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

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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Kronecker Compressive Sensing

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2012年第2期21卷 494-504页

作者： Duarte, Marco F. Baraniuk, Richard G. Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA

Compressive sensing (CS) is an emerging approach for the acquisition of signals having a sparse or compressible representation in some basis. While the CS literature has mostly focused on problems involving 1-D signals and 2-D images, many important applications involve multidimensional signals;the construction of sparsifying bases and measurement systems for such signals is complicated by their higher dimensionality. In this paper, we propose the use of Kronecker product matrices in CS for two purposes. First, such matrices can act as sparsifying bases that jointly model the structure present in all of the signal dimensions. Second, such matrices can represent themeasurement protocols used in distributed settings. Our formulation enables the derivation of analytical bounds for the sparse approximation of multidimensional signals and CS recovery performance, as well as a means of evaluating novel distributed measurement schemes.

关键词： Compressed sensing compression algorithms hyperspectral imaging multidimensional signal processing video compression

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Enabling Reliable Change Detection for Independently Compressed SAR Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2017年第8期55卷 4785-4794页

作者： McGuinness, Christopher Balster, Eric Univ Dayton Dept Elect & Comp Engn Dayton OH 45469 USA

This paper develops distortion metrics for compressed synthetic aperture radar (SAR) imagery from change detection test statistics. These metrics are used to predict lossy image compression's impact on change detection performance. The metrics do not require the intended change detection comparison image to provide these benefits. An SAR compression system leveraging the distortion metrics is proposed. The system generates a bad-pixel mask highlighting potential false alarms that are generated due to compression and are subsequently discarded in the change detection process. The proposed system's performance is demonstrated through noncoherent change detection analysis after JPEG2000 and JPEG image compression. Similarly, a coherent change detection system is evaluated after JPEG2000 image compression. For noncoherent change detection at large compression ratios (CRs) using JPEG2000, the proposed system provides a 33% reduction in false alarms at a 0.1 probability of detection as well as the ability to maintain near-distortionless false alarm rates across a wide range of CRs. At a 0.1 probability of detection for coherent change detection, the system provides a 37% reduction in false alarms at modest CRs. The coherent change detection system is also demonstrated to maintain low false alarm rates across a range of CRs.

关键词： Change detection algorithms compression algorithms synthetic aperture radar (SAR)

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Geometric inhomogeneous random graphs

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THEORETICAL COMPUTER SCIENCE 2019年 760卷 35-54页

作者： Bringmann, Karl Keusch, Ralph Lengler, Johannes Max Planck Inst Informat Saarbrucken Germany Swiss Fed Inst Technol Inst Theoret Comp Sci Zurich Switzerland

Real-world networks, like social networks or the internet infrastructure, have structural properties such as large clustering coefficients that can best be described in terms of an underlying geometry. This is why the focus of the literature on theoretical models for real-world networks shifted from classic models without geometry, such as Chung-Lu random graphs, to modern geometry-based models, such as hyperbolic random graphs. With this paper we contribute to the theoretical analysis of these modern, more realistic random graph models. Instead of studying directly hyperbolic random graphs, we use a generalization that we call geometric inhomogeneous random graphs (GIRGs). Since we ignore constant factors in the edge probabilities, GIRGs are technically simpler (specifically, we avoid hyperbolic cosines), while preserving the qualitative behavior of hyperbolic random graphs, and we suggest to replace hyperbolic random graphs by this new model in future theoretical studies. We prove the following fundamental structural and algorithmic results on GIRGs. (1) We provide a sampling algorithm that generates a random graph from our model in expected linear time, improving the best-known sampling algorithm for hyperbolic random graphs by a substantial factor O (root n). (2) We establish that GIRGs have clustering coefficients in Omega(1), (3) we prove that GIRGs have small separators, i.e., it suffices to delete a sublinear number of edges to break the giant component into two large pieces, and (4) we show how to compress GIRGs using an expected linear number of bits. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Real-world networks Random graph models Hyperbolic random graphs Sampling algorithms compression algorithms Clustering coefficient

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Fast and Efficient compression of Next Generation Sequencing Data

Fast and Efficient Compression of Next Generation Sequencing...

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Data compression Conference (DCC)

作者： Constantinescu, Cornel Schmidt, Gero IBM Almaden Res Ctr San Jose CA 95120 USA

ISBN: (纸本)9781538648834

Despite the large number of research papers and compression algorithms proposed for com- pressing FASTQ genomic data generated by sequencing machines, by far the most commonly used compression algorithm in the industry for FASTQ data is gzip. The main drawback of the proposed alternative special-purpose compression algorithms is the slow speed of either compression or de- compression or both, and also their brittleness by making various limiting assumptions about the input FASTQ format (for example, the structure of the headers or fixed lengths of the records [5]) in order to further improve their specialized compression. In this paper we propose using a simple modeling improvement combined with fast general purpose encoders, achieving 4X-40X speed improvements and 6%-11% more efficienct compression ratios for the compression and decompression of FASTQ genomic data compared to gzip and also being 5X faster than special-purpose FASTQ compressors like DSRC v2 and slimfastq.

关键词： Genomics Bioinformatics Sequential analysis compression algorithms Robustness Data compression Industries

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Titan II: An IPcomp processor for 10-Gbps networks

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IEEE DESIGN & TEST OF COMPUTERS 2004年第6期21卷 514-523页

作者： Papaefstathiou, I Fdn Res & Technol Hellas Inst Comp Sci GR-71110 Iraklion Greece

Data compression techniques can alleviate low-bandwidth problems in multigigabit networks, and are especially useful when combined with encryption. The Titan II is a hardware compressor/decompressor core capable of speeds of up to 10 gigabits per second. Its compression algorithm is a variation of the Lempel-Ziv LZ) algorithm that uses part of the previous input stream as the dictionary.

关键词： Hardware Bandwidth compression algorithms Lithography Cams Very large scale integration Data compression Dictionaries Design optimization Silicon

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TREE-STRUCTURED VECTOR QUANTIZATION OF CT CHEST SCANS - IMAGE QUALITY AND DIAGNOSTIC-ACCURACY

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IEEE TRANSACTIONS ON MEDICAL IMAGING 1993年第4期12卷 727-739页

作者： COSMAN, PC TSENG, C GRAY, RM OLSHEN, RA MOSES, LE DAVIDSON, HC BERGIN, CJ RISKIN, EA STANFORD UNIV SCH MEDDIV BIOSTATSTANFORDCA 94305 STANFORD UNIV SCH MEDDEPT DIAGNOST RADIOL & NUCL MEDSTANFORDCA 94305 STANFORD UNIV SCH MEDDIV DIAGNOST RADIOLSTANFORDCA UNIV WASHINGTON DEPT ELECT ENGNSEATTLEWA 98195

The quality of lossy compressed images is often characterized by signal-to-noise ratios, informal tests of subjective quality, or receiver operating characteristic (ROC) curves that include subjective appraisals of the value of an image for a particular application. We believe that for medical applications, lossy compressed images should be judged by a more natural and fundamental aspect of relative image quality: their use in making accurate diagnoses. We apply a lossy compression algorithm to medical images, and quantify the quality of the images by the diagnostic performance of radiologists, as well as by traditional signal-to-noise ratios and subjective ratings. Our study is unlike previous studies of the effects of lossy compression in that we consider non-binary detection tasks, simulate actual diagnostic practice instead of using paired tests or confidence rankings, use statistical methods that are more appropriate for non-binary clinical data than are the popular ROC curves, and use low-complexity predictive tree-structured vector quantization for compression rather than DCT-based transform codes combined with entropy coding. Our diagnostic tasks are the identification of nodules (tumors) in the lungs and lymphadenopathy in the mediastinum from computerized tomography (CT) chest scans. Radiologists read both uncompressed and lossy compressed versions of images. For the image modality, compression algorithm, and diagnostic tasks we consider, the original 12 bit per pixel (bpp) CT image can be compressed to between 1 bpp and 2 bpp with no significant changes in diagnostic accuracy. The techniques presented in this paper for evaluating image quality do not depend on the specific compression algorithm and are useful new methods for evaluating the benefits of any lossy image processing technique.

关键词： Vector quantization Computed tomography Image coding compression algorithms Biomedical imaging Medical diagnostic imaging Performance loss Signal to noise ratio Predictive models Medical simulation

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PRIORITIZATION AND TRANSPORT IN THE ADTV DIGITAL SIMULCAST SYSTEM

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 1992年第3期38卷 319-324页

作者： JOSEPH, K NG, S SIRACUSA, R RAYCHAUDHURI, D ZDEPSKI, J SAINTGIRONS, R THOMSON CONSUMER ELECTR LOS ANGELES RES & DEV LABCULVER CITYCA 90230 DAVID SARNOFF RES CTR BROADBAND COMMUN RES GRPPRINCETONNJ 08543

This paper provides a technical overview of the prioritization and transport in the Advanced Digital Television system (ADTV). ADTV is the all-digital terrestrial simulcast system developed by the Advanced Television Research Consortium, ATRC, (Thomson Consumer Electronics, Philips North America, NBC, compression Labs, and the David Sarnoff Research Center.) ADTV incorporates an efficient MPEG-compatible compression algorithm at its central core, with application-specific data prioritization and transport features added as separable layers. The compression process is based on a 1440x960 (1050-line 2:1 interlaced) HDTV format, producing a selectable bit-rate in the region of 15-20 Mbps. The data prioritization layer of ADTV achieves robust delivery over an appropriate two-tier modem by separating compressed video data into high- and standard-priority bitstreams with appropriate bit-rates. This prioritized data is then formatted into fixed length "cells" (packets) with appropriate data-link level and service-specific adaptation level headers, designed to provide capabilities such as flexible service multiplexing, priority handling, efficient cell packing, error detection and graceful recovery from errors. (Issues related to the ADTV compression coding, receiver error recovery, and transmission are discussed in separate papers.[1,2,3])

关键词： HDTV Video compression Transform coding Error correction Consumer electronics Robustness Digital TV compression algorithms Decoding Cities and towns

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Dynamic coding of visual information

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 1997年第3期7卷 489-500页

作者： Reusens, E Ebrahimi, T Kunt, M SWISS FED INST TECHNOL SIGNAL PROC LAB CH-1015 LAUSANNE SWITZERLAND

This paper introduces a novel approach to visual data compression. The approach, named dynamic coding, consists of an effective competition between several representation models used for describing data portions, The image data is represented as the union of several regions each approximated by a representation model locally appropriate, The dynamic coding concept leads to attractive features such as genericness, flexibility, and openness and is therefore particularly suited to a multimedia environment in which many types of applications are involved. Dynamic coding is a general proposal to visual data compression and many variations on the same theme may be designed. They differ by the particular procedure by which the data is segmented into objects and the local representation model selected, As an illustrative example, a video compression scheme based on the principles of dynamic coding is presented, This compression algorithm performs a joint optimization of the segmentation (restricted to a so-called generalized quadtree partition) together with the representation models associated with each data segment. Four representation models are competing namely, fractal, motion compensation, text and graphics, and background modes, Optimality is defined with respect to a rate-distortion tradeoff and the optimization procedure leads to a multicriterion segmentation.

关键词： data compression fractals image representation image segmentation image sequences motion compensation multimedia communication optimisation quadtrees rate distortion theory video coding background modes compression algorithm data segmentation dynamic cod Video coding Rate distortion theory quaternary tree compression algorithms image representation dynamic encoding image sequences Data compression image segmentation Fractals Data partitioning Motion compensation background mode visual information

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Fast Neural Representations for Direct Volume Rendering

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COMPUTER GRAPHICS FORUM 2022年第6期41卷 196-211页

作者： Weiss, S. Hermueller, P. Westermann, R. Tech Univ Munich Munich Germany

Despite the potential of neural scene representations to effectively compress 3D scalar fields at high reconstruction quality, the computational complexity of the training and data reconstruction step using scene representation networks limits their use in practical applications. In this paper, we analyse whether scene representation networks can be modified to reduce these limitations and whether such architectures can also be used for temporal reconstruction tasks. We propose a novel design of scene representation networks using GPU tensor cores to integrate the reconstruction seamlessly into on-chip raytracing kernels, and compare the quality and performance of this network to alternative network- and non-network-based compression schemes. The results indicate competitive quality of our design at high compression rates, and significantly faster decoding times and lower memory consumption during data reconstruction. We investigate how density gradients can be computed using the network and show an extension where density, gradient and curvature are predicted jointly. As an alternative to spatial super-resolution approaches for time-varying fields, we propose a solution that builds upon latent-space interpolation to enable random access reconstruction at arbitrary granularity. We summarize our findings in the form of an assessment of the strengths and limitations of scene representation networks for compression domain volume rendering, and outline future research directions. Source code:

关键词： volume rendering neural networks compression algorithms

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