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DeepCABAC: A Universal compression Algorithm for Deep Neural Networks

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2020年第4期14卷 700-714页

作者： Wiedemann, Simon Kirchhoffer, Heiner Matlage, Stefan Haase, Paul Marban, Arturo Marinc, Talmaj Neumann, David Nguyen, Tung Schwarz, Heiko Wiegand, Thomas Marpe, Detlev Samek, Wojciech Fraunhofer Heinrich Hertz Inst Machine Learning Grp D-10587 Berlin Germany Fraunhofer Heinrich Hertz Inst D-10587 Berlin Germany Fraunhofer Heinrich Hertz Inst Video Coding Grp D-10587 Berlin Germany

In the past decade deep neural networks (DNNs) have shown state-of-the-art performance on a wide range of complex machine learning tasks. Many of these results have been achieved while growing the size of DNNs, creating a demand for efficient compression and transmission of them. In this work we present DeepCABAC, a universal compression algorithm for DNNs that is based on applying Context-based Adaptive Binary Arithmetic Coder (CABAC) to the DNN parameters. CABAC was originally designed for the H.264/AVC video coding standard and became the state-of-the-art for the lossless compression part of video compression. DeepCABAC applies a novel quantization scheme that minimizes a rate-distortion function while simultaneously taking the impact of quantization to the DNN performance into account. Experimental results show that DeepCABAC consistently attains higher compression rates than previously proposed coding techniques for DNN compression. For instance, it is able to compress the VGG16 ImageNet model by x63.6 with no loss of accuracy, thus being able to represent the entire network with merely 9 MB. The source code for encoding and decoding can be found at https://***/fraunhoferhhi/DeepCABAC.

关键词： Neural networks Source coding Decoding compression algorithms Quantization (signal) Machine learning Deep learning neural network compression efficient representation source coding rate-distortion quantization arithmetic coding

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ON THE DESIGN AND IMPLEMENTATION OF A LOSSLESS DATA-compression AND DEcompression CHIP

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IEEE JOURNAL OF SOLID-STATE CIRCUITS 1993年第9期28卷 948-953页

作者： ROYALS, DM MARKAS, T KANOPOULOS, N REIF, JH STORER, JA DUKE UNIV DEPT COMP SCIDURHAMNC 27706 BRANDEIS UNIV DEPT COMP SCIWALTHAMMA 02254

A lossless data compression and decompression (LDCD) algorithm based on the notion of textual substitution has been implemented in silicon using a linear systolic array architecture. This algorithm employs a model where the encoder and decoder each have a finite amount of memory which is referred to as the dictionary. compression is achieved by finding matches between the dictionary and the input data stream whereby a substitution is made in the data stream by an index referencing the corresponding dictionary entry. The LDCD system is built using 30 application-specific integrated circuits (ASIC's) each containing 126 identical processing elements (PE's) which perform both the encoding and decoding function at clock rates up to 20 MHz.

关键词： Data compression Dictionaries Decoding compression algorithms Vocabulary Computer science Algorithm design and analysis Silicon Systolic arrays Application specific integrated circuits

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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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Reference-Based Framework for Spatio-Temporal Trajectory compression and Query Processing

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2020年第11期32卷 2227-2240页

作者： Zheng, Kai Zhao, Yan Lian, Defu Zheng, Bolong Liu, Guanfeng Zhou, Xiaofang Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Peoples R China Zhejiang Lab Hangzhou 310000 Peoples R China Soochow Univ Sch Comp Sci & Technol Inst Artificial Intelligence Suzhou 215006 Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Peoples R China Univ Sci & Technol China Sch Data Sci Hefei 230026 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Macquarie Univ Dept Comp Sydney NSW 2109 Australia Univ Queensland Brisbane Qld 4072 Australia

The pervasiveness of GPS-enabled devices and wireless communication technologies results in massive trajectory data, incurring expensive cost for storage, transmission, and query processing. To relieve this problem, in this paper we propose a novel framework for compressing trajectory data, REST (Reference-based Spatio-temporal trajectory compression), by which a raw trajectory is represented by concatenation of a series of historical (sub-)trajectories (called reference trajectories) that form the compressed trajectory within a given spatio-temporal deviation threshold. In order to construct a reference trajectory set that can most benefit the subsequent compression, we propose three kinds of techniques to select reference trajectories wisely from a large dataset such that the resulting reference set is more compact yet covering most footprints of trajectories in the area of interest. To address the computational issue caused by the large number of combinations of reference trajectories that may exist for resembling a given trajectory, we propose efficient greedy algorithms that run in the blink of an eye and dynamic programming algorithms that can achieve the optimal compression ratio. Compared to existing work on trajectory compression, our framework has few assumptions about data such as moving within a road network or moving with constant direction and speed, and better compression performance with fairly small spatio-temporal loss. In addition, by indexing the reference trajectories directly with an in-memory R-tree and building connections to the raw trajectories with inverted index, we develop an extremely efficient algorithm that can answer spatio-temporal range queries over trajectories in their compressed form. Extensive experiments on a real taxi trajectory dataset demonstrate the superiority of our framework over existing representative approaches in terms of both compression ratio and efficiency.

关键词： Trajectory Query processing Indexes Measurement Roads Computer science compression algorithms Reference trajectory spatio-temporal trajectory compression

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Framework and algorithms for collaborative compression

Framework and algorithms for collaborative compression

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IEEE International Symposium on Information Theory

作者： Krishnamurthy Viswanathan Ram Swaminathan Hewlett Packard Laboratories Palo Alto CA USA

ISBN: (纸本)9781424422562

We present a framework for considering the problem of compressing large collections of similar sequences. In this framework, an unknown individual sequence is modified several times independently to obtain the collection of sequences to be compressed. For certain collections generated by context-dependent bit flips of the individual sequencepsilas bits, and for those generated by simple edit operations on the individual sequence, we derive universal compression algorithms that compress the collection of sequences almost as well as an optimal compressor that has knowledge of the underlying individual sequence and the modifying processes.

关键词： Collaboration Redundancy compression algorithms Benchmark testing Decoding Partitioning algorithms Context modeling

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SHyLoC 2.0: A Versatile Hardware Solution for On-Board Data and Hyperspectral Image compression on Future Space Missions

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IEEE ACCESS 2020年 8卷 54269-54287页

作者： Barrios, Yubal Sanchez, Antonio J. Santos, Lucana Sarmiento, Roberto Univ Las Palmas Gran Canaria Inst Appl Microelect Las Palmas Gran Canaria 35001 Spain European Space Agcy European Space Res & Technol Ctr NL-2220 AG Noordwijk Netherlands

In this paper, we present the design, implementation and results of a set of IP cores that perform on-board hyperspectral image compression according to the CCSDS 123.0-B-1 lossless standard, specifically designed to be suited for on-board systems and for any kind of hyperspectral sensor. As entropy coder, the sample-adaptive entropy coder defined in the 123.0-B-1 standard or the low-complexity block-adaptive encoder defined by the CCSDS 121.0-B-2 lossless standard could be used. Both IPs, 123.0-B-1 and 121.0-B-2, are part of SHyLoC 2.0, and can be used together for compression of hyperspectral images, being also possible the compression of any kind of data using only the 121-IP. SHyLoC 2.0 improves and extends the capabilities of SHyLoC 1.0, currently available at the ESA IP Cores library, increasing its compression efficiency and throughput, without compromising the resources footprint. Moreover, it incorporates new features, such as the unit-delay predictor option defined by the CCSDS 121.0-B-2 standard, and burst capabilities in the external memory interface of the CCSDS 123-IP, among others. Dedicated architectures have been designed for all the possible input image sample arrangements, in order to maximise throughput and reduce the hardware resources utilization. The design is technology-agnostic, enabling the mapping of the VHDL code in different FPGAs or ASICs. Results are presented for a representative group of well-known space-qualified FPGAs, including the new NanoXplore BRAVE family. A maximum throughput of 150 MSamples/s is obtained for Xilinx Virtex XQR5VFX130 when the SHyLoC 2.0 CCSDS-123 IP is configured in Band-Interleaved by Pixel (BIP) order, using only the 4 & x0025;of LUTs and less than the 1 & x0025;of internal memory.

关键词： Hyperspectral imaging compression algorithms field programmable gate arrays hardware implementations space missions on-board data processing

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A comparison of algorithms for lossless data compression using the Lempel-Ziv-Welch type methods

A comparison of algorithms for lossless data compression usi...

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IEEE-IMS Workshop on Information Theory and Statistics

作者： A.T. Murgan R. Radescu Applied Electronics Department University Politehnica Bucharest Romania

Lempel-Ziv-Welch methods and their variations are all based on the principle of using a prescribed parsing rule to find duplicate occurrences of data and encoding the repeated strings with some sort of special code word identifying the data to be replaced. This paper includes a general presentation of five existing lossless compression methods used in any application of digital signal processing. The comparisons are made experimentally by computer simulation.

关键词： Data compression Signal processing algorithms Testing Encoding Computer simulation Application software Digital signal processing compression algorithms Computational modeling Performance evaluation

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VLSI Implementation of Lossless ECG compression Algorithm for Low Power Devices

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2020年第12期67卷 3317-3321页

作者： Tsai, Tsung-Han Hussain, Muhammad Awais Natl Cent Univ Dept Elect Engn Taoyuan 320 Taiwan

This brief presents a VLSI implementation of an efficient lossless compression scheme for electrocardiogram (ECG) data encoding to save storage space and reduce transmission time. As compression algorithm is able to save storage space and reduce transmission time, this opportunity has been seized by implementing memory-less design while working at a high clock speed in VLSI. ECG compression algorithm comprises two parts: an adaptive linear prediction technique and content-adaptive Golomb Rice code. An efficient and low power VLSI implementation of compression algorithm has been presented. To improve the performance, the proposed VLSI design uses bit shifting operations as a replacement for the different arithmetic operations. VLSI implementation has been applied to the MIT-BIH arrhythmia database which is able to achieve a lossless bit compression rate of 2.77. Moreover, VLSI architecture contains 3.1 K gate count and core of the chip consumes 27.2 nW of power while working at 1 KHz frequency. The core area is 0.05 mm(2) in 90 nm CMOS process.

关键词： Electrocardiography Very large scale integration Encoding Hardware compression algorithms Computer architecture Monitoring ECG compression Golomb rice coding VLSI low power design adaptive linear prediction

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Novel compression Algorithm Based on Sparse Sampling of 3-D Laser Range Scans

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COMPUTER JOURNAL 2013年第7期56卷 852-870页

作者： Dobrucali, Oguzcan Barshan, Billur Bilkent Univ Dept Elect & Elect Engn TR-06800 Ankara Turkey

Three-dimensional models of environments can be very useful and are commonly employed in areas such as robotics, art and architecture, facility management, water management, environmental/industrial/urban planning and documentation. A 3-D model is typically composed of a large number of measurements. When 3-D models of environments need to be transmitted or stored, they should be compressed efficiently to use the capacity of the communication channel or the storage medium effectively. We propose a novel compression technique based on compressive sampling applied to sparse representations of 3-D laser range measurements. The main issue here is finding highly sparse representations of the range measurements, since they do not have such representations in common domains, such as the frequency domain. To solve this problem, we develop a new algorithm to generate sparse innovations between consecutive range measurements acquired while the sensor moves. We compare the sparsity of our innovations with others generated by estimation and filtering. Furthermore, we compare the compression performance of our lossy compression method with widely used lossless and lossy compression techniques. The proposed method offers a small compression ratio and provides a reasonable compromise between the reconstruction error and processing time.

关键词： sensor systems robot sensing systems intelligent sensors compressed sensing data compression compression algorithms 3-D mapping 3-D modeling 3-D laser range measurement laser range finders

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VECTOR-BASED SIGNAL-PROCESSING AND QUANTIZATION FOR IMAGE AND VIDEO compression

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PROCEEDINGS OF THE IEEE 1995年第2期83卷 317-335页

作者： LI, WP ZHANG, YQ GTE LABS INC WALTHAMMA 02254

Image and video compression has become an increasingly important and active area. Many techniques have been developed in this area. Any compression technique can be modeled as a three-stage process. The first stage can be generally called a signal processing stage where an image or video signal is converted into a different domain. Usually, there is no or little loss of information in this stage. The second stage is quantization where loss of information occurs. The third stage is lossless coding that generates the compressed bit stream. The purpose of the signal processing stage is to convert an image or video signal into such a form that quantization can achieve better performance than without the signal processing stage. Because the quantization stage is the place where most of compression is achieved and loss of information occurs, it is naturally the central stage of any compression technique. Since scalar quantization or vector quantization may be used in the second stage, the operation in the first stage should be scalar-based or vector-based respectively in order to match the second stage so that the compression performance can be optimized. In this paper, we summarize the most recent research results on vector-based signal processing and quantization techniques that have shown high compression performance.

关键词： Video signal processing Quantization Video compression Image coding Image converters Signal processing Signal processing algorithms Very large scale integration compression algorithms Bit rate

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