate medical image compression techniques. For accurate diagnosis, lossless compression methods are recommended. However, the tradeoff between the compression ratio (CR) and the preservation of the image quality is still challenging. On the other hand, the advantages of the convolutional neural networks inspired this work to design a novel proposed system for dermoscopic image compression based on the integration of the compression direction-ConvNet (CD-ConvNet) and decompression direction-ConvNet (DD-ConvNet) with discrete cosine transform (DCT) and Huffman coding, called DermCompressNet. To reconstruct a high-quality image at the receiver, the inverse processes using the DD-ConvNet network were followed. The proposed system was evaluated by measuring several image quality metrics, namely the mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM), along with compression quality metrics, namely CR, and computational time (CT). The experimental results achieved 34.6 dB, 2.5, 0.85, and 56% of PSNR, MSE, SSIM, and CR, respectively. A comparison studies with the JPEG and state-of-the-art methods proved the superiority of the proposed system, showing23%, 16%, 4.3%, and 1.8% improvements in the PSNR, MSE, SSIM, and CR, respectively, compared to the JPEG.

Synthesis and analysis of an ensemble of SAR signals

Due to the development of digital technologies for receiving and transmitting radio location signals, the development of modern radars with a synthesized aperture has made it possible to widely use an ensemble of probing signals with periodic sequences of identical pulses. The manipulation of probing pulses in transmission opens up opportunities to reduce the level of interference of ambiguity in range, expand the capture bands, improve azimuth resolution, and reduce interference from the background surrounding the target. However, the literature currently does not sufficiently address the issues of synthesis and analysis of ensembles of probing signals for a synthetic aperture radar (RSA) placed on board an unmanned aerial vehicle. Several variants of the implementation of ensembles of probing signals in the RSA are considered, mathematical modeling and experimental testing are carried out on the basis of the RSA operating in the VHF frequency range, placed on board an unmanned aerial vehicle (UAV). The conducted modeling allows us to draw the following conclusions. The best characteristics are possessed by an ensemble of signals encoded by Huffman codes, where M-sequences were used to "scatter" the roots. In second place is an ensemble of signals encoded by composite M-sequences. However, with an increase in the base of probing signals (N>64), the best results are shown by an ensemble of signals based on composite M-sequences.

Research on image compression technology based on improved SPIHT compression algorithm for power grid data

Abstract At present, the amount of information on power grid operation and maintenance monitoring image data is increasing, and the requirements for data compression are higher and higher. Based on the improved SPIHT image compression algorithm, this study presents the research of power grid data compression. First, the basic theory of image compression, and the principle of one-dimensional wavelet transform and two-dimensional wavelet transform are introduced. The development process, characteristics, and advantages of image coding are discussed. Then, the shortcomings of the SPIHT algorithm are analyzed, and the SPIHT coding is improved by parallel computation. The parallel wavelet transform algorithm based on the block idea and the parallel SPIHT coding algorithm based on the code tree are proposed in the data parallelism of the compression algorithm. At the same time, the data dependence between tasks in the process of SPIHT image compression coding is analyzed, and the task parallelism in the compression algorithm is realized by using the relative independence of tasks in different threshold coding. Finally, the application and simulation analysis of power grid data based on the SPIHT compression algorithm, the construction of power grid data model simulation, and the composition of two-dimensional power grid data images are carried out. Secondly, the obtained 2D power grid data image is compressed by the SPIHT algorithm and improved SPIHT algorithm, respectively, and the compression effect of the two algorithms on the power grid data image is compared. When the bit rate is 0.5, the compression effect of the improved SPIHT algorithm is 13.6506. When the bit rate is 1, the compression effect of the improved SPIHT algorithm is 18.9287. The results show that the improved SPIHT algorithm can compress the grid data to obtain better grid image quality.

A Fast and robustColor Image Encryption Scheme by Huffman Compression, 5D Chaotic Map and DNA Encoding

In the era of information technology, securing the real-time digital image is the greatest challenge especially colour image. In this paper, a colour image encryption scheme is introduced to secure the image based on compression-then-encryption concept. Theproposed method is a hybrid combination of the Chaos techniques for key generation, Huffman Encoding/compression for compression and avoiding colour decomposition, scrambling for more confusion and DNA Encoding for reducing storage size. In order to enhance security, scrambled data are converted to the Deoxyribonucleic acid (DNA) sequence, make ADD operation and apply the complementary rules to attain the cipher image. Experimental results have been proved as robustness, accurate and high security against attacks, malicious attacks, differential attacks and statistical attacks. Furthermore, results show that the time speed is faster minimum storage space than the existing colour image encryption scheme.

Reversible data hiding in encrypted image based on key-controlled balanced Huffman coding

To achieve privacy protection and effective management in cloud computing, and solve the problem of existing reversible data hiding in encrypted image (RDH-EI) algorithms being unable to resist existing various attacks, an RDH-EI algorithm based on key-controlled balanced Huffman coding (KBHC) is proposed. The novelty lies in KBHC and variable-length bit scrambling. KBHC possesses non-preset, balanced, and key-controlled characteristics, providing the proposed algorithm with high capacity and enhanced security. The non-preset allows coding tables to be adaptively generated based on prediction error maps, resulting in shorter encoded streams for higher embedding capacity. The balanced characteristic is achieved by adjusting the subtrees, so that the balance rate in the encoded stream is 0.014, and can also reach 0.065 for particularly smooth images, achieving uniform distribution of the encoded stream, thereby improving the ability to resist statistical analysis attacks. The random key controls the leaf nodes scrambling in the Huffman tree, which realizes the variability of the encoded stream and avoids the potential security risks caused by timestamp reconstruction, laying the foundation to achieve differential attack security. Variable-length bit scrambling determines the pseudo-random extension length and scrambling sequence by both the encryption key and coding table information, effectively resists brute force attacks and ensures up to 100 % difference rate between scrambling sequences generated in each run. Experimental results demonstrate that compared to several RDH-EI methods, the proposed algorithm achieves higher embedding capacity and security under acceptable complexity. The average embedding rate of three databases reaches 3.897 bpp, and the proposed algorithm effectively resists statistical analysis attacks, COA, KPA, and differential attack.

Social Network Forensics Analysis Model Based on Network Representation Learning.

The rapid evolution of computer technology and social networks has led to massive data generation through interpersonal communications, necessitating improved methods for information mining and relational analysis in areas such as criminal activity. This paper introduces a Social Network Forensic Analysis model that employs network representation learning to identify and analyze key figures within criminal networks, including leadership structures. The model incorporates traditional web forensics and community algorithms, utilizing concepts such as centrality and similarity measures and integrating the Deepwalk, Line, and Node2vec algorithms to map criminal networks into vector spaces. This maintains node features and structural information that are crucial for the relational analysis. The model refines node relationships through modified random walk sampling, using BFS and DFS, and employs a Continuous Bag-of-Words with Hierarchical Softmax for node vectorization, optimizing the value distribution via the Huffman tree. Hierarchical clustering and distance measures (cosine and Euclidean) were used to identify the key nodes and establish a hierarchy of influence. The findings demonstrate the effectiveness of the model in accurately vectorizing nodes, enhancing inter-node relationship precision, and optimizing clustering, thereby advancing the tools for combating complex criminal networks.

MSFuzz: Augmenting Protocol Fuzzing with Message Syntax Comprehension via Large Language Models

Network protocol implementations, as integral components of information communication, are critically important for security. Due to its efficiency and automation, fuzzing has become a popular method for protocol security detection. However, the existing protocol-fuzzing techniques face the critical problem of generating high-quality inputs. To address the problem, in this paper, we propose MSFuzz, which is a protocol-fuzzing method with message syntax comprehension. The core observation of MSFuzz is that the source code of protocol implementations contains detailed and comprehensive knowledge of the message syntax. Specifically, we leveraged the code-understanding capabilities of large language models to extract the message syntax from the source code and construct message syntax trees. Then, using these syntax trees, we expanded the initial seed corpus and designed a novel syntax-aware mutation strategy to guide the fuzzing. To evaluate the performance of MSFuzz, we compared it with the state-of-the-art (SOTA) protocol fuzzers, namely, AFLNET and CHATAFL. Experimental results showed that compared with AFLNET and CHATAFL, MSFuzz achieved average improvements of 22.53% and 10.04% in the number of states, 60.62% and 19.52% improvements in the number of state transitions, and 29.30% and 23.13% improvements in branch coverage. Additionally, MSFuzz discovered more vulnerabilities than the SOTA fuzzers.

Exploring Team Collaboration in the New Metaverse (The 3D-AI Internet)

This study explores the anticipated changes in team collaboration driven by the rise of AI-reliant businesses in the metaverse. The research is motivated by the rapid technological advancements and increasing interest from major tech companies in creating shared virtual environments for distributed teams. The main objective is to investigate how team collaboration will evolve in this new context, providing insights for future metaverse team members and leaders. The qualitative study employs semi-structured, in-depth interviews in January-February 2022 with team members from the virtual world of Second Life, focusing on payment satisfaction, globalized team culture, training and learning, and fictional identities. This research period is contemporary, reflecting current trends and capabilities. The primary methods include thematic analysis of interview data to identify emerging patterns. Data is analyzed using “Dedoose”, a web application for mixed methods research, and collected in several codes and subcodes that will eventually form a “code tree”. Descriptive validity, interpretative validity, theoretical validity and generalizable validity were confirmed. Key findings reveal that metaverse-native teams need innovative payment methods and a focus on authentic self-expression. A globalized team culture will require balancing work and social life in virtual environments, with Corporate Social Responsibility being vital to attracting Generation Alpha. Payment satisfaction will shift towards fulfilment and unique experiences rather than just financial security, and businesses should explore alternative payment methods beyond cryptocurrency. Fictional identities will necessitate building metaverse-specific reputations for professionalism and trust. Training and learning will connect virtual and real-life scenarios, enhancing skills and quality. The study confirms that traditional collaboration models may only partially apply in augmented virtuality. These results have practical significance for businesses and professionals preparing to operate in the metaverse, offering a foundation for future research and development in virtual team collaboration.

A novel compression methodology for medical images using deep learning for high-speed transmission

Medical imaging is a rapidly growing field having a high impact on the early detection, diagnosis and surgical planning of diseases. Several imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound (US) imaging generate a higher volume of data, necessitating additional storage and communication requirements. Hence, image compression is utilized in medical field to reduce redundancy and alleviate memory and bandwidth issues. This paper presents a novel deep learning-based compression method to reduce the size of medical images. This method employs a deep convolutional neural network for learning compact representations of medical images, then coded by a Huffman encoder. The compression process is reversed to reconstruct the original image. Several tests are conducted to compare the results with other wellknown compression methods. The proposed model achieved a mean peak signal-to-noise ratio (PSNR) of 42.82 dB with storage space saving (SSS) of 96.15% for CT, 43.88 dB with SSS of 96.25% for MRI, 46.29 dB with SSS of 96.07% for US and 43.51 dB with SSS of 96.95% for X-ray images. The findings showed that the proposed compression technique could greatly compress the image size, saving storage space, facilitating better transmission and preserving critical diagnostic information.

Enhanced Compression Medical Image Speed for Computed Radiography

In recent years, there has been a significant surge in the volume of medical imaging data. This surge poses challenges for the functioning of PACS communication systems and image archiving. The most effective solution to address this issue involves compressing images through digital encryption, which optimally utilizes storage space. This process involves reformatting the imaging data by reducing redundancy, leading to image compression. While this reduction in redundancy is readily apparent in individual images, there is a vulnerability in these methods that tends to overlook a source of repetition present in similar stored images. To emphasize this common occurrence, we introduce the term "redundancy group." Similar images are frequently encountered within medical image databases, resulting in a considerable redundancy reduction. In this paper, our focus is on enhancing the control of redundancy extraction in the data used, specifically medical images. To enhance the compression efficiency of standard image compression, we employ improved methods, namely MinMax Predictive (MMP) and Min-Max Differential (MMD). Our experiments demonstrate that these methods lead to a substantial enhancement in brain CT compression, with potential improvements of up to 130% when using Huffman coding. Similar improvements are observed in arithmetic coding, with a 94% improvement compared to the number-arithmetic code and a 37% improvement compared to the Lempel-Ziv compression. These improvements occur when combining the MMP technology with the MMD technology, utilizing inverse operations that result in lossless compression.

Improving the performance of 3D image model compression based on optimized DEFLATE algorithm

This study focuses on optimizing and designing the Delayed-Fix-Later Awaiting Transmission Encoding (DEFLATE) algorithm to enhance its compression performance and reduce the compression time for models, specifically in the context of compressing NX three-dimensional (3D) image models. The DEFLATE algorithm, a dual-compression technique combining the LZ77 algorithm and Huffman coding, is widely employed for compressing multimedia data and 3D models. Three 3D models of varying sizes are selected as subjects for experimentation. The Wavelet algorithm, C-Bone algorithm, and DEFLATE algorithm are utilized for compression, with subsequent analysis of the compression ratio and compression time. The experimental findings demonstrate the DEFLATE algorithm’s exceptional performance in compressing 3D image models. Notably, when compressing small and medium-sized 3D models, the DEFLATE algorithm exhibits significantly higher compression ratios compared to the Wavelet and C-Bone algorithms while also achieving shorter compression times. Compared to the Wavelet algorithm, the DEFLATE algorithm enhances the compression performance of 3D image models by 15% and boosts data throughput by 49%. While the compression ratio of the DEFLATE algorithm for large 3D models is comparable to that of the Wavelet and C-Bone algorithms, it notably reduces the actual compression time. Furthermore, the DEFLATE algorithm enhances data transmission reliability in NX 3D image model compression by 12.1% compared to the Wavelet algorithm. Therefore, the following conclusions are drawn: the DEFLATE algorithm serves as an excellent compression algorithm for 3D image models. It showcases significant advantages in compressing small and medium-sized models while remaining highly practical for compressing large 3D models. This study offers valuable insights for enhancing and optimizing the DEFLATE algorithm, and it serves as a valuable reference for future research on 3D image model compression.

Development of an algorithm for compressing aerospace images for the subsequent recognition and identification of various objects

The object of study is the recognition and identification of various objects in aerospace images. To solve the problems of compressing hyperspectral aerospace images with losses, the development of a compression algorithm is proposed. As a result, an algorithm has been developed for compressing aerospace images for subsequent recognition and identification of various objects using wavelet transform for processing high- and medium-resolution space images when monitoring from remote sensing satellites, based on the use of structural features of object images. In particular, orthogonal and wavelet transforms are presented, adapted for compression of hyperspectral aerospace images with losses, an adaptive discrete cosine transform algorithm is presented, followed by quantization with a loss level and compression. Thanks to a series of experiments on hyperspectral aerospace images, the effectiveness of the proposed algorithm in terms of the degree of compression, as well as the characteristics of the limits of its applicability, can be highlighted. The use of wavelets provides progressive compression of the bitstream, which makes it possible to achieve lossless compression with minimal loss of information due to the modified Huffman algorithm with a compression ratio of 9 more than 2.5 times in existing algorithms, as well as the quality metric of the restored images, the peak signal-to-noise ratio is sufficiently below 32.56. The developed compression algorithm demonstrates the effectiveness of its application in terms of a set of characteristics and is superior to analogues. The scope and conditions for the practical use of the results obtained is a comparison of the proposed algorithm with the results of experiments obtained for universal compression algorithms for archivers and a compressor

Scalable solutions for implementing satellite-based sensor networks

The importance of sensor networks in the field of IoT cannot be overstated, especially with the growing demand for IoT solutions. The new Internet protocols such as IPv6 and 5G mobile provide high-capacity communication networks, demonstrating the relevance and potential applications of IoT in the future. However, with new demands come new challenges. This paper aims to explore the challenges of Satellite-based Sensor Networks, a new topic with various applications. This article explores the challenges and constraints of implementing a scalable satellite-based sensor network. We examined the hardware and software limitations of the Iridium satellite SBD protocol and proposed tailored solutions using design flow algorithms to tackle flexibility, power consumption, bandwidth, and scalability issues. Satellite-based sensor networks face challenges in data transmission and sensor lifespan. In this study, we introduce event-driven and compression algorithms to address these challenges. By implementing clustering, we achieved a significant increase in the number of nodes within each cluster. Our proposed methods were validated using simulated datasets derived from temperature and location data. A unique data transmission method, based on event count, was introduced. Notably, the Differential Huffman compression algorithm resulted in an 81 % reduction in data transmission compared to traditional methods. This demonstrates about 62 % improvement in efficiency over existing techniques. Our approach not only paves the way for future advancements in this domain but also highlights the untapped potential of satellite-based sensor networks.

Distributed video transmission reduction approach for energy saving in WMSNs

SummaryWireless Multimedia Sensor Networks (WMSNs) are composed of a large number of sensor nodes that are distributed in a region to collect and transmit data. Video transmission is one of the most important applications of WMSNs because it can provide critical information about monitored areas. WMSNs face challenges related to energy consumption, bandwidth usage, and network congestion related to huge amounts of data collected by sensors. To tackle this problem, this paper proposes the Distributed Video Transmission Reduction Approach for Energy Saving in WMSN (DiViTRA). The method involves two phases: sensing and transmission phases. DiViTRA achieves frame rate adaptation to reduce the number of captured video frames and save energy during the sensing phase. In the transmission phase, three effective techniques, ORB (Oriented FAST and Rotated BRIEF), Brute‐Force (BF) Matcher, and Grid‐based Motion Statistics (GMS) are applied to decide whether to transmit the current captured frame or remove it and adjust the frame capturing rate of the video sensor accordingly. In the case of frame transmission, the DiViTRA approach compresses the frame using two data reduction approaches: PCA (Principal Component Analysis) and Huffman encoding. Through simulations, DiViTRA demonstrates a 12% reduction in energy consumption, and 71% is a ratio of reduction in sent frames while preserving stream quality. The approach has been validated in scenarios involving critical events, showcasing its efficacy in maintaining data integrity during transmission.