Arithmetic Coding Algorithm Research Articles

Implementasi Algoritma Arithmetic Coding Pada Aplikasi Kompresi File PDF

Currently, application users use information more and more commonly. However, there is a problem that is often encountered, namely the need for large data so that less storage space is needed. Apart from that, the small amount of storage space also affects the speed of sending data. One of the data that is often used is PDF format files. The solution to this problem is to compress the PDF file. Compression is a process of changing a collection of data into a form of code to save on data storage requirements so that the data used is not too large. The Arithemtic Coding algorithm is a data compression algorithm that can be used, by knowing the results of the compression ratio and redundancy process. In applying the Arithmetic Coding algorithm in this research, it can provide compression results which initially have a large size which can be compressed very well in PDF files and the compression results show a compression ratio of 2.15 while the Compression ratio is 46% and Redundancy 54% will be beneficial in sending and moving PDF files will be even easier

Statistical Arithmetic Coding Algorithm Adaptive to Correlation Properties of Wavelet Transform Coefficients

It is shown that, in order to increase the compression ratio in the course of statistical arithmetic coding, it is necessary to take into account the conditional probabilities of code symbols when the preceding symbols appear. The problem of obtaining the location of the most significant symbols when encoding the current symbol is solved by calculating the autocorrelation function of the encoded symbols. An algorithm for arithmetic coding and decoding is provided, which takes into account the dependencies between the coefficients of the wavelet transform and the results of modeling its operation.

Hiding patients’ medical reports using an enhanced wavelet steganography algorithm in DICOM images

In the latest years, there is an increasing emphasis on DICOM medical images, especially in digital medical images transmission to e-health services. Besides, the confidentiality and protection of patients’ secure information need more attention and research. In this paper, we propose an enhanced medical image steganographic algorithm to hide patients’ secure information in their medical images. The proposed algorithm has a higher level of confidentiality while maintaining a higher quality of the medical image and a higher embedding capacity and robustness. The proposed algorithm has two main steps. The medical cover image is decomposed using an integer wavelet filter in the first step. Moreover, the patients’ confidential information is processed using the Arithmetic Coding (AC) and Data Encryption Standard (DES) algorithms to be compressed and encrypted before of the embedding process. In the second step, the insignificant coefficients are selected from the integer wavelet high-frequency subbands of the transformed medical image, and finally, both the target cover bits and the secret bits are grouped during the injection process. The performance of our algorithm was evaluated using different evaluation metrics. The obtained results show that our algorithm outperforms existing ones, with a higher embedding capacity, and a lower imperceptibility. The proposed algorithm can be applied in hospitals and health care canters worldwide.

Implementasi Algoritma Arithmetic Coding dan Sannon-Fano Pada Kompresi Citra PNG

The rapid development of technology plays an important role in the rapid exchange of information. In sending information in the form of images, there are still problems, including because of the large size of the image so that the solution to this problem is to perform compression. In this thesis, we will implement and compare the performance of the Arithmetic Coding and Shannon-Fano algorithms by calculating the compression ratio, compressed file size, compression and decompression process speed. Based on all test results, that the Arithmetic Coding algorithm produces an average compression ratio of 62.88% and a Shannon-Fano compression ratio of 61.73%, then Arithmetic Coding the average speed in image compression is 0.072449 seconds and Shannon-Fano 0.077838 second. Then the Shannon-Fano algorithm has an average speed for decompression of 0.028946 seconds and the Arithmetic Coding algorithm 0.034169 seconds. The decompressed image on the Arithmetic Coding and Shannon-Fano algorithm is in accordance with the original image. It can be concluded from the test results that the Arithmetic Coding algorithm is more efficient in compressing *.png images than the Shannon-Fano algorithm, although in terms of decompression Shannon-Fanose is a little faster compared to Arithmetic Coding.

Huffman-based lossless image encoding scheme

The data produced in today’s Internet and computer world are expanding their wings day by day. With the passage of time, storage and archiving of this data are becoming a significant problem. To overcome this problem, attempts have been made to reduce data sizes using compression methods. Therefore, compression algorithms have received great attention. In this study, two efficient encoding algorithms are presented and explained in a crystal-clear manner. In all compression algorithms, frequency modulation is used. In this way, the characters with the highest frequency after each character are determined and the Huffman encoding algorithm is applied to them. In this study, the compression ratio (CR) is 49.44%. Moreover, 30 randomly selected images in three different datasets consisting of USC-SIPI, UCID, and STARE databases have been used to evaluate the performance of the algorithms. Consequently, excellent results have been obtained in all test images according to well-known comparison algorithms such as the Huffman encoding algorithm, arithmetic coding algorithm, and LPHEA.

DRAC: a delta recurrent neural network-based arithmetic coding algorithm for edge computing

This paper develops an arithmetic coding algorithm based on delta recurrent neural network for edge computing devices called DRAC. Our algorithm is implemented on a Xilinx Zynq 7000 Soc board. We evaluate DRAC with four datasets and compare it with the state-of-the-art compressor DeepZip. The experimental results show that DRAC outperforms DeepZip and achieves 5X speedup ratio and 20X power consumption saving.

Perancangan Aplikasi Kompresi File Audio dengan Algoritma Aritmetic Coding

Information exchange nowadays requires speed in sending information. The speed of this transmission depends on the size of the information. One solution to the above problem is compression. There are lots of data compression methods available today, but in this thesis we will discuss the working principles of the Arithmetic Coding algorithm with an implementation using Visual Basic 6.0. This algorithm performance analysis aims to determine the performance of this algorithm in * .MP3 and * .WAV audio files. In this system there are compression and decompression stages. The compression stage aims to compress the audio file size, while the decompression stage aims to restore the audio file size to its original size.

Image Compression Based on Arithmetic Coding Algorithm

The past years have seen a rapid development in the area of image compression techniques, mainly due to the need of fast and efficient techniques for storage and transmission of data among individuals. Compression is the process of representing the data in a compact form rather than in its original or incompact form. In this paper, integer implementation of Arithmetic Coding (AC) and Discreet Cosine Transform (DCT) were applied to colored images. The DCT was applied using the YCbCr color model. The transformed image was then quantized with the standard quantization tables for luminance and chrominance. The quantized coefficients were scanned by zigzag scan and the output was encoded using AC. The results showed a decent compression ratio with high image quality.

Integrated encryption in dynamic arithmetic compression

A variant of adaptive arithmetic coding is proposed, adding cryptographic features to this classical compression method. The idea is to perform the updates of the frequency tables for characters of the underlying alphabet selectively, according to some randomly chosen secret key K. We give empirical evidence that with reasonably chosen parameters, the compression performance is not hurt, and discuss also aspects of how to improve the security of the system being used as an encryption method. To keep the paper self-contained, we add a short description of the arithmetic coding algorithm that is necessary to understand the details of the new suggested method.

IMPLEMENTASI ALGORITMA KOMPRESI ADAPTIVE ARITHMETIC CODING PADA APLIKASI PENYIMPANAN FILE ONLINE

Along with the development of technology at this time some programmers have found a solution to overcome the problem of storing important files that we worried about before the file security by making several applications that we often refer to as online file storage. Files saved to online file storage are usually document files and image files. However, the problem that arises is the image file has a large size and takes up large storage space and requires a long time in the process of exchanging data. To reduce the size of an image file compression techniques can be used. Compression is a way to compress data so that it only requires a smaller storage space so that it is efficient in storing or shortening time in exchanging information. Compression technique has several algorithms, in this study the authors used the Adaptive Arithmetic Coding algorithm. From the results of the study the authors managed to compress a color image file with jpg extension using the Adaptive Arithmetic Coding algorithm. The application produced in this research is a web designed using Dreamweaver CS5 and php programming language.Keywords: Compression, Adaptive Arithmetic Coding, DreamweaverCS5, PHP.

Residual Syntax Elements Analysis and Design Targeting High-Throughput HEVC CABAC

Video processing is a topic of major interest due to the demand for real-time delivery of increasing resolution videos. Moreover, Internet traffic related to high-quality video processing tends to be the dominant type of information transmitted. The high-efficiency video coding (HEVC) standard is a suitable and efficient choice to overcome the challenges regarding the matter presented. In HEVC, the final encoding step is the entropy encoding, in which the context-adaptive binary arithmetic coding (CABAC) algorithm is the sole option used. Many recent research efforts have been successful in enhancing CABAC throughput for a hardware approach, which is a remarkable difficulty due to the intrinsic dependencies generated by the algorithm. The throughput improvement happens due to the capability of these entropy-encoding designs to consume multiple input data coming from the previous encoding steps. Nevertheless, the same care in terms of data generation shall occur for the stage right before the CABAC block. Otherwise, a transfer of the potential processing bottleneck would move to the input data fabrication. Therefore, this paper proposes a novel scheme, named multi residual syntax element treatment (MRSET), to be compliant with these recent high-throughput CABAC designs, being able to supply the entropy coding with enough input data and thus it would not starve. A complete analysis involving HEVC reference software and recommend video sequences occurred to drive MRSET inception. Different multi-core MRSET hardware architectures results are provided, and the best suitable option is defined based on the characteristics of the state-of-the-art CABAC architectures.

Compression and Decompression of Audio Files Using the Arithmetic Coding Method

Audio file size is relatively larger when compared to files with text format. Large files can cause various obstacles in the form of large space requirements for storage and a long enough time in the shipping process. File compression is one solution that can be done to overcome the problem of large file sizes. Arithmetic coding is one algorithm that can be used to compress audio files. The arithmetic coding algorithm encodes the audio file and changes one row of input symbols with a floating point number and obtains the output of the encoding in the form of a number of values greater than 0 and smaller than 1. The process of compression and decompression of audio files in this study is done against several wave files. Wave files are standard audio file formats developed by Microsoft and IBM that are stored using PCM (Pulse Code Modulation) coding. The wave file compression ratio obtained in this study was 16.12 percent with an average compression process time of 45.89 seconds, while the average decompression time was 0.32 seconds.

An innovative design of a hybrid chain coding algorithm for bi-level image compression using an agent-based modeling approach

In this article, the researcher introduces a hybrid chain code for shape encoding, as well as lossless and lossy bi-level image compression. The lossless and lossy mechanisms primarily depend on agent movements in a virtual world and are inspired by many agent-based models, including the Paths model, the Bacteria Food Hunt model, the Kermack–McKendrick model, and the Ant Colony model. These models influence the present technique in three main ways: the movements of agents in a virtual world, the directions of movements, and the paths where agents walk. The agent movements are designed, tracked, and analyzed to take advantage of the arithmetic coding algorithm used to compress the series of movements encountered by the agents in the system. For the lossless mechanism, seven movements are designed to capture all the possible directions of an agent and to provide more space savings after being encoded using the arithmetic coding method. The lossy mechanism incorporates the seven movements in the lossless algorithm along with extra modes, which allow certain agent movements to provide further reduction. Additionally, two extra movements that lead to more substitutions are employed in the lossless and lossy mechanisms. The empirical outcomes show that the present approach for bi-level image compression is robust and that compression ratios are much higher than those obtained by other methods, including JBIG1 and JBIG2, which are international standards in bi-level image compression. Additionally, a series of paired-samples t-tests reveals that the differences between the current algorithms’ results and the outcomes from all the other approaches are statistically significant.

Performance evaluation of arithmetic coding data compression for internet of things applications

<span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Wireless Sensor Network (WSN) is known for its autonomous sensors, where it has been found to be useful during the development of Internet of Things (IoT) devices. However, WSN is also known for its limited energy supply and memory space, as it carries small-sized batteries and memory space. Hence, a data compression approach has been introduced in this paper with the purpose of solving this particular issue. This paper focused on the performance of the Arithmetic Coding algorithm. Temperature (Temp), Sea-Level Pressure (Pressure), stride interval (Stride), and heart rate (BPM) were chosen as the dataset in this project. Based on the results, the compression ratio of Temp, Pressure, Stride, and BPM were 0.428, 0.255, 0.217, and 0.159 respectively. From this analysis, BPM produced the best compression ratio. Undeniably, the Arithmetic Coding algorithm is one of the best methods to compress real-world datasets. Hence, by using this approach, it can reduce the usage of energy and memory space.</span><table class="MsoTableGrid" style="width: 444.85pt; border-collapse: collapse; border: none; mso-border-alt: solid windowtext .5pt; mso-yfti-tbllook: 1184; mso-padding-alt: 0in 5.4pt 0in 5.4pt;" width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr style="mso-yfti-irow: 0; mso-yfti-firstrow: yes; mso-yfti-lastrow: yes; height: 63.4pt;"><td style="width: 290.6pt; border: none; border-top: solid windowtext 1.0pt; mso-border-top-alt: solid windowtext .5pt; padding: 0in 5.4pt 0in 5.4pt; height: 63.4pt;" valign="top" width="387"><p class="MsoNormal" style="margin-top: 6.0pt; text-align: justify;"><span style="font-size: 9.0pt; color: black; mso-bidi-font-style: italic;">Wireless Sensor Network (WSN) is known for its autonomous sensors, where it has been found to be useful during the development of Internet of Things (IoT) devices. However, WSN is also known for its limited energy supply and memory space, as it carries small-sized batteries and memory space. Hence, a data compression approach has been introduced in this paper with the purpose of solving this particular issue. This paper focused on the performance of the Arithmetic Coding algorithm. Temperature (Temp), Sea-Level Pressure (Pressure), stride interval (Stride), and heart rate (BPM) were chosen as the dataset in this project. Based on the results, the compression ratio of Temp, Pressure, Stride, and BPM were 0.428, 0.255, 0.217, and 0.159 respectively. From this analysis, BPM produced the best compression ratio. Undeniably, the Arithmetic Coding algorithm is one of the best methods to compress real-world datasets. Hence, by using this approach, it can reduce the usage of energy and memory space.</span></p></td></tr></tbody></table>

Improving Efficiency of Entropy Coding Method in Modern Video Compression Standards

Modern video coding standards have high coding efficiency, but the encoding performance has to be improved to meet the growing multimedia applications. The paper deals with the entropy encoding methods and algorithms in video coding standard H.264/AVC and H.265/HEVC. Context-based Adaptive Variable Length Coding (CAVLC) for the H.264/AVC standard was originally designed for lossy video coding, and as such does not yield adequate performance for lossless video coding. Context-Adaptive Binary Arithmetic Coding (CABAC) is a method of entropy coding first introduced in H.264/AVC and now used in the standard H.265/HEVC. While it provides high coding efficiency, the data dependencies in H.264/AVC CABAC make it challenging to parallelize and thus, limit its throughput. Accordingly, during the standardization of entropy coding for HEVC, both coding efficiency and throughput were considered. Based on an analysis of their advantages and disadvantages, a method called the entropy coding algorithm using the enumerative coding of the hierarchical approach is proposed. The proposed algorithm consists of the Context-Adaptive Binary Arithmetic Coding algorithm and the enumerative coding algorithm with a hierarchical approach. The proposed algorithm is tested in the Visual C ++ development environment on various test video sequences. The results of the experiments showed a greater efficiency of coding of multimedia data (the proposed one reduces on average up to 15% of the storage volume compared to the traditional CABAC method), while the method requires a longer coding time (approximately twice). The proposed method can be recommended for use in telecommunication systems for storage, transmission and processing of multimedia data, where a high degree of compression is required first.

Image Steganography In Securing Sound File Using Arithmetic Coding Algorithm, Triple Data Encryption Standard (3DES) and Modified Least Significant Bit (MLSB)

The amount of data inserted in the form of audio samples that use 8 bits with LSB algorithm, affect the value of PSNR which resulted in changes in image quality of the insertion (fidelity). So in this research will be inserted audio samples using 5 bits with MLSB algorithm to reduce the number of data insertion where previously the audio sample will be compressed with Arithmetic Coding algorithm to reduce file size. In this research will also be encryption using Triple DES algorithm to better secure audio samples. The result of this research is the value of PSNR more than 50dB so it can be concluded that the image quality is still good because the value of PSNR has exceeded 40dB.

SMS Compression System using Arithmetic Coding Algorithm

<p>Short Message Service (SMS) has limitation in the length of its text message, which only provides 160 characters per SMS. It means that if we send more than 160 characters, it will be considered as sending more than one SMS, so that we have to spend more cost for sending SMS. On the other side, the Arithmetic Coding algorithm provides an effective mechanism for text compression. It has performed the great compression result and in many case it was considered as the better compression algorithm than other ones, such as Huffman and LZW (Lempel-Ziv-Welch). This research will implement the Arithmetic Coding algorithm to develop an application that will compress the SMS text message. The concept of Arithmetic Coding will be implemented to compress the SMS text message before it is sent from the sender to the receiver. The application is called CheaperZipper (CZ). This application will handle the sending and receiving SMS in the hand phone by preceding the compression and decompression process. </p>

An Efficient Medical Image Watermarking Technique in E-healthcare Application Using Hybridization of Compression and Cryptography Algorithm

Abstract The main objective of this paper is to propose a medical image watermarking technique in E-healthcare application based on the hybridization of compression and cryptography algorithm. Basically, the proposed system consists of two stages: (i) watermark embedding process and (ii) watermark extraction process. In the embedding process, at first, we segment the tumor part separately using a region growing algorithm. Then, we encrypt the region of interest part using Secure Hash Algorithm-256 and encrypt the electronic health record (EHR) using the elliptical curve cryptography algorithm. Thereafter, we concatenate and compress the information using an arithmetic coding algorithm. Finally, we embed the compressed bit into the original image. The same process is repeated for the extraction process. The experimental results are shown for different medical images with EHR and the effectiveness of the proposed algorithm is analyzed with the help of the peak signal-to-noise ratio and normalized correlation.

New Image Compression/Decompression Technique Using Arithmetic Coding Algorithm

Arithmetic coding is a form of entropy encoding used in lossless data compression. In this article, new technique for image compression/decompression based on arithmetic coding decoding was used. The input image is divided into blocks, each block is 32 x 32 , 64 x 64, and 128 x 128 pixels. If the image is not multiple of the block size , the size of what remains of the image is calculated and columns and rows of zero values are added(padding) up to be all the blocks are 32 x 32,64 x 64..etc . The blocks are separated first as a stream for processing. The result is a stream of compressed tables after do arithmetic coding process and the store bytes are calculated during this process. Then compression ratio is calculated. Finally the stream of the compressed tables is recollected for decompressing (reconstruction image after decoding process) and this is done for each block. The algorithm is tested on different images and the results show a high space saving (88-90%), the process is fast, and it is easy to decode the binary bits to recover the original image exactly without any lose.

Modified Image Compression by Combined Neural Network and Arithmetic Coding Processing

Modified Image Compression by Combined Neural Network and Arithmetic Coding Processing This article emphases the skeleton for image compression-decompression by using (Perceptron Neural Network and Arithmetic Coding). At first compression of each three-pixel into single value, this value is called Compression value. In our work the neural network is not needed for weight training at compression part, instead, the weights used in our work are one dimensional array containing floating point values. The total of weights values equal one. In the decompression part, the weights become an input to neural networks. Later, neural networks update the pixels according to error between Compression value and desired output to become approximately original three-Bytes. The second stage is Arithmetic coding algorithm uses to convert vector of compression values in to single floating point number. Our approach tested with three types of images and different size; also in this article the performance of the algorithm is computed.