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Processing algorithms for three-dimensional data compression of ultrasonic radio frequency signals

IET SIGNAL PROCESSING 2015年第3期9卷 267-276页

作者： Govindan, Pramod Saniie, Jafar IIT Dept Elect & Comp Engn Chicago IL 60616 USA

Ultrasonic systems are widely used in imaging applications for non-destructive evaluation, quality assurance and medical diagnosis. These applications require large volumes of data to be processed, stored and/or transmitted in real-time. Therefore it is essential to compress the acquired ultrasonic radio frequency (RF) signal without inadvertently degrading desirable signal features. In this paper, two algorithms for ultrasonic signal compression are analysed based on: sub-band elimination using discrete wavelet transform;and decimation/interpolation using time-shift property of Fourier transform. Both algorithms offer high signal reconstruction quality with a peak signal-to-noise ratio (PSNR) between 36 to 39 dB for minimum 80% compression. The computational loads and signal reconstruction quality are examined in order to determine the best compression method in terms of the choice of DWT kernel, sub-band decomposition architecture and computational efficiency. Furthermore, for compressing a large amount of volumetric information, three-dimensional (3D) compression algorithms are designed by utilising the temporal and spatial correlation properties of the ultrasonic RF signals. The performance analysis indicates that the 3D compression algorithm presented in this paper offers an overall 3D compression ratio of 95% with a minimum PSNR of 27 dB.

关键词： radiofrequency compression algorithms subband Data compression radio frequency signal large volume data Peak signal-to-noise ratio Signal reconstruction discrete wavelet transform processing algorithm

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A novel architecture for LZSS compression of configuration bitstreams within FPGA

A novel architecture for LZSS compression of configuration b...

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IEEE Design and Diagnostics of Electronic Circuits and Systems (DDECS)

作者： Radek Iša Jiří Matoušek CESNET z. s. p. o. Praha 6 Czech Republic

Partial run-time reconfigurability of current FPGAs has been shown to be beneficial in many application domains. However, utilization of this feature is limited by the time it takes to reconfigure a selected part of an FPGA. This is commonly addressed by compression of a configuration bitstream, often using LZSS algorithm. To allow speeding up the reconfiguration also in self-adaptive architectures, bitstream compression has to be done within FPGA. Therefore, this paper presents a novel architecture of an LZSS compression engine that is able to achieve very low resource utilization or throughput several times higher than similar architectures, while keeping the other parameter as well as compression ratio at acceptable level. The presented architecture is generic, thus the user can tune the input token size and the size of buffers to achieve desired characteristics. The paper also includes an evaluation of a trade-off among the size of input token, the size of buffers utilized in LZSS algorithm, and a compression ratio for several configuration bitstreams. This evaluation can help the user to select the right set of parameters for the architecture.

关键词： Field programmable gate arrays Throughput Engines Table lookup Resource management compression algorithms

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FLIF: FREE LOSSLESS IMAGE FORMAT BASED ON MANIAC compression 23

FLIF: FREE LOSSLESS IMAGE FORMAT BASED ON MANIAC COMPRESSION

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23rd IEEE International Conference on Image Processing (ICIP)

作者： Sneyers, Jon Wuille, Pieter Cloudinary Ltd Petah Tiqwa Israel Blockstream San Francisco CA USA

ISBN: (纸本)9781467399616

We present a novel lossless image compression algorithm. It achieves better compression than popular lossless image formats like PNG and lossless JPEG 2000. Existing image formats have specific strengths and weaknesses: e.g. JPEG works well for photographs, PNG works well for line drawings or images with few distinct colors. For any type of image, our method performs as good or better (on average) than any of the existing image formats for lossless compression. Interlacing is improved compared to PNG, making the format suitable for progressive decoding and responsive web design.

关键词： Digital images compression algorithms

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Signal Characteristics on Sensor Data compression in IoT - An Investigation 13

Signal Characteristics on Sensor Data Compression in IoT - A...

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13th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)

作者： Bose, Tulika Bandyopadhyay, Soma Kumar, Sudhir Bhattacharyya, Abhijan Pal, Arpan TATA Consultancy Serv Innovat Labs Kolkata India

ISBN: (纸本)9781509017324

In Internet of Things (IoT), numerous and diverse types of sensors generate a plethora of data that needs to be stored and processed with minimum loss of information. This demands efficient compression mechanisms where loss of information is minimized. Hence data generated by diverse sensors with different signal features require optimum balance between compression gain and information loss. This paper presents a unique analysis of contemporary lossy compression algorithms applied on real field sensor data with different sensor dynamics. The aim of the work is to classify the compression algorithms based on the signal characteristics of sensor data and to map them to different sensor data types to ensure efficient compression. The present work is the stepping stone for a future recommender system to choose the preferred compression techniques for the given type of sensor data.

关键词： Lossy compression compression algorithms IoT Signal Characteristics Sensor data

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When Bloom Filters Are No Longer Compact: Multi-Set Membership Lookup for Network Applications

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IEEE-ACM TRANSACTIONS ON NETWORKING 2016年第6期24卷 3326-3339页

作者： Qiao, Yan Chen, Shigang Mo, Zhen Yoon, Myungkeun Google Inc Mountain View CA 94043 USA Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA Kookmin Univ Seoul 02707 South Korea

Many important network functions require online membership lookup against a large set of addresses, flow labels, signatures, and so on. This paper studies a more difficult, yet less investigated problem, called multi-set membership lookup, which involves multiple (sometimes in hundreds or even thousands) sets. The lookup determines not only whether an element is a member of the sets but also which set it belongs to. To facilitate the implementation of multi-set membership lookup in on-die memory of a network processor for line-speed packet inspection, the existing work uses the variants of Bloom filters to encode set IDs. However, through a thorough analysis of the mechanism and the performance of the prior art, much to our surprise, we find that Bloom filters-which were originally designed for encoding binary membership information-are actually not efficient for encoding set IDs. This paper takes a different solution path by separating membership encoding and set ID storage in two data structures, called index filter and set-id table, respectively. With a new ID placement strategy called uneven candidate-entry distribution and a two-level design of an index filter, we demonstrate through analysis and simulation that when compared with the best existing work, our new approach is able to achieve significant memory saving under the same lookup accuracy requirement, or achieve significantly better lookup accuracy under the same memory constraint.

关键词： Computational efficiency approximation algorithms compression algorithms

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SHO-FA: Robust Compressive Sensing With Order-Optimal Complexity, Measurements, and Bits

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IEEE TRANSACTIONS ON INFORMATION THEORY 2016年第12期62卷 7419-7444页

作者： Bakshi, Mayank Jaggi, Sidharth Cai, Sheng Chen, Minghua Chinese Univ Hong Kong Inst Network Coding Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China

Suppose x is any exactly k-sparse vector in R-n. We present a class of sparse matrices A, and a corresponding algorithm that we call short and fast(1) (SHO-FA) that, with high probability over A, can reconstruct x from Ax. The SHO-FA algorithm is related to the invertible bloom lookup tables recently introduced by Goodrich et al., with two important distinctions-SHO-FA relies on linear measurements, and is robust to noise. The SHO-FA algorithm is the first to simultaneously have the following properties: 1) it requires only O(k) measurements;2) the bit precision of each measurement and each arithmetic operation is O(log(n) + P) (here, 2(-P) corresponds to the desired relative error in the reconstruction of x);3) the computational complexity of decoding is O(k) arithmetic operations and that of encoding is O(n) arithmetic operations;and 4) if the reconstruction goal is simply to recover a single component of x instead of all of x, with significant probability over A, this can be done in constant time. All the above constants are independent of all problem parameters other than the desired probability of success. For a wide range of parameters, these properties are information-theoretically order-optimal. In addition, our SHO-FA algorithm works over fairly general ensembles of sparse random matrices, and is robust to random noise and (random) approximate sparsity for a large range of k. In particular, suppose the measured vector equals A(x + z) + e, where z and e correspond to the source tail and measurement noise, respectively. Under reasonable statistical assumptions on z and e, our decoding algorithm reconstructs x with an estimation error of O(parallel to z parallel to(2) + parallel to e parallel to(2)). The SHO-FA algorithm works with high probability over A, z, and e, and still requires only O(k) steps and O(k) measurements over O(log(n))-bit numbers. This is in contrast to most existing algorithms that focus on the worst case z model, where it is known that Ome

关键词： Compressed sensing compression algorithms algorithm design and analysis graph theory signal processing algorithms

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A new adaptive coefficient scanning based on local and global prediction

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SIGNAL IMAGE AND VIDEO PROCESSING 2016年第1期10卷 189-197页

作者： Chen, Hsin-Hui Ding, Jian-Jiun Natl Taiwan Univ Grad Inst Commun Engn 1 Sec 4Roosevelt Rd Taipei 10617 Taiwan

In this paper, a new adaptive coefficient scanning scheme, which is called local-and global-prediction-based adaptive scanning (LGPAS), is described to improve the coding efficiency of discrete cosine transform (DCT)-based image compression methods including JPEG and H.264/AVC intra-coding, in which zigzag scanning is used. The coding performance is limited because the zigzag scan order ignores the statistical properties of the DCT coefficients. On the other hand, we adopt not only the global information but also the local information to perform learning and adaptively generate the scanning patterns, unlike the existing methods. Furthermore, we adopt variation prediction, nonzero probability estimation, and the proposed techniques of zigzag weighting and energy weighting matrices to generate the scanning pattern. On the basis of the local and global predictions for the probability distributions of the nonzero DCT coefficients in an image, the proposed LGPAS scheme can adaptively update the scan order patterns and thus achieves a higher entropy coding gain. Simulations show that the proposed scheme significantly outperforms the conventional zigzag scanning method and other existing adaptive scanning methods.

关键词： Adaptive scanning Intra-prediction JPEG compression algorithms Data compression

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Compressibility of Positive Semidefinite Factorizations and Quantum Models

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IEEE TRANSACTIONS ON INFORMATION THEORY 2016年第5期62卷 2867-2880页

作者： Stark, Cyril J. Harrow, Aram W. MIT Ctr Theoret Phys Cambridge MA 02139 USA

We investigate compressibility of the dimension of positive semidefinite matrices, while approximately preserving their pairwise inner products. This can either be regarded as compression of positive semidefinite factorizations of nonnegative matrices or (if the matrices are subject to additional normalization constraints) as compression of quantum models. We derive both lower and upper bounds on compressibility. Applications are broad and range from the analysis of experimental data to bounding the one-way quantum communication complexity of Boolean functions.

关键词： Mathematical programming Quantum mechanics compression algorithms Data analysis

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Symmetry-Compressible Graphs

Symmetry-Compressible Graphs

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Data compression Conference (DCC)

作者： Uroš Čibej Jurij Mihelič University of Ljubljana Slovenia

This article describes an alternative representation of graphs, using symmetries. We define the class of graphs that are compressed using this representation as symmetry-compressible graphs. This class of graphs is extended into the class of near symmetry-compressible graphs, which includes many more graphs arising in practical applications. To demonstrate the practical potential of the proposed concepts, an empirical evaluation of two algorithms is given.

关键词： Image coding Data compression compression algorithms Image reconstruction Bipartite graph Presses

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Analysis the Functions of Wavelet Transform to Compress Content in Television Production 13

Analysis the Functions of Wavelet Transform to Compress Cont...

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13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET)

作者： Patlaenko, Nikolay OS Popovs Odessa Natl Acad Telecommun Kovalska Str 1-3 UA-65029 Odesa Ukraine

ISBN: (纸本)9786176078067

This paper provides the results of researches in a large class of wavelet transformations, in particular, were investigated such types of wavelets as Haar, Daubechies, coiflets, Mayer's, biorthogonals. Dependences of compression ratio are analyzed on the relation with a PSNR. There were objectively compared the qualitative characteristics of the compressed image with respect to traditional compression methods in terms of compression efficiency.

关键词： The wavelet transform image compression compression algorithms classification algorithms signal/noise ratio the analysis of compression algorithms

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