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Real-Time Lossless compression for Ultrahigh-Density Synchrophasor and Point-on-Wave Data

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第2期69卷 2012-2021页

作者： Chen, Chang Wang, Weikang Yin, He Zhan, Lingwei Liu, Yilu Univ Tennessee Knoxville TN 37996 USA Oak Ridge Natl Lab Oak Ridge TN 37831 USA

Modern advanced phasor measurement units are developed with ultrahigh reporting rates to meet the demand for monitoring the power systems dynamics in detail. Due to the large volume of data, the communication and storage systems are seriously challenged with the presence of ultrahigh-density (UHD) synchrophasor and point-on-wave (POW) data. Therefore, it is an urgent task to compress the UHD data for more efficient communication and data storage. This article proposes several methods to compress the synchrophasor and POW data in a lossless manner. First, an improved time-series special compression (ITSSC) method is proposed to compress the UHD frequency data. Second, a delta-difference Huffman method is combined with the time-series special compression algorithm to compress the UHD phase angle data. Finally, a cyclical high-order delta modulation method is proposed to compress the UHD POW data. The proposed models are extensively tested and compared with different existing lossless compression algorithms using the field-collected synchrophasor and POW data at different reporting rates. The results indicate that the proposed algorithms are efficient in performing lossless compression for the UHD synchrophasor and POW data in real time.

关键词： Phasor measurement units Real-time systems Encoding Prediction algorithms Entropy compression algorithms Tools Huffman encoding method improved time-series special compression (ITSSC) method lossless data compression point-on-wave (POW) data synchrophasor data ultrahigh density

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Context-Tree-Based Lossy compression and Its Application to CSI Representation

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IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第7期70卷 4417-4428页

作者： Miyamoto, Henrique K. Yang, Sheng Paris Saclay Univ Cent Supelec Lab Signals & Syst L2S F-91190 Gif Sur Yvette France Univ Campinas Unicamp Inst Math Stat & Sci Comp IMECC BR-13083859 Campinas Brazil

We propose novel compression algorithms for time-varying channel state information (CSI) in wireless communications. The proposed scheme combines (lossy) vector quantisation and (lossless) compression. First, the new vector quantisation technique is based on a class of parametrised companders applied on each component of the normalised CSI vector. Our algorithm chooses a suitable compander in an intuitively simple way whenever empirical data are available. Then, the sequences of quantisation indices are compressed using a context-tree-based approach. Essentially, we update the estimate of the conditional distribution of the source at each instant and encode the current symbol with the estimated distribution. The algorithms have low complexity, are linear-time in both the spatial dimension and time duration, and can be implemented in an online fashion. We run simulations to demonstrate the effectiveness of the proposed algorithms in such scenarios.

关键词： Symbols Encoding Antenna arrays compression algorithms Vector quantization Correlation Codes Data compression MIMO systems vector quantisation

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Removing Data Dependencies in the CCSDS 123.0-B-2 Predictor Weight Updating

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Barrios, Yubal Bartrina-Rapesta, Joan Hernandez-Cabronero, Miguel Sanchez, Antonio Blanes, Ian Serra-Sagrista, Joan Sarmiento, Roberto Univ Las Palmas De Gran Canaria ULPGC Inst Appl Microelect IUMA Las Palmas Gran Canaria 35017 Spain Univ Autonoma Barcelona Dept Informat & Commun Engn Bellaterra 08193 Spain

The Consultative Committee for Space Data Systems (CCSDS) first standardized near-lossless coding capabilities in the CCSDS 123.0-B-2 algorithm. However, this standard does not describe strategies to produce high-throughput hardware implementations, which are not trivial to derive from its definition. At the same time, throughput optimizations without significant compression performance penalties are paramount to enable real-time compression on-board next-generation satellites. This work demonstrates that the weight update stage of the CCSDS 123.0-B-2 predictor can be selectively bypassed to enhance throughput for both lossless and near-lossless modes with minimal impact on compression performance and still produce fully compliant bitstreams. Skipping the weight update implies that those weights must be carefully chosen outside the original CCSDS 123.0-B-2 pipeline. Two strategies are proposed to select effective weight values based on whether a priori information about the current image is exploited or not. Comprehensive experimental results are presented for both proposed strategies and for lossless and near-lossless regimes, using a representative set of hyperspectral images. The coding penalty is, on average, 1% for lossless and 8% for near-lossless, depending on the strategy used to set the initial weights. The proposed method obtains a maximum throughput of one processed sample per clock cycle when it is evaluated using high-level synthesis (HLS), consuming 4.6% of the look-up tables (LUTs) and 31.1% of the internal memory on a Xilinx Kintex UltraScale space-grade field programmable gate array (FPGA).

关键词： compression algorithms Consultative Committee for Space Data Systems (CCSDSs) 1230-B-2 high throughput hyperspectral imaging onboard data processing

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Improved Step-Size Schedules for Proximal Noisy Gradient Methods

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 189-201页

作者： Khirirat, Sarit Wang, Xiaoyu Magnusson, Sindri Johansson, Mikael Royal Inst Technol KTH Div Decis & Control Syst S-11428 Stockholm Sweden Stockholm Univ Dept Comp & Syst Sci S-11419 Stockholm Sweden

Noisy gradient algorithms have emerged as one of the most popular algorithms for distributed optimization with massive data. Choosing proper step-size schedules is an important task to tune in the algorithms for good performance. For the algorithms to attain fast convergence and high accuracy, it is intuitive to use large step-sizes in the initial iterations when the gradient noise is typically small compared to the algorithm-steps, and reduce the step-sizes as the algorithm progresses. This intuition has been confirmed in theory and practice for stochastic gradient descent. However, similar results are lacking for other methods using approximate gradients. This paper shows that the diminishing step-size strategies can indeed be applied for a broad class of noisy gradient algorithms. Our analysis framework is based on two classes of systems that characterize the impact of the step-sizes on the convergence performance of many algorithms. Our results show that such step-size schedules enable these algorithms to enjoy the optimal rate. We exemplify our results on stochastic compression algorithms. Our experiments validate fast convergence of these algorithms with the step decay schedules.

关键词： Gradient methods compression algorithms convex functions machine learning neural networks

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A High-Speed, Multi-Channel Lossless compression Algorithm for High-Resolution Video on FPGA

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IEEE Access 2025年 13卷 95911-95921页

作者： Xingkai Du Longhua Xie Hongchuan Huang Huawei Chen Xiaolong Guo Tingyu Zhao Department of Physics Zhejiang Key Laboratory of Quantum State Control and Optical Field Manipulation Zhejiang Sci-Tech University Hangzhou China Hangzhou ToupTek Photonics Company Ltd. Hangzhou China

With the increasing image resolution, data bit depth, and frame rate of industrial cameras, the signal output speed needs to be improved to keep pace with the rapidly developing image sensor technologies. The USB 3.0 interface has become a common transmission method for industrial cameras due to its high versatility. However, limited bandwidth often requires data compression techniques to address transmission bottlenecks. This paper proposes a four-channel high-speed lossless compression algorithm based on FPGA (Field-Programmable Gate Array). The algorithm uses the difference between adjacent pixels, which has fewer data bit widths, to replace the original pixels for transmission. This approach enhances the transmission frame rate while maintaining image quality. Additionally, a novel “first-in, last-out” data inversion technique is employed to solve the problem of real-time processing of four-channel data, significantly reducing FPGA hardware resource usage. The performance of the proposed algorithm is evaluated through on-board FPGA experiments. When transmitting $4096\times 4096$ resolution video via USB 3.0, the frame rate reaches 18.3 fps with a data size reduced by up to 49.98% (corresponding to a compression ratio of 50.02%), with an encoding overhead delay of $20.76~\mu $ s, resulting in a frame rate increase of approximately 71.03%. Additionally, decompression speeds for a large resolution image set ( $5568\times 3712$ ) are improved by at least 40% compared to mainstream algorithms. The algorithm demonstrates low FPGA resource utilization and high processing speed, ensuring efficient compression and transmission even for complex image scenes. These results provide a viable high-speed lossless compression solution for industrial camera applications.

关键词： Image coding Kernel Field programmable gate arrays Cameras Calibration Streaming media compression algorithms Universal Serial Bus Image resolution Real-time systems

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SortComp (Sort-and-Compress) - Towards a Universal Lossless compression Scheme for Matrix and Tabular Data

SortComp (Sort-and-Compress) - Towards a Universal Lossless ...

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Data compression Conference (DCC)

作者： Cheng, Xizhe Lin, Sian-Jheng Sun, Jie Huawei Technol Co Ltd Hong Kong R&D Ctr Hong Kong Peoples R China

ISBN: (纸本)9781665478939

A universal scheme is proposed for the lossless compression of two-dimensional tables and matrices. Instead of standard row- or column-based compression, we propose to sort each column first and record both the sorted table and the corresponding permutation table of the sorting permutations. These two tables are then separately compressed. In this new scheme, both intra- and inter-column correlations can be efficiently captured, giving rise to improved compression ratio in particular when both column-wise and row-wise dependencies cooccur. This scheme reduces the problem of the compression of an arbitrary two-dimensional table to that of a 'permutation table' together with a 'sorted table', where the former is only dependent on the table dimension and the latter can be effectively compressed column-by-column using predictive methods. Based on this scheme, a new algorithm is proposed, SortComp (sort-and-compress). For correlated columns, we give an estimation of the asymptotic bit rate of the algorithm and compare it to column-oriented compression schemes. Numerical experiments on real-life csv datasets validate the advantages of SortComp compared to existing row- and column-oriented compression algorithms.

关键词： Correlation Bit rate Estimation Data compression Prediction algorithms Standards compression algorithms

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Applying Practical Parallel Grammar compression to Large-scale Data

Applying Practical Parallel Grammar Compression to Large-sca...

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Data compression Conference (DCC)

作者： Matsushita, Masaki Inoguchi, Yasushi Japan Adv Inst Sci & Technol Asahidai 1-1 Nomi Ishikawa 9231292 Japan

ISBN: (纸本)9781665478939

Re-pair is a grammar-based compression algorithm. In the poster session of the Data compression Conference 2021, we propose the basic concepts of Parallel Re-pair, a parallel variant of Re-pair that achieves shorter compression time with multi-core CPUs. However, our experimental results show it achieves only 1.6 to 2.4 times faster with 32 processors. In this poster session, we propose practical implementation of Parallel Re-pair with Intel Threading Building Blocks.

关键词： Program processors Data compression Grammar compression algorithms

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An Efficient Hardware Accelerator For Lossless Data compression 5

An Efficient Hardware Accelerator For Lossless Data Compress...

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5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Mahmoud, Adel Farid, Samuel Maged, Mark Mohamed, Othman Karam, Reham Salah, Khaled El-Kharashi, M. Watheq Ain Shams Univ Dept Comp & Syst Fac Engn Cairo Egypt Siemens Digital Ind Software Fremont CA 94538 USA

ISBN: (纸本)9781665482370

Data compression is a trending field that is used in data storage and data transmission systems. Lossy compression means that data cannot be completely retrieved while in lossless compression the compressed data must be reconstructed exactly. Lossless data compression is used in compressing binary files, telemetry data and high-fidelity medical and scientific images where details are crucial. There is no generic compression algorithm that gives best compression ratio on all data pattern. In this paper, we propose a hybrid lossless hardware architecture that compresses most of data patterns such as repeated data, Gaussian distribution data and images. A profiling-before-compressing and then choosing the right compression hardware is proposed. The proposed design is a highly parallelized architecture that can compress/decompress 64 bytes/cycle with minor overhead. Moreover, it provides high compression ratio on small block sizes as well as large ones.

关键词： Lossless Data compression Lossless Image compression Hardware Accelerator compression algorithms Rice-Golomb Predictive Coding XORing

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Ship Trajectory Online compression Algorithm Considering Handling Patterns

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IEEE ACCESS 2021年 9卷 70182-70191页

作者： Zhu, Feixiang Ma, Zhihong Dalian Maritime Univ Nav Coll Dalian 116026 Peoples R China

With the widespread use of shipborne Automatic Identification System (AIS) and the establishment of shore-based AIS networks in the past twenty years, a large amount of AIS trajectory data has been generated and accumulated in the shore-based systems. However, big data size increases the cost of storing, querying, and processing AIS data in practical applications. AIS data includes not only the ship spatial position information but also the course, speed, heading information et al. In other words, the knowledge of ship handling behavior is hidden in AIS data. In fact, many practical applications, such as maritime accident investigation and evidence collection, route extraction, ship behavior analysis, will require a compression algorithm to retain the ship key handling points in the original trajectory. To address these research challenges, the ship trajectory compression algorithm considering handling patterns is proposed in this paper. In order to explain the implicit handling patterns of AIS trajectories, the suggested method adopts the rate of course change (ROCC) and the rate of speed change (ROSC) in the sliding window as the criterion of whether the current trajectory point can be simplified. Numerical experiments are performed to verify the effectiveness of the proposed algorithm. Compared with Douglas-Peucker (DP) algorithm, Sliding Window (SW) algorithm, Opening Window Time Ratio (OPW-TR) algorithm, the results show that the proposed algorithm can efficiently compress trajectories by considering ship behavior patterns under application requirement.

关键词： Marine vehicles Trajectory Artificial intelligence compression algorithms Heuristic algorithms Turning Shape Automatic identification system handling patterns ship trajectory sliding window trajectory compression

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Concurrent Encryption and Lossless compression using Inversion Ranks

Concurrent Encryption and Lossless Compression using Inversi...

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Data compression Conference (DCC)

作者： Koc, Basar Arnavut, Ziya Kocak, Huseyin Stetson Univ Dept Comp Sci Deland FL 32723 USA SUNY Coll Fredonia Dept Comp Sci Fredonia NY 14063 USA Univ Miami Dept Comp Sci Coral Gables FL 33124 USA

ISBN: (纸本)9781665478939

For secure and efficient transmission or storage, data files are commonly compressed and encrypted. In this work, we introduce a cost-effective encryption method of files as a built-in component of a lossless compression algorithm, thus avoiding the added cost of employing two separate processes. We have shown in earlier studies that preprocessing data with Burrows-Wheeler Transformation followed by Inversion Ranking transformation in advance of the utilization of an entropy coder resulted in an extremely effective general-purpose lossless compression technique [1], [2]. During the compression process, we encrypt the frequency vector of the Inversion Ranking transformation and transmit it along with the compressed data. Since the frequency vector is required for decompression, no further encryption is necessary to secure the compressed file, see Figure 1.

关键词： Costs Data compression Entropy Encryption compression algorithms

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