Code Compression Research Articles

One DAG to Rule Them All.

In this paper, we present novel strategies for optimizing the performance of many binary image processing algorithms. These strategies are collected in an open-source framework, GRAPHGEN, that is able to automatically generate optimized C++ source code implementing the desired optimizations. Simply starting from a set of rules, the algorithms introduced with the GRAPHGEN framework can generate decision trees with minimum average path-length, possibly considering image pattern frequencies, apply state prediction and code compression by the use of Directed Rooted Acyclic Graphs (DRAGs). Moreover, the proposed algorithmic solutions allow to combine different optimization techniques and significantly improve performance. Our proposal is showcased on three classical and widely employed algorithms (namely Connected Components Labeling, Thinning, and Contour Tracing). When compared to existing approaches -in 2D and 3D-, implementations using the generated optimal DRAGs perform significantly better than previous state-of-the-art algorithms, both on CPU and GPU.

Effective compression and decompression coding techniques using multilevel DWT decomposition and DCT

Effective compression and decompression coding techniques using multilevel DWT decomposition and DCT

Analysis of the impact of data compression on condition monitoring algorithms for ball screws

The overall equipment effectiveness (OEE) is a management ratio to evaluate the added value of machine tools. Unplanned machine downtime reduces the operational availability and therefore, the OEE. Increased machine costs are the consequence. An important cause of unplanned machine downtimes is the total failure of ball screws of the feed axes due to wear. Therefore, monitoring of the condition of ball screws is important. Common concepts rely on high-frequency acceleration sensors from external control systems to detect a change of the condition. For trend and detailed damage analysis, large amounts of data are generated and stored over a long time period (>5 years), resulting in corresponding data storage costs. Additional axes or machine tools increase the data volume further, adding to the total storage costs. To minimize these costs, data compression or source coding has to be applied. To achieve maximum compression ratios, lossy coding algorithms have to be used, which introduce distortion in a signal. In this work, the influence of lossy coding algorithms on a condition monitoring algorithm (CMA) using acceleration signals is investigated. The CMA is based on principal component analysis and uses 17 features such as standard deviation to predict the preload condition of a ball screw. It is shown that bit rate reduction through lossy compression algorithms is possible without affecting the condition monitoring - as long as the compression algorithm is known. In contrast, an unknown compression algorithm reduces the classification accuracy of condition monitoring by about 20 % when coding with a quantizer resolution of 4 bit/sample.

Design and Implementation of Lossless Compression System for CCSDS Hyperspectral Images

Based on the xc7k325tffg900 FPGA chip of Xilinx Company and combining the actual hyperspectral image compression task requirements, the calculating speed and compression efficiency of the algorithm module are optimized. In the design of the prediction module in this article, the two-clock solution is used to solve the problem that the algorithm speed is restricted due to the feedback of the weight update. And the RTL-level design is implemented on the FPGA. CCSDS 123.0-B-1 hyperspectral image lossless compression algorithm is achieved. The system uses block compression to deal with the data source to avoid the entire system compression data errors caused by a certain pixel compression error. So the system’s Error Resilience ability is improved. Finally the compressed code stream is decoded. The decompressed image is consistent with the raw one, which verifies the correctness of this design.

Non-Degraded Adaptive HEVC Steganography by Advanced Motion Vector Prediction

Current video steganography operates with either the decoded frame images or the compression coding parameters, which could cause quality degradation of the reconstructed frames. In this letter, by exploiting the advanced motion vector prediction (AMVP) technique of High Efficiency Video Coding (HEVC) standard, we propose a non-degraded adaptive steganographic approach for H.265/HEVC videos. The index value in the candidate list of the prediction unit (PU) is used for embedding. Experimental results demonstrate the superiority of the proposed steganographic approach against both hand-crafted feature-based and deep learning network-based steganalytic detectors. Our work explores a new embedding space that is not previously studied. It is a significant development in finding new ways to escape from video quality change-based steganalysis.

A highly efficient chain code for compression using an agent-based modeling simulation of territories in biological beavers

The accelerated developments in technology led to a tremendous increase in the volumes of data to be transferred and exchanged between various network channels. These advancements create a huge demand for researchers to investigate new data compression techniques. Recent evidence from the literature shows that agent-based modeling is a promising direction to reduce the size of the data and change its original representation. In this article, the objective is to build an agent-based modeling simulation for chain coding and take advantage of it in data compression. Our agent-based model is inspired by the concept of defended territories of biological beavers. To this end, we use the pixel distribution in a bi-level image to construct a virtual environment of agents, add the beavers, and build territories around them. The main idea of defended beaver territories is to allow each beaver to maintain its area and protects it from intruders. To put it another way, defended territories allow beavers to work on different parts of an image while the algorithm tracks and records their movements, as well as manages disputes between them. Our research findings represent a further step towards employing the generated codes of movements in image processing operations other than coding and compression. Additionally, the experimental results showed that the current model was prosperous, and it could outperform many existing image compression techniques, including JBIG family methods. What’s more, paired-samples t-tests reveal that the mean differences between the outcomes of the current approach and each of the other standardized benchmarks we employed in comparison are statistically significant.

Hierarchical Predictive Coding-Based JND Estimation for Image Compression.

The human visual system (HVS) is a hierarchical system, in which visual signals are processed hierarchically. In this paper, the HVS is modeled as a three-level communication system and visual perception is divided into three stages according to the hierarchical predictive coding theory. Then, a novel just noticeable distortion (JND) estimation scheme is proposed. In visual perception, the input signals are predicted constantly and spontaneously in each hierarchy, and neural response is evoked by the central residue and inhibited by surrounding residues. These two types' residues are regarded as the positive and negative visual incentives which cause positive and negative perception effects, respectively. In neuroscience, the effect of incentive on observer is measured by the surprise of this incentive. Thus, we propose a surprise-based measurement method to measure both perception effects. Specifically, considering the biased competition of visual attention, we define the product of the residue self-information (i.e., surprise) and the competition biases as the perceptual surprise to measure the positive perception effect. As for the negative perception effect, it is measured by the average surprise (i.e., the local Shannon entropy). The JND threshold of each stage is estimated individually by considering both perception effects. The total JND threshold is finally obtained by non-linear superposition of three stage thresholds. Furthermore, the proposed JND estimation scheme is incorporated into the codec of Versatile Video Coding for image compression. Experimental results show that the proposed JND model outperforms the relevant existing ones, and over 16% of bit rate can be reduced without jeopardizing the perceptual quality.

Adaptive Compressive Video Coding for Embedded Camera Sensors: Compressed Domain Motion and Measurements Estimation

This paper presents a new framework for wireless video streaming services using the resource-constrained embedded camera sensors based on block compressive sensing. We propose adaptive encoding scheme that exploits high redundancy between successive video frames to reduce transmission cost while effectively handling occlusion effects and, at the same time, maintains the simple and energy conserving encoder design. The proposed methodology adapts the compression ratio for different blocks of the non-key frame depending on temporal correlation. We also propose compressed domain motion and measurements estimation techniques to exploit the high correlation between successive frames at the decoder. The proposed motion estimation technique makes use of restricted isometry property of the sensing matrix to seek the best matching measurement vector for motion estimation as opposed to block matching in conventional video coding. In the proposed measurement estimation technique, efficient utilization of bandwidth is achieved by skipping some measurements at the transmitter side. The skipped measurements are estimated at the receiver by exploiting the correlation between CS measurements of the non-key frame and corresponding motion predicted frame using multiple regression model. Extensive simulation results on a set of diverse video sequences are presented to demonstrate the effectiveness of the proposed video coding technique.

Projection-based Occupancy Map Coding for 3D Point Cloud Compression

An occupancy map coding method to compress 3D point cloud data is proposed. A 3D point cloud can be coded by projecting into a texture, depth, and occupancy map. This paper presents an efficient way to signal consecutive fully occupied occupancy coding units with a single bit for each coding unit row of an object in the occupancy map is presented. The results showed that the proposed algorithm could save 3.02% of bits without coding loss compared to an existing algorithm.

A Brief Survey of Fixed-Parameter Parallelism

This paper provides an overview of the field of parameterized parallel complexity by surveying previous work in addition to presenting a few new observations and exploring potential new directions. In particular, we present a general view of how known FPT techniques, such as bounded search trees, color coding, kernelization, and iterative compression, can be modified to produce fixed-parameter parallel algorithms.

Using Black-Box Compression Algorithms for Phase Retrieval

Compressive phase retrieval refers to the problem of recovering a structured $n$ -dimensional complex-valued vector from its phase-less under-determined linear measurements. The non-linearity of the measurement process makes designing theoretically-analyzable efficient phase retrieval algorithms challenging. As a result, to a great extent, existing recovery algorithms only take advantage of simple structures such as sparsity and its convex generalizations. The goal of this article is to move beyond simple models through employing compression codes. Such codes are typically developed to take advantage of complex signal models to represent the signals as efficiently as possible. In this work, it is shown how an existing compression code can be treated as a black box and integrated into an efficient solution for phase retrieval. First, COmpressive PhasE Retrieval (COPER) optimization, a computationally-intensive compression-based phase retrieval method, is proposed. COPER provides a theoretical framework for studying compression-based phase retrieval. The number of measurements required by COPER is connected to $\kappa $ , the $\alpha $ -dimension (closely related to the rate-distortion dimension) of a given family of compression codes. To finds the solution of COPER, an efficient iterative algorithm called gradient descent for COPER (GD-COPER) is proposed. It is proven that under some mild conditions on the initialization and the compression code, if the number of measurements is larger than $ C \kappa ^{2} \log ^{2}~n$ , where $C$ is a constant, GD-COPER obtains an accurate estimate of the input vector in polynomial time. In the simulation results, JPEG2000 is integrated in GD-COPER to confirm the state-of-the-art performance of the resulting algorithm on real-world images.

Caesar Cipher with Goldbach Code Compression for Efficient Cryptography

Caesar Cipher with Goldbach Code Compression for Efficient Cryptography

CID: Co-Architecting Instruction Cache and Decompression System for Embedded Systems

Code compression is widely used to reduce the footprint of code memory in cost-sensitive embedded systems. However, despite the small code size, the decompressor and the address translator required to support the code compression incur energy and area overheads. To reduce such overheads while still supporting code compression, we co-architect the instruction cache and decompression system (CID). In CID, each component is placed at the optimal location and the instruction cache is redesigned to recognize the compression state and retain the original address, through the cache division and address space decompression process. As a result of the cache division, the energy consumption and area overheads of the CID instruction cache are reduced. Since the decompressor overhead depends on the code compression technique, we propose a new code compression technique called entropy-based pattern code compression, which reduces overheads of the decompressor. Our experimental results show that the total energy consumption of the instruction cache and decompression system is reduced by up to 29.7 percent and their area is reduced by up to 15.4 percent compared to the post-cache architecture with almost no performance degradation, while achieving an 18.8 percent improvement in the compression ratio compared to the state-of-the-art code compression technique.

An optimal bit-rate allocation algorithm to improve transmission efficiency of images in deep space exploration

As fixed compression ratio is used in traditional deep space exploration image transmission application, the same compression code rate is allocated to each image. However, since the information of each image in a space observation mission is nonuniform, the image with more information will inevitably lead to more compression distortion than the image with less information. Obviously, it's not an efficient way to transmit information in terms of data importance or overall distortion. Therefore, we proposed a combinatorial optimal bit rate allocation algorithm to improve the efficiency of image transmission in space application. Different from traditional method, the rate-distortion model of wavelet coefficients for each image in a transmission task was built, and under the overall maximum transmission rate constraint, an bit-rate optimal allocation was applied for each image to minimize the overall distortion of a batch of images. The proposed algorithm can be widely used in image compression algorithm with embedded code stream characteristics, such as JPEG2000 and SPIHT. Experimental results shows that in the range of 2.6 to 10 compression ratio, the algorithm can reduce image distortion MSB by 40%∼64% at the same overall transmission code rate, which equivalent to improvement of PSNR 3 dB to 5.4 dB.

METAGRAMMATICAL MODEL AND METHOD OF DECODING THE VIDEOGRAPHICAL INFORMATION WITH PREFIX COMPRESSION CODES

In the article, a metagrammatic model and the local-frequency decoding method are proposed to solve the problem of decoding videographic information with prefix compression codes. The metagrammatic model is presented as a five – level stochastic metagrammatics that allows us to take into account the structural and statistical features of prefix coded data and use them to improve the decoding efficiency. It is shown that only regular Chomsky grammars can be used in the proposed stochastic metagrammatics. The model uses a combined metagrammatics scheme with matching rules such as “terminal – initial symbol” (TS) and “terminal – product label” (TP). It is shown that structuring the model in the form of stochastic metagrammatics can significantly reduce the total number of products in metagrammatics and simplify its modification when solving problems of decoding promising prefix compression codes. The features of the local-frequency method of decoding videographic information with prefix compression codes focused on this metagrammatic model are considered. To implement one of the main and time-consuming procedures of this method, related to the search in local code tables and translation of uneven prefix codes, a local-private procedure for searching code words is proposed. The features of the software implementation of the proposed method are considered. The results of experimental testing of the method on the example of real arrays of non-uniform prefix codes are presented.

Preconditioning methods for compressible flow CFD codes: Revisited

The present article aims to implement and investigate a different preconditioning method based in a three-dimensional in-house compressible CFD code that ensures the robustness and numerical stability to determine the flowfield considering low Mach number flow. The present preconditioning method involve two different methodologies developed by references [8, 32]. The CFD solver was developed to calculate the Euler and Navier-Stokes equations, numerically, for steady-state regime based on the cell-centered finite volume method (FVM) using Reynolds Averaged Navier-Stokes equations (RANS). The centered second-order scheme was used for the discretization of convective terms from momentum equations. The explicit second-order five-step Runge-Kutta scheme was employed for the time-marching procedure, using an implicit residual smoothing technique to enhance the numerical stability. A local preconditioning method was implemented due to its robustness in predicting low Mach number flows in a compressible CFD code environment. However, for low Mach number flows, near stagnation points, numerical perturbations were amplified generating a stiffness in the convergence rate, which provided an inaccurate solution and numerical stability degradation. Aiming to improve the preconditioning robustness, a flux function and a new limiter were applied to operate with the preconditioning technique based on a pressure sensor. Those corrections re-scale the eigenvectors and ensure the locality of the algorithm, which improves the numerical stability and guarantees the convergence for low-speed flows. The inviscid flow over a NACA 0012 airfoil for compressible and incompressible cases shown accurate and robust solutions. For a viscous flow over a flat plate case in the compressible and incompressible cases, the preconditioning technique purposed in this work supplied good numerical solution in agreement with the analytical solution.

A Multiparametric Class of Low-complexity Transforms for Image and Video Coding

Discrete transforms play an important role in many signal processing applications, and low-complexity alternatives for classical transforms became popular in recent years. Particularly, the discrete cosine transform (DCT) has proven to be convenient for data compression, being employed in well-known image and video coding standards such as JPEG, H.264, and the recent high efficiency video coding (HEVC). In this paper, we introduce a new class of low-complexity 8-point DCT approximations based on a series of works published by Bouguezel, Ahmed and Swamy. Also, a multiparametric fast algorithm that encompasses both known and novel transforms is derived. We select the best-performing DCT approximations after solving a multicriteria optimization problem, and submit them to a scaling method for obtaining larger size transforms. We assess these DCT approximations in both JPEG-like image compression and video coding experiments. We show that the optimal DCT approximations present compelling results in terms of coding efficiency and image quality metrics, and require only few addition or bit-shifting operations, being suitable for low-complexity and low-power systems.

Non-Linear Data Structure for Data Coding for Size Compression

Data compression is a special case of communication system which is a basic problem in engineering and computer science. In this work, the new scheme has been introduced for data coding to compress the size. Compression by using one type of Non-linear Data-Structure which is Prefix Binary Tree data compression is essential and beneficial in the diversity of application which is embraced in general into two branches: the storage and transmission.the storage of a file in a compressed form takes fewer bit than in uncompressed form, therefore make the most of storage resources (containing main memory itself), and by transmission means sending the files over a channel with reduced number of bits by compression process. This leads to more efficacious transmission process. The compression process of stored data or that to be transmitted leads to significantly reducing both of the storage and transmission. We use text type as a data in the experiments. The results show the compression rate is reduce the actual size above 50%.

RETRACTED ARTICLE: Divide and compress discrete cosine lossless compression coder to reduce dimensionality of test data

The increasing test data volume is considered as a biggest challenge in circuit under test. This challenge leads to higher computational complexity associated with the testing circuits. To reduce the testing time and addresses the problem related to high overhead, high fault coverage andincreased power dissipationmethod is used in system-on-chip (SoC). A new test compression method called divide and compress (D&C) lossless compression coder with Discrete Cosine Transformis used to enhance the compression capability. This framework of D&C lossless compression coder combines the lossless and lossy compression methods. The Discrete Cosine Transform (DCT) manipulates each data bit inside file and this reduces the file size without any data loss after decoding to lossless compression.Depending on the data characteristic, the compression of data is done without any loss in data. Further, the decompression is attained using advanced Finite State Machine (FSM) to reduce overhead. The experimental results on large benchmark circuits like ITC’99 and ISCAS’99 proves that the proposed D&C lossless compression attains reduced testing time and better compression ratio. Further, itlays only soft burden on the hardware.

Numerical investigations of interface instability and turbulent mixing driven by implosion

The interface instability and turbulent mixing of perturbed Air/SF6 interface driven by implosion in spherical convergent geometry are numerically investigated by the use of in-house compressible hydrodynamic code (multi-viscous flow and turbulence). The results revealed the complex evolving laws and physical mechanisms of interface instability and turbulent mixing due to the evolution of complex waves in this case. The RM instability is induced when the perturbed Air/SF6 interface is driven by the incident shock wave. And the perturbed interface accelerates toward the center, thereafter RT instability occurs. Then the perturbed interface slows down and decelerates towards the center, and the RT stabilization appears and suppresses the development of the interface instability and turbulent mixing. When the RT stabilization acts an absolutely dominant role, the growth of the turbulent mixing zone (TMZ) width is restrained completely, and its width in turn reduces. After the rebound of transmitted shock waves, the secondary loading from heavy fluid to light fluid is a combination of quasi-isentropic ramp wave, shock wave and Taylor wave in time series. The loading of quasi-isentropic ramp wave also contributes to the RT stabilization mechanism, the drive of rebound shock wave was seen to have induced the RM instability too, and the impact of Taylor wave resulted in RT instability once again. In the following, the reflected wave moves toward the center and bounces, the third loading process is the same as the secondary one; the TMZ width repeats the same growth laws. The Bell-Plesset (BP) effect which belongs to the conergent geometry can promote the development of TMZ. In the spherical converegent geometry, the competition mechanism among the RM instability, RT instability, BP effect and RT stabilization controls the evolution of interface instability and turbulent mixing. The distributions of turbulent kinetic energy indicate that TMZ evolves asymmetrically along the radial direction. The distributions of three direction components of turbulent kinetic energy and energy spectra also exhibit the evolution of TMZ as strong anisotropy.