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Summary: Wireless 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. [ABSTRACT FROM AUTHOR]

In the last decade, data hiding in encrypted image has got a lot of attention within the security and privacy researcher's communities. This attention has led to the development of various reversible data hiding techniques for encrypted images (RDHEI) which enables separable lossless recovery of cover media and extraction of the secret message, at the receiving end. In this paper, a link chain driven (LCD) RDHEI algorithm for providing high embedding capacity, is proposed. The LCD-RDHEI exploits neighbourhood similarity of image pixels and clubs them in variable size link chains. The pixels in each link chain are estimated either by their average or using two quantization values based on the size of link chains. This helps in reserving a big room inside the image which can be used to fill in the secret information. Additionally, the pixels which are not part of any of the link chains are also utilized for embedding the secret information by adaptive multi MSB prediction and Huffman Coding method. Experimental results show that the LCD-RDHEI provides significantly greater embedding capacity than the existing and related modern data hiding method for encrypted images. [ABSTRACT FROM AUTHOR]

In this era of information security and communication, a major priority is the achievement of a robust and secure steganography system when thinking about information concealment. The development of such an information-hiding scheme demands that the scheme be able to hide a secret message within the cover media. The most vexing issues in existing steganography protocols are imperceptibility, security, and capacity, and researchers have frequently emphasized a trade-off between these issues. Scholars have consistently ignored the balance between security and payload because resolving one problem has been shown to have an impact on the other, and vice versa. To overcome these problems, an effective method known as the Conventional Neural Network based Edge Detection Method (CNN-EDM) has been presented for image steganography in this study. The CNN-EDM is used to improve the contributions of the proposed scheme. Four main stages were used to achieve the objectives in this research, beginning with the cover image and secret image preparation, followed by embedding, and culminating in extraction. The last stage is the evaluation stage, which employs several evaluations to benchmark the obtained results. A standard database from the Signal and Image Processing Institute (SIPI) containing color and grayscale images with 512 × 512 pixels was utilized in this study. Different parameters were used to test the performance of the suggested scheme based on security and imperceptibility (image quality). The image quality was evaluated using three important metrics: histogram analysis, peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Furthermore, two metrics were used to evaluate the security properties of the proposed system: the Human Visual System (HVS) and Chi-square (X2) attacks. The evaluations showed that the proposed scheme can enhance the capacity, invisibility, and security features and address the already existing problems in this domain. [ABSTRACT FROM AUTHOR]

Protecting information from manipulation and theft is a top priority as a result of progress in technology and the infrastructure of the multimedia network in addition to the development of illegal methods of obtaining information. One of the means of protecting and preserving information is to hide it in a digital medium. The motivation for introducing such a system is to enhance the security of confidential data by providing ways to protect the data and reduce attempts to attack it. The proposed system contains several steps summarized as follows: The sender side includes firstly the stage of generating hiding locations randomly depending on the genetic algorithm (GA) to generate rows and seed to generate columns. Secondly, the stage of including data after compressing it by the Huffman method. Data embedding depends on the pixel index as an indicator to choose one of the three bands to hide using LSB. The recipient side extracts the important information hiding in the two last rows which helps to extract the data and convert it into the original text. The proposed system gained efficiency and robustness with the help of genetic and Huffman where genetic chooses the best way for hidding among a set of suggested solution in addition to the randomness it possesses. The role of Huffman reduce data size and thus increase the cover capacity. System efficiency has been measured by PSNR through conducting a number of experiments that included using set of texts with different sizes and two types of standard colored cover images. [ABSTRACT FROM AUTHOR]

Goal: Purpose of this paper is Study of the efficiency of using RLE and Hoffman compression methods for image compression, taking into account the limited resources of microcontrollers and, accordingly, the need to reduce image transmission time and reduce energy consumption in the wireless sensor networks. Method: An experimental study of the degree of image compression was conducted using the Hoffman and RLE methods. The algorithms are implemented in software for the ESP32-CAM microcontroller module with a built-in camera. The degree of image compression performed using the Hoffman and RLE algorithms was evaluated and compared. Results: A software module was created to implement the Huffman and RLE compression algorithms on the ESP32-CAM microcontroller. The effectiveness of using Huffman and RLE algorithms for image compression with limited microcontroller resources is investigated, compression efficiency and compressed file sizes are compared. As a result of the research, it was found that the RLE method showed a low compression efficiency (less than 1%) compared to the Huffman method, which provided a compression ratio of 11% for the image under test. The previous use of the Haar wavelet transform has a positive effect on the compression results for both investigated methods: for RLE, the degree of compression increased to 16%; for Huffman to 31% without loss of image quality. The JPEG method was used for efficiency comparison, which provides a degree of compression of the same image up to 70%, but with a loss of compressed data quality. Scientific innovation: An analysis of the data compression efficiency using the Huffman and RLE methods, implemented taking into account the limited computing capabilities of 32-bit microcontrollers with the use of additional external memory, was performed. The influence of the choice of method on the time of image processing and the amount of memory used was studied. Practical significance: Software module has been created that implements Huffman and RLE data compression algorithms. The expediency of using the above methods in systems on 32-bit microcontrollers to reduce energy consumption during image transformations and transmission, which makes it possible to extend the battery life of the sensor node, has been confirmed. At the same time, the investigated algorithms for image compression can ensure the quality of the original image with the least possible data loss. [ABSTRACT FROM AUTHOR]

Data storage on the device can affect the access speed of the device used; for example, files, images, and data will affect the performance of the device, become slow to access, difficult to open, download, and save images, files, because the available storage capacity is limited, with the problems that arise, an image compression technique is needed to minimize storage space and speed up the access. The compression technique can reduce a file, image, and data size but does not reduce the existing image's quality or lower the threshold during the sending or receiving. This research aims to reduce the size, speed up the process of accessing data on devices, and, more importantly, minimize memory space. It can also affect the bandwidth used when sending and receiving files and can speed up the process of sending from source to destination. The method used in this study is Lossy Compression, lose less Compression by comparing RLE, Huffman, and LZW using different image file types. For the Lossless Technique, the best quality reduction ratio is in binary image types, whether using a background or not using a background. The best results obtained are 99.10% (PNG Compression). Using the BMP file extension type, the recommended reduced ratio is lossy compression with format image BMP (JPG compression) for binary image using Lossless Compression has a good reduced ratio compression with an average of 99%. [ABSTRACT FROM AUTHOR]

Compression is a process that is always carried out in terms of digitizing data, which is considered very important, especially in the development and growth of the Big Data era. Lossless compression is the process of reducing the size of the data but with the condition that it can be returned to its original source during the decompression process. One of the purposes of doing Lossless compression is to archive a file, usually the file is RAW and has a large file size with a minimum 16-Bit file system (65,536 possible differences in values). Huffman's algorithm is currently still very effective in compressing 8-bit data, which can be grouped into Static, Dynamic, and Adaptive extensions, but its performance cannot be determined if it is performed on data with complex variables and probabilities such as WAV format audio data. Based on a literature review, the compression performance measurement for file archiving uses the Compression Ratio (CR) and Compression Time (CT) indicators. This research resulted in a new scheme which we named 4-ary/MQ, the architectural basis of which is based on entropy coding rooted in the static, dynamic and adaptive variants of the Huffman scheme. For the variable code length characteristics, it follows the Quad Tree dynamic branching (FGK rule), the node symbol setting adopts an adaptive method, namely adding a maximum of 2 variables with a value of '0' to maintain the root of the branch after the root always has 4 branches. Based on descriptive analysis of compression results, deviation, average, ANOVA and DMRT, 4-ary/MQ produces optimal CR with fast CT when compared to various variants of the Huffman algorithm and other lossless compression applications such as (PKZIP, WinZip, 7-Zip, and Monkeys Audio). From the results of trial analysis based on manual mathematical and statistical calculations, it is certain that 4-ary/MQ provides high compression results with a very fast process, so it has many benefits if it is used to compress data on local storage media, hosting/cloud and bandwidth. [ABSTRACT FROM AUTHOR]

Steganography is the practice of hiding data, such as images, videos, or text, within a cover image without it being detectable by the human eye. Several factors, such as the capacity, security, and robustness of the technique, are essential when transferring information using this method. In this study, we propose a new approach to image steganography that improves the Least Significant Bit (LSB) technique by utilizing images of 24 bits in each pixel. Improving the capacity and security of LSB-based steganography requires a combination of techniques, such as indirect embedding, embedding in multiple channels, and applying cryptographic and compression techniques. This approach conducts by encrypting each compressed secret message bit with the most significant bit of the red channel and then saving the output bit (hidden bit) in the least significant bit of the (green/blue) channel according to the row value (odd/ even). To further security, the suggested approach uses multi-level encryption; employs RSA to encrypt the secret message before applying the Huffman compression and encrypting the hidden bit by (XOR/XNOR) in the embedding method based on the row value of the pixel. Meanwhile, it is planned to use the Huffman coding technique to shorten the length of the encrypted message that will be inserted in the cover image. For the color photos in this work, the standard images were acquired from a standard dataset (USC-SIPI). The suggested approach performs better when measured in terms of mean square error (MSE), peak signalto- noise ratio (PSNR), and comparison to findings from related prior efforts. [ABSTRACT FROM AUTHOR]

Advanced therapeutic imaging innovation produces an immense amount of information, predominantly from processed tomography and other imaging modalities. This causes a significant challenge when storing them on a local personal computer or communicating them over cyberspace. Therefore, a proficient image compression system is fundamentally required. From this perspective, this paper proposes a lossless image compression procedure by reducing image dimension and using a prediction technique. In the proposed strategy, the column dimension of a grey-scale image is first reduced and then the prediction errors are encoded using Huffman coding. The decoding process is carried out in the reverse direction. The proposed method is executed and applied to several bench-marked images. The performance of this proposed algorithm is assessed and compared with the state-of-the-art techniques based on several assessment criteria, such as average code length (ACL), compression ratio (CR), encoding time, decoding time, efficiency, peak signal to noise ratio (PSNR) and normalised correlation (NC). The proposed algorithm also demonstrates an improvement in the average code length compared with the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Shannon-Fano coding (SFC) and Huffman coding (HC) are classic and well-known algorithms, but still in use today. The search for the simplest example that proves HC overperforms SFC is still of interest. The problem is not as trivial as it looks like at first view because of several decisions that must be considered. We perform a full-search of the stream data space for a maximum stream length of 100. Depending on additional requests we impose, the simplest solution we found is {1,1,1,1,3} when we accept to select a specific cutting, {2,3,3,3,7} when we accept only deterministic (unique) cuttings and {4,5,6,7,14} when we also ask for different frequencies for symbols as well. [ABSTRACT FROM AUTHOR]

The Vernam-Cipher is known as a one-time pad of algorithm that is an unbreakable algorithm because it uses a typically random key equal to the length of data to be coded, and a component of the text is encrypted with an element of the encryption key. In this paper, we propose a novel technique to overcome the obstacles that hinder the use of the Vernam algorithm. First, the Vernam and advance encryption standard AES algorithms are used to encrypt the data as well as to hide the encryption key; Second, a password is placed on the file because of the use of the AES algorithm; thus, the protection record becomes very high. The Huffman algorithm is then used for data compression to reduce the size of the output file. A set of files are encrypted and decrypted using our methodology. The experiments demonstrate the flexibility of our method, and it is successful without losing any information. [ABSTRACT FROM AUTHOR]

The data or image transmission plays a very important role in current days. In general image can be transmitted in terms of data. The basic image can be converted or encoded into bits or chunks. This data can be transmitted in efficient form. Transmitted images in the multimedia field is difficult task. The images will occupies huge amount of size; without compressing it will take more amount of disk space and time. In this, there is a chance of data loss due to timeout. There is a solution to overcome such difficult issue is to reduce the size of an image without losing image data by implementing compressing technique. In this paper, we discussed about image compression and different image compression techniques without any data loss. Few of it are run length arithmetic encoding, Huffman coding and LZW. Finally, we presented the performance issues and pros of compression techniques. [ABSTRACT FROM AUTHOR]

In this paper we give an overview and compare Huffman and Arithmetic algorithm using PCM audio data 16 bits stereo. The motivation for the study is to increase the transmission speed in the information exchange which is greatly dependent upon the size of the information. Compression has become one of the solutions to the above problem. The quest for efficient audio data compression rate is still ongoing and good quality compression particularly for music files are greatly sought after by the industry as well as users. Currently music files are stored in various formats and the paper will focus on a lossless technique. The technique is applied to raw WAV files with a comparison of two algorithms, namely Huffman and Arithmetic, and to compare the parameters of file size, ratio, speed, the factor of compression and waveform. The results are then compared to previous existing research and then present the difference of the test result on other data such as text and images. [ABSTRACT FROM AUTHOR]

This letter presents a fronthaul signal compression scheme based on linear prediction coding adapted to the orthogonal frequency division multiplexing signals. The proposed method is capable of providing fine tuning of the compression factor, which is an alternative to legacy compression methods that tune the compression factor by changing the discrete number of bits of the quantizer, and consequently, are only able to do so with coarse resolution. [ABSTRACT FROM PUBLISHER]

The significance and the part of discrete science points in the area of data innovation and interchanges are talked about. A few associations and uses of these themes with the substance of sanctified subjects in a data innovation program are investigated. An uncommon accentuation is given to the uses of established and fluffy rationale in data innovation. [ABSTRACT FROM AUTHOR]

Sequence clustering has become an important topic that experts in data mining are currently investigating. However, clustering quality is typically significantly affected by both the selection of initial centers and the mean sequences. In this study, the sequence clustering algorithm based on weighted vector identification (SCAWVI) algorithm is developed based on sequence element composite similarity and the weight of a sequence in its corresponding cluster. Based on the weighted sequence element, all sequences in the sequence database are preprocessed into M-dimensional weighted vector identifications. Then, using Huffman-based initial clustering centers optimization algorithm, the initial clustering centers are optimized. In addition, the weighted vector identification and the weight of a sequence in its corresponding cluster are used to update the clustering centers. The theoretical experimental results and the analysis results in this study show that the SCAWVI algorithm has a higher rate of accurate results in its clustering results and higher execution efficiency. [ABSTRACT FROM AUTHOR]

Maintenance of ever-expanding road networks all over the world is a critical task and regular monitoring of the road surface is imperative for successful maintenance. Existing monitoring techniques use dedicated vehicles equipped with sensors. Limited availability of these specialized vehicles makes regular monitoring very challenging. This paper explores a novel approach of empowering the smartphone user community for monitoring roads that they use. Smartphones are used for sensing the road surface conditions from a moving vehicle. The system involves client applications on smartphones that collect accelerations as the users travel on the roads, processing of the information and communication to a central server, georeference database, and visualization of road conditions on Google maps. In this paper, the fuzzy logic--based road surface roughness classification is proposed for monitoring the road roughness. The implementation has been demonstrated in different road networks at northern region of India. The experiment demonstrates the huge potential of user community participation in maintenance of road infrastructure. [ABSTRACT FROM AUTHOR]

Typical fully distributed optical fiber sensors (DOFS) with dozens of kilometers are equivalent to tens of thousands of point sensors along the whole monitoring line, which means tens of thousands of data will be generated for one pulse launching period. Therefore, in an all-day nonstop monitoring, large volumes of data are created thereby triggering the demand for large storage space and high speed for data transmission. In addition, when the monitoring length and channel numbers increase, the data also increase extensively. The task of mitigating large volumes of data accumulation, large storage capacity, and high-speed data transmission is, therefore, the aim of this paper. To demonstrate our idea, we carried out a comparative study of two lossless methods, Huffman and Lempel Ziv Welch (LZW), with a lossy data compression algorithm, fast wavelet transform (FWT) based on three distinctive DOFS sensing data, such as Φ-OTDR, P-OTDR, and B-OTDA. Our results demonstrated that FWT yielded the best compression ratio with good consumption time, irrespective of errors in signal construction of the three DOFS data. Our outcomes indicate the promising potentials of FWT which makes it more suitable, reliable, and convenient for real-time compression of the DOFS data. Finally, it was observed that differences in the DOFS data structure have some influence on both the compression ratio and computational cost. [ABSTRACT FROM AUTHOR]

The purpose of this article is to describe the concept of point clouds and to present the main idea and the important steps made in this research domain. This article also presents methods that aim to solve the point clouds compression problem. [ABSTRACT FROM AUTHOR]

This paper presents a new code compression method where we merge the Huffman algorithm with the formation of patterns of blocks, which are found into the dictionary. We seek two consecutive blocks in two levels, one of them into compressed code and the other related to uncompressed code. We have used simulation with SimpleScalar and programs from MiBench. Our proposal has achieved a compression rate of 34.5% and 7% improvement in code compression when compared to the Huffman traditional method. [ABSTRACT FROM PUBLISHER]

This paper presents an introduction about Image compression techniques. Image compression is an application of data compression that encodes the original image with few bits. The objective of image compression is to reduce the redundancy of the image and to store or transmit data in an efficient form. In recent years, the development and demand of multimedia product grows increasingly fast, contributing to insufficient bandwidth of network and storage of memory device. Therefore, the theory of data compression becomes more and more significant for reducing the data redundancy to save more hardware space and transmission bandwidth. In computer science and information theory, data compression or source coding is the process of encoding information using fewer bits or other information-bearing units than an unencoded representation. Compression is useful because it helps reduce the consumption of expensive resources such as hard disk space or transmission bandwidth. [ABSTRACT FROM AUTHOR]

Over the last two decades, great developments have been made in image compression approaches driven by a growing demand for storage and transmission of visual information. However, a number of publications have demonstrated good results of the compressed domain approach to image analysis. Nevertheless, very little work has been carried out on ROI based compression. In this paper, we have proposed an adaptive approach to compress the image without lossless version using selection of DCT and DWT coefficients and MKFCM. Our proposed approach consists of three stages, (i) Region segmentation (ii) Image compression (iii) Image decompression. At first, the input medical image is segmented by ROI, Non-ROI and background using MKFCM. Subsequently, the ROI region compressed by DCT and SPIHT coding and Non-ROI region is compressed by DWT and Huffman coding. The non-relevant regions are directly converted to zero. From the compressed regions like as ROI, Non-ROI and background, finally we obtain compression ratio to evaluate the proposed approach. Then, in decompression stage, the original medical image is extracted using the devised procedure. We can also see that our proposed image compression approach have outperformed by having better compression ratio of 4.6446 when compared existing technique only achieved 4.4326. [ABSTRACT FROM AUTHOR]

This paper presents the various image compression techniques on the basis of analyzing existing research papers. Basically two types of image compression techniques are introduced namely lossless & lossy image compression lossless image represented in BMP(bit map),PNG(portable network Graphics),TIFF(tagged image file format) formats and Lossy image represented in JPEG (Join photographic Experts group), JPEG 2000 formats. Lossless compression algorithms were developed to meet both the need of compression and the requirement for no change in image bits for certain applications. Video and audio files are very large unless we develop and maintain very high bandwidth networks (gigabytes per second or more) we have to compress data. Compression becomes part of the representation or coding scheme which have become popular audio, image and video formats. In the lossless group of image compression algorithms, the desire is to reduce the number of bits to represent the image in a way that is reversible without any data loss. [ABSTRACT FROM AUTHOR]

Packet marking is a commonly used IP traceback algorithm, which marks the path information into the IP header and is able to reconstruct the attack path according to the marked information so as to find the attack source. The marked information is not much due to the limited mark area, so several marked packets are required to reconstruct an attack path, resulting in high complexity, low efficiency and poor accuracy of path reconstruction algorithms. To address this problem, a path reconstruction algorithm based on Huffman code was proposed, which marks the link information into the mark area and does not need to store the marked information into intermediate nodes. The proposed algorithm is applicable to IPv6 networks and only requires a single marked packet to reconstruct the entire attack path. The experiment shows that the proposed algorithm is fast and has high efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Radiographic images are generally of high volume. These images when stored occupy a lot of storage space and the transmission of them is also difficult due their large size. So reduction of size plays an important role in storage and transmission of images. To achieve this reduction, compression techniques are being widely used. This paper focuses on the process of welding, Tungsten Inert Gas (TIG) welding in specific, the generation and development of photographic film of weldments and also a novel method is presented, which is intended to reduce the size of the radiographic image developed, by applying lossless compression technique on the crucial regions of the image. The crucial regions of the image are automatically detected by using segmentation technique in the proposed method. After the detection of crucial regions, lossless compression technique is applied on them. The compressed data obtained along with some information of the image is transmitted to the other validated user for reconstruction of the original image. Apparently, the original image data can't be understood by the third party during transmission, which makes this method to be used for secure transmission of data. The image reconstructed by this method will not have any loss of data in the crucial regions of the image but certain loss is present in the non-crucial regions. This method is tested on some images and their results are shown in the paper along with the compression rates. The results are also compared with other methods. [ABSTRACT FROM AUTHOR]

JPEG coder is still the most attractive compression scheme for still image. Using Huffman coding makes the JPEG coder very sensitive to communication error. In this paper we propose a simple modification to JPEG coder by using reversible variable length code designed for the DC coefficients of each block. The results show great improvement in error resilience in a binary symmetric channel transmission with an average reduction of 5-10% in compression ratio. [ABSTRACT FROM AUTHOR]

Compression and Encryption techniques are getting very popular day by day as compression reduces file size & encryption ensures the security of a particular file which is to be sent over some unreliable network like internet. In this project, our main objective was to encrypt a image file, compress it using any compression algorithm and send it over the mail such that the receiver gets the image file in more compressed format. On the way of doing this, at first we digitized a image file and encrypted it using RSA algorithm. After that we compressed it using Huffman algorithm. The reverse process has been applied to get the original image file but in more compressed format. [ABSTRACT FROM AUTHOR]

In the last few years, Low-Power Wide-Area Network (LPWAN) technologies have been proposed for Machine-Type Communications (MTC). In this paper, we evaluate wireless relay technologies that can improve LPWAN coverage for smart meter communication applications. We provide a realistic coverage analysis using a realistic correlated shadow-fading map and path-loss calculation for the environment. Our analysis shows significant reductions in the number of MTC devices in outage by deploying either small cells or Device-to-Device (D2D) communications. In addition, we analyzed the energy consumption of the MTC devices for different data packet sizes and Maximum Coupling Loss (MCL) values. Finally, we study how compression techniques can extend the battery lifetime of MTC devices. [ABSTRACT FROM AUTHOR]

Text compression is one of the most significant research fields, and various algorithms for text compression have already been developed. This is a significant issue, as the use of internet bandwidth is considerably increasing. This article proposes a Burrows–Wheeler transform and pattern matching-based lossless text compression algorithm that uses Huffman coding in order to achieve an excellent compression ratio. In this article, we introduce an algorithm with two keys that are used in order to reduce more frequently repeated characters after the Burrows–Wheeler transform. We then find patterns of a certain length from the reduced text and apply Huffman encoding. We compare our proposed technique with state-of-the-art text compression algorithms. Finally, we conclude that the proposed technique demonstrates a gain in compression ratio when compared to other compression techniques. A small problem with our proposed method is that it does not work very well for symmetric communications like Brotli. [ABSTRACT FROM AUTHOR]

Wireless Capsule Endoscopy is a state-of-the-art technology for medical diagnoses of gastrointestinal diseases. The amount of data produced by an endoscopic capsule camera is huge. These vast amounts of data are not practical to be saved internally due to power consumption and the available size. So, this data must be transmitted wirelessly outside the human body for further processing. The data should be compressed and transmitted efficiently in the domain of power consumption. In this paper, a new approach in the design and implementation of a low complexity, multiplier-less compression algorithm is proposed. Statistical analysis of capsule endoscopy images improved the performance of traditional lossless techniques, like Huffman coding and DPCM coding. Furthermore the Huffman implementation based on simple logic gates and without the use of memory tables increases more the speed and reduce the power consumption of the proposed system. Further analysis and comparison with existing state-of-the-art methods proved that the proposed method has better performance. [ABSTRACT FROM AUTHOR]

We describe an algorithm computing an optimal prefix free code for n unsorted positive weights in time within O (n (1 + lg α)) ⊆ O (n lg n) , where the alternation α ∈ [ 1.. n − 1 ] approximates the minimal amount of sorting required by the computation. This asymptotical complexity is within a constant factor of the optimal in the algebraic decision tree computational model, in the worst case over all instances of size n and alternation α. Such results refine the state of the art complexity of Θ (n lg n) in the worst case over instances of size n in the same computational model, a landmark in compression and coding since 1952. Beside the new analysis technique, such improvement is obtained by combining a new algorithm, inspired by van Leeuwen's algorithm to compute optimal prefix free codes from sorted weights (known since 1976), with a relatively minor extension of Karp et al.'s deferred data structure to partially sort a multiset accordingly to the queries performed on it (known since 1988). Preliminary experimental results on text compression by words show α to be polynomially smaller than n, which suggests improvements by at most a constant multiplicative factor in the running time for such applications. [ABSTRACT FROM AUTHOR]

Modern daily life activities result in a huge amount of data, which creates a big challenge for storing and communicating them. As an example, hospitals produce a huge amount of data on a daily basis, which makes a big challenge to store it in a limited storage or to communicate them through the restricted bandwidth over the Internet. Therefore, there is an increasing demand for more research in data compression and communication theory to deal with such challenges. Such research responds to the requirements of data transmission at high speed over networks. In this paper, we focus on deep analysis of the most common techniques in image compression. We present a detailed analysis of run-length, entropy and dictionary based lossless image compression algorithms with a common numeric example for a clear comparison. Following that, the state-of-the-art techniques are discussed based on some bench-marked images. Finally, we use standard metrics such as average code length (ACL), compression ratio (CR), pick signal-to-noise ratio (PSNR), efficiency, encoding time (ET) and decoding time (DT) in order to measure the performance of the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]