Arithmetic Coding Research Articles

Cover Reproducible Steganography via Deep Generative Models

Whereas cryptography easily arouses attacks by means of encrypting a secret message into a suspicious form, steganography is advantageous for its resilience to attacks by concealing the message in an innocent-looking cover signal. Minimal distortion steganography, one of the mainstream steganography frameworks, embeds messages while minimizing the distortion caused by the modification on the cover elements. Due to the unavailability of the original cover signal for the receiver, message embedding is realized by finding the coset leader of the syndrome function of steganographic codes migrated from channel coding, which is complex and has limited performance. Fortunately, deep generative models and the robust semantic of generated data make it possible for the receiver to perfectly reproduce the cover signal from the stego signal. With this advantage, we propose cover-reproducible steganography where the source coding, e.g., arithmetic coding, serves as the steganographic code. Specifically, the decoding process of arithmetic coding is used for message embedding and its encoding process is regarded as message extraction. Taking text-to-speech and text-to-image synthesis tasks as two examples, we illustrate the feasibility of cover-reproducible steganography. Steganalysis experiments and theoretical analysis are conducted to demonstrate that the proposed methods outperform the existing methods in most cases.

Image compression and reconstruction based on GUI and Huffman coding

Huffman coding is an important part of image compression technology, the image compression platform is based on GUI, and Huffman is also widely used. This paper introduces the basic principle of the Huffman algorithm, compares it with arithmetic coding and run length encoding, and expounds on the application of these three algorithms in JPEG compression. The AC algorithm combined block-based, fine texture models and adaptive arithmetic coding in the given an example. The RLE algorithm used automatic threshold, direction judgment, and selective value counts to improve its compression efficiency. JPEG algorithm adopted an adaptive quantization table to reduce the distortion rate of image compression. This paper proves the possibility of a better compression rate and distortion rate of the hybrid compression algorithm by demonstrating the improved example of the basic image compression algorithm. In the future, the improved basic algorithms can be combined based on the original JPEG algorithm, and different algorithms can be integrated on the GUI to face different use environments.

Arithmetic Autocorrelation Distribution of Binary m -Sequences

Binary <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> -sequences are those with the largest period <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>m</i></sup> –1 among the binary sequences produced by linear shift registers with length <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> . They have a wide range of applications in communication since they have several desirable pseudorandom properties, such as balance, uniform pattern distribution, and ideal (classical) autocorrelation. In 1967, Mandelbaum introduced a 2-adic version of classical autocorrelation of binary sequences, called arithmetic autocorrelation, in his research on arithmetic codes. Later, Goresky and Klapper generalized this notion to the nonbinary case and got several properties of arithmetic autocorrelation related to linear shift registers with carry. Recently, Z. Chen et al. showed an upper bound on the arithmetic autocorrelation of binary <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> -sequences and raised a conjecture on the absolute value distribution of the arithmetic autocorrelation of binary <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> -sequences. In this paper, we present a general formula for computing arithmetic autocorrelation, from which we completely determine the arithmetic autocorrelation distribution of arbitrary binary <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> -sequences. In particular, the conjecture raised by Z. Chen et al. is verified.

Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model

In recent years, we have witnessed the presence of point cloud data in many aspects of our life, from immersive media, autonomous driving to healthcare, although at the cost of a tremendous amount of data. In this paper, we present an efficient lossless point cloud compression method that uses sparse tensor-based deep neural networks to learn point cloud geometry and color probability distributions. Our method represents a point cloud with both occupancy feature and three attribute features at different bit depths in a unified sparse representation. This allows us to efficiently exploit feature-wise and point-wise dependencies within point clouds using a sparse tensor-based neural network and thus build an accurate auto-regressive context model for an arithmetic coder. To the best of our knowledge, this is the first learning-based lossless point cloud geometry and attribute compression approach. Compared with the-state-of-the-art lossless point cloud compression method from Moving Picture Experts Group (MPEG), our method achieves 22.6% reduction in total bitrate on a diverse set of test point clouds while having 49.0% and 18.3% rate reduction on geometry and color attribute component, respectively.

Separate and Combined Effective Coding of Bit Planes of Grayscale Images

Currently, an approach involving a coder with a combined structure for compressing images combining several different coders, the system for connecting them to various bit planes, and the control system for these connections have not been studied. Thus, there is a need to develop a structure and study the effectiveness of a combined codec for compressing images of various types without loss in the spatial domain based on arithmetic and (Run-Length Encoding) RLE-coding algorithms. The essence of separate effective coding is to use independent coders of the same type or one coder connected to the planes alternately in order to compress the higher and lower bit planes of the image or their combinations. In this paper, the results of studying the effectiveness of using a combination of arithmetic and RLE coding for several types of images are presented. As a result of developing this structure, the effectiveness of combined coding for compressing the differences in the channels of hyperspectral images (HSI) has been established, as hyperspectral images consist of multi-spectral bands, instead of just the typical three bands (RGB) or (YCbCr) found in regular images. Where, each pixel in a hyperspectral image represents the entire spectrum of light reflected by the object or scene at that particular location.

Data Compression in LoRa Networks: A Compromise between Performance and Energy Consumption

The Internet of Things (IoT) end devices have major limitations related to hardware and energy autonomy. Generally, the highest energy consumption is related to communication, which accounts for up to 60% of consumption depending on the application. Among the strategies to optimize the energy consumed by communication, data compression methods are one of the most promising. However, most data compression algorithms are designed for personal computers and need to be adapted to the IoT context. This study aims to adapt classical algorithms, such as LZ77, LZ78, LZW, Huffman, and Arithmetic coding, and to analyse their performance and energy metrics in IoT end devices. The evaluation is performed in a device with an ESP32 processor and LoRa modulation. The study makes use of real datasets derived from two IoT applications. The results show compression rates close to 70%, a three-fold increase in the number of messages sent, and a reduction in energy consumption of 22%. An analytical model was also developed to estimate the gain in the battery life of the device using the adapted algorithms.

LL‐FMC: Low‐latency frame memory compression scheme with high reconstructed quality

AbstractFrame memory compression (FMC) saves power by reducing the data bandwidth for accessing reference frames of video coding standards in the external memory. However, since the continuously accumulated data loss degrades the performance of video coding standards, the quality of the reconstructed reference frame must be improved. Previous lossy frame memory compression methods focused on processing speed or simplicity, but their performance remains limited in terms of the quality of the reconstructed frame. In this study, a Huffman coding with a fixed codebook designed to considerably reduce the latency caused by arithmetic coding in lossless compression schemes is proposed. A novel lossy frame memory compression technique based on the Hadamard transform that does not require additional memory access using a lookup table is also proposed. The result of experiments on several reconstructed videos preprocessed with HEVC show that the proposed method improves BDPSNR by 10.7 dB and BDBR by 40.4% compared with the DWT+SPIHT‐based compression technique which is widely used for lossy frame memory compression.

Adaptive integer arithmetic coding with RLE-transform

The paper proposes a method of multilevel selective processing to increase the availability of information about critical infrastructure objects with a given level of reliability and confidentiality. This technology is based on the identification of key information at several stages of processing and adaptation of the RLE algorithm and integral arithmetic coding into a new input data structure. Thus, the last stage of processing video data by the proposed approach is considered, which is aimed at reducing the volume with the following features: further development of the method of linearization of two-dimensional transforms based on zig-zag scanning is obtained. The differences of the method lie in carrying out the vector intertransform zigzag linearization taking into account the selection of spectral components, which is defined as complementary. For the first time, a method of decomposition of a linearized transform based on the definition of a threshold has been developed. Characteristic features of the method: the threshold is found taking into account the presence in the group of different types of transformants to determine the total uneven number of non-equilibrium complementary components; the integral arithmetic coding has been improved based on taking into account the frequencies of dictionary elements (two-dictionary integral arithmetic coding). Distinctive features of the method: determination of the current code components by the decomposed working interval, depending on the capacity of the dictionaries of significant elements and the number of repetitions. This allows additionally taking into account the statistical features of the components of the RLE-structured linearized transform and reducing the length of the arithmetic code; For the first time, a transformant compression technology was created based on the reduction of various types of redundancy in groups of transformants. Characteristic features of the technology: RLE-structuring of the non-uniform complementary part of the group of transforms after their vector linearization and decomposing; the formation of two arithmetic codes according to two dictionary principles of uneven division of the working interval, depending on the type of transformants in the group.

Common lossless compression algorithms and their error resiliency performance

With the development of communication technology and computer technology, many related industries, such as multimedia entertainment, put forward higher requirements for storing and transmitting information data. The research of data compression technology has attracted more and more attention. Therefore, the error resiliency ability of data compression algorithm is particularly important. How to enhance the error resiliency of data compression communication systems has been a hot topic for researchers. This paper mainly introduces the lossless data compression technology and its basic principle and performance index. Two typical lossless compression codes, Huffman and Arithmetic coding are deeply studied, including the principle of coding and the problem of error resiliency. Huffman coding and Arithmetic coding are two very important lossless compression codes widely used. The ability to resist channel error is an important index for data compression in communication. It is of great significance to further improve the channel adaptability of data compression to study the above two kinds of codes and their ability to resist channel error.

Application of Principal Component Analysis to advancing digital phenotyping of plant disease in the context of limited memory for training data storage

Despite its widespread employment as a highly efficient dimensionality reduction technique, limited research has been carried out on the advantage of Principal Component Analysis (PCA)–based compression/reconstruction of image data to machine learning-based image classification performance and storage space optimization. To address this limitation, we designed a study in which we compared the performances of two Convolutional Neural Network-Random Forest Algorithm (CNN-RF) guava leaf image classification models developed using training data from a number of original guava leaf images contained in a predefined amount of storage space (on the one hand), and a number of PCA compressed/reconstructed guava leaf images contained in the same amount of storage space (on the other hand), on the basis of four criteria – Accuracy, F1-Score, Phi Coefficient and the Fowlkes–Mallows index. Our approach achieved a 1:100 image compression ratio (99.00% image compression) which was comparatively much better than previous results achieved using other algorithms like arithmetic coding (1:1.50), wavelet transform (90.00% image compression), and a combination of three transform-based techniques – Discrete Fourier (DFT), Discrete Wavelet (DWT) and Discrete Cosine (DCT) (1:22.50). From a subjective visual quality perspective, the PCA compressed/reconstructed guava leaf images presented almost no loss of image detail. Finally, the CNN-RF model developed using PCA compressed/reconstructed guava leaf images outperformed the CNN-RF model developed using original guava leaf images by 0.10% accuracy increase, 0.10 F1-Score increase, 0.18 Phi Coefficient increase and 0.09 Fowlkes–Mallows increase.

A Highly Pipelined and Highly Parallel VLSI Architecture of CABAC Encoder for UHDTV Applications.

Recently, specifically designed video codecs have been preferred due to the expansion of video data in Internet of Things (IoT) devices. Context Adaptive Binary Arithmetic Coding (CABAC) is the entropy coding module widely used in recent video coding standards such as HEVC/H.265 and VVC/H.266. CABAC is a well known throughput bottleneck due to its strong data dependencies. Because the required context model of the current bin often depends on the results of the previous bin, the context model cannot be prefetched early enough and then results in pipeline stalls. To solve this problem, we propose a prediction-based context model prefetching strategy, effectively eliminating the clock consumption of the contextual model for accessing data in memory. Moreover, we offer multi-result context model update (MCMU) to reduce the critical path delay of context model updates in multi-bin/clock architecture. Furthermore, we apply pre-range update and pre-renormalize techniques to reduce the multiplex BAE's route delay due to the incomplete reliance on the encoding process. Moreover, to further speed up the processing, we propose to process four regular and several bypass bins in parallel with a variable bypass bin incorporation (VBBI) technique. Finally, a quad-loop cache is developed to improve the compatibility of data interactions between the entropy encoder and other video encoder modules. As a result, the pipeline architecture based on the context model prefetching strategy can remove up to 45.66% of the coding time due to stalls of the regular bin, and the parallel architecture can also save 29.25% of the coding time due to model update on average under the condition that the Quantization Parameter (QP) is equal to 22. At the same time, the throughput of our proposed parallel architecture can reach 2191 Mbin/s, which is sufficient to meet the requirements of 8 K Ultra High Definition Television (UHDTV). Additionally, the hardware efficiency (Mbins/s per k gates) of the proposed architecture is higher than that of existing advanced pipeline and parallel architectures.

FLoCIC: A Few Lines of Code for Raster Image Compression.

A new approach is proposed for lossless raster image compression employing interpolative coding. A new multifunction prediction scheme is presented first. Then, interpolative coding, which has not been applied frequently for image compression, is explained briefly. Its simplification is introduced in regard to the original approach. It is determined that the JPEG LS predictor reduces the information entropy slightly better than the multi-functional approach. Furthermore, the interpolative coding was moderately more efficient than the most frequently used arithmetic coding. Finally, our compression pipeline is compared against JPEG LS, JPEG 2000 in the lossless mode, and PNG using 24 standard grayscale benchmark images. JPEG LS turned out to be the most efficient, followed by JPEG 2000, while our approach using simplified interpolative coding was moderately better than PNG. The implementation of the proposed encoder is extremely simple and can be performed in less than 60 lines of programming code for the coder and 60 lines for the decoder, which is demonstrated in the given pseudocodes.

Weighted Burrows–Wheeler Compression

A weight-based dynamic compression method has recently been proposed, which is especially suitable for the encoding of files with locally skewed distributions. Its main idea is to assign larger weights to closer to be encoded symbols by means of an increasing weight function, rather than considering each position in the text evenly. A well known transformation that tends to convert input files into files with a more skewed distribution is the Burrows–Wheeler Transform (BWT). This paper proposes to apply the weighted approach on Burrows–Wheeler transformed files. While it is shown that the compression performance is not altered for static and adaptive arithmetic coding by any permutation of the symbols, hence in particular for BWT, empirical evidence of the efficiency of the combination of BWT with the weighted approach is provided.

VLSI Design of a High-Performance Multicontext MQ Arithmetic Coder

The MQ arithmetic coding, which is an adaptive arithmetic coding developing from Q coding, has become a major throughput bottleneck of JPEG2000 compression due to its inherent serial operations. To overcome the bottleneck, this brief proposes a high-performance hardware architecture of the multicontext MQ coder. The proposed architecture is capable of concurrent coding for two adjacent more probable symbols (MPSs). Performance analysis results show that the proposed coder consumes 1.61 CXD pairs per cycle and achieves a throughput of 506.93 MSymbols/s under 0.5 bpp bit rate. The proposed architecture not only achieves high throughput, but also maintains both low hardware utilization and low power consumption. Compared with the state-of-the-art two-context coder, the figure of merit (FoM) is increased by 38%. Compared with the single-context coder, the power-delay product (PDP) is reduced by 49%.

Non-Decimated Wavelet Transform and Vector Quantization for Lossy Medical Images Compression

This study presents a new approach for lossy medical image compression using vector quantization. Recently, the digital image has been a reliable replacement for a hard copy of medical images, therefore, an effort has been made to ensure maintaining high-quality images to use for archiving, classification, or automated diagnostics support. Although the medical application contains all sorts of the images like microscopic, X-rays, tomography, and fiber optics imaging by angioplasty, all of this comes at the cost of using digital storage that needs to be regularly backed up and maintained and to help minimize the need for larger storage media, this study is focusing on applying Non-Decimated Wavelet Transform (NDWT) and combined lossy and lossless compression techniques that will allow the images to take much smaller storage space while maintaining the high level of quality for these images. This study is focusing on chest X-ray images compression using a combination of lossy compression techniques using two Vector Quantization (VQ) algorithms such as k-means clustering and Linde, Buzo, and Gray (LBG) algorithm, and three lossless compression techniques such as Arithmetic Coding (AC), Run Length Encoding (RLE) and Huffman Coding (HC) and choose the optimum combination of them. Then, the performance is measured using Compression Ratio (CR), processing time, or called run time, Peak Signal to Noise Ratio (PSNR), and Bit Rate.

Fair Numerical Algorithm of Coset Cardinality Spectrum for Distributed Arithmetic Coding.

As a typical symbol-wise solution of asymmetric Slepian-Wolf coding problem, Distributed Arithmetic Coding (DAC) non-linearly partitions source space into disjoint cosets with unequal sizes. The distribution of DAC coset cardinalities, named the Coset Cardinality Spectrum (CCS), plays an important role in both theoretical understanding and decoder design for DAC. In general, CCS cannot be calculated directly. Instead, a numerical algorithm is usually used to obtain an approximation. This paper first finds that the contemporary numerical algorithm of CCS is theoretically imperfect and does not finally converge to the real CCS. Further, to solve this problem, we refine the original numerical algorithm based on rigorous theoretical analyses. Experimental results verify that the refined numerical algorithm amends the drawbacks of the original version.

Experimental lossless data compressor

x3 is a lossless optimizing dictionary-based data compressor. The algorithm uses a combination of a dictionary, context modeling, and arithmetic coding. Optimization adds the ability to find the most appropriate parameters for each file. Even without optimization, x3 can compress data with a compression ratio comparable to the best dictionary compression methods like LZMA, zstd, or Brotli. Our work differs from others by explicitly inserting phrases into the dictionary. This is a gap not yet covered by any other compression method.

DNA-Aeon provides flexible arithmetic coding for constraint adherence and error correction in DNA storage

The extensive information capacity of DNA, coupled with decreasing costs for DNA synthesis and sequencing, makes DNA an attractive alternative to traditional data storage. The processes of writing, storing, and reading DNA exhibit specific error profiles and constraints DNA sequences have to adhere to. We present DNA-Aeon, a concatenated coding scheme for DNA data storage. It supports the generation of variable-sized encoded sequences with a user-defined Guanine-Cytosine (GC) content, homopolymer length limitation, and the avoidance of undesired motifs. It further enables users to provide custom codebooks adhering to further constraints. DNA-Aeon can correct substitution errors, insertions, deletions, and the loss of whole DNA strands. Comparisons with other codes show better error-correction capabilities of DNA-Aeon at similar redundancy levels with decreased DNA synthesis costs. In-vitro tests indicate high reliability of DNA-Aeon even in the case of skewed sequencing read distributions and high read-dropout.

Tree Social Relations Optimization-Based ReLU-BiLSTM Framework for Improving Video Quality in Video Compression

High-Efficiency Video Coding (HEVC) has a higher coding efficiency, its encoding performance must be increased to keep up with the expanding number of multimedia applications. Therefore, this paper proposes a novel Rectified Linear Unit-Bidirectional Long Short-Term Memory-based Tree Social Relations Optimization (ReLU-BiLSTM-based TSRO) method to enhance the quality of video transmission. The significant objective of our proposed method aims in enhancing the standards of entropy encoding process in HEVC. Here, context-adaptive binary arithmetic coding (CABAC) framework which is prevalent and an improved form of entropy coding model is utilized in HEVC standards. In addition to this, the performances of the proposed method are determined by evaluating various measures such as mean square error, cumulative distribution factor, compression ratio, peak signal-to-noise ratio (PSNR) and bit error rate. Finally, the proposed method is examined with five different sequences of video from football, tennis, garden, mobile and coastguard. The performances of the proposed method are compared with various approaches, and the result analysis shows that the proposed method attained minimum mean square error (MSE) loss with maximum PSNR rate.

Using Four Hypothesis Probability Estimators for CABAC in Versatile Video Coding

This article introduces the key technologies involved in four hypothetical probability estimators for Context-based Adaptive Binary Arithmetic Coding (CABAC). The focus is on the selected adaptation rate performed in these estimators, which are selected based on coding efficiency and memory considerations, and also the relationship with the current size of the coding block. The proposed scheme can linearly realize the quantitative representation of probabilistic prediction and describes the scalability potential for higher accuracy. Besides a description of the design concept, this work also discusses motivation and implementation aspects, which are based on simple operations such as bitwise operations and single subsampling for subinterval updates. The experimental results verify the effectiveness of the proposed CABAC method specified in Versatile Video Coding (VVC).