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Graph Wedgelets: Adaptive data compression on Graphs Based on Binary Wedge Partitioning Trees and Geometric Wavelets

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2023年 9卷 24-34页

作者： Erb, Wolfgang Univ Padua Dipartimento Matemat Tullio Levi Civita I-35122 Padua Italy

We introduce graph wedgelets - a tool for data compression on graphs based on the representation of signals by piecewise constant functions on adaptively generated binary graph partitionings. The adaptivity of the partitionings, a key ingredient to obtain sparse representations of a graph signal, is realized in terms of recursive wedge splits adapted to the signal. For this, we transfer adaptive partitioning and compression techniques known for 2D images to general graph structures and develop discrete variants of continuous wedgelets and binary space partitionings. We prove that continuous results on best $m$-term approximation with geometric wavelets can be transferred to the discrete graph setting and show that our wedgelet representation of graph signals can be encoded and implemented in a simple way. Finally, we illustrate that this graph-based method can be applied for the compression of images as well.

关键词： Image coding Image segmentation Wavelet analysis Information processing Continuous wavelet transforms Topology Discrete wavelet transforms Binary graph partitioning big data compression geometric wavelets greedy algorithms on graphs non-linear approximation graph wedgelets

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Pattern Matching Based Algorithms for Graph compression 4

Pattern Matching Based Algorithms for Graph Compression

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4th IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)

作者： Chatterjee, Amlan Shah, Rushabh Jitendrakumar Sen, Soumya Calif State Univ Dominguez Hills Dept Comp Sci Carson CA 90747 USA Univ Calcutta AK Choudhury Sch Informat Technol Kolkata WB India

ISBN: (纸本)9781538676387

Graphs can be used to represent a wide variety of data belonging to different domains. Graphs can capture the relationship among data in an efficient way, and have been widely used. In recent times, with the advent of big data, there has been a need to store and compute on large data sets efficiently. However, considering the size of the data sets in question, finding optimal methods to store and process the data has been a challenge. Therefore, we study different graph compression techniques and propose novel algorithms to do the same in this paper. Specifically, given a graph G = (V, E), where V is the set of vertices and E is the set of edges, and vertical bar V vertical bar = n, we propose techniques to compress the adjacency matrix representation of the graph. Our algorithms are based on finding patterns within the adjacency matrix data, and replacing the common patterns with specific markers. All the techniques proposed here are lossless compression of graphs. Based on the experimental results, it is observed that our proposed techniques achieve almost 70% compression as compared to the adjacency matrix representation. The results show that large graphs can be efficiently stored in smaller memory and exploit the parallel processing power of compute nodes as well as efficiently transfer data between resources.

关键词： Graph compression Patterns Quadtree big data compression compression Algorithms

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Pattern Matching Based Algorithms for Graph compression

Pattern Matching Based Algorithms for Graph Compression

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IEEE International Conference on Research in Computational Intelligence and Communication Networks

作者： Amlan Chatterjee Rushabh Jitendrakumar Shah Soumya Sen Department of Computer Science California State University Dominguez Hills A.K. Choudhury School of Information Technology University of Calcutta

ISBN: (纸本)9781538676394;9781538676387

Graphs can be used to represent a wide variety of data belonging to different domains. Graphs can capture the relationship among data in an efficient way, and have been widely used. In recent times, with the advent of big data, there has been a need to store and compute on large data sets efficiently. However, considering the size of the data sets in question, finding optimal methods to store and process the data has been a challenge. Therefore, we study different graph compression techniques and propose novel algorithms to do the same in this paper. Specifically, given a graph G = (V, E), where V is the set of vertices and E is the set of edges, and |V| = n, we propose techniques to compress the adjacency matrix representation of the graph. Our algorithms are based on finding patterns within the adjacency matrix data, and replacing the common patterns with specific markers. All the techniques proposed here are lossless compression of graphs. Based on the experimental results, it is observed that our proposed techniques achieve almost 70% compression as compared to the adjacency matrix representation. The results show that large graphs can be efficiently stored in smaller memory and exploit the parallel processing power of compute nodes as well as efficiently transfer data between resources.

关键词： Graph compression Patterns Quadtree big data compression compression algorithms

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Toward practical guideline for design of image compression algorithms for biomedical applications

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 56卷 360-367页

作者： Karimi, Nader Samavi, Shadrokh Soroushmehr, S. M. Reza Shirani, Shahram Najarian, Kayvan Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran Univ Michigan Dept Emergency Med Ann Arbor MI 48109 USA McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada Univ Michigan Dept Computat Med & Bioinformat Ann Arbor MI 48109 USA

Improvements in medicine and healthcare are accelerating. Information generation, sharing, and expert analysis, play a great role in improving medical sciences. big data produced by medical procedures in hospitals, laboratories, and research centers needs storage and transmission. data compression is a critical tool that reduces the burden of storage and transmission. Medical images, in particular, require special consideration in terms of storage and transmissions. Unlike many other types of big data, medical images require lossless storage. Special purpose compression algorithms and codecs could compress variety of such images with superior performance compared to the general purpose lossless algorithms. For the medical images, many lossless algorithms have been proposed so far. A compression algorithm comprises of different stages. Before designing a special purpose compression method we need to know how much each stage contributes to the overall compression performance so we could accordingly invest time and effort in designing different stages. In order to compare and evaluate these multi stage compression techniques and to design more efficient compression methods for big data applications, in this paper the effectiveness of each of these compression stages on the total performance of the algorithm is analyzed. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Medical data compression compression performance analysis Lossless compression big data compression

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High-throughput Biological Cell Classification Featuring Real-time Optical data compression 49

High-throughput Biological Cell Classification Featuring Rea...

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49th Annual Conference on Information Sciences and Systems (CISS)

作者： Jalali, Bahram Mahjoubfar, Ata Chen, Claire L. Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

ISBN: (纸本)9781479984282

High throughput real-time instruments are needed to acquire large data sets for detection and classification of rare events. Enabled by the photonic time stretch digitizer, a new class of instruments with record throughputs have led to the discovery of optical rogue waves [1], detection of rare cancer cells [2], and the highest analog-to-digital conversion performance ever achieved [3]. Featuring continuous operation at 100 million frames per second and shutter speed of less than a nanosecond, the time stretch camera is ideally suited for screening of blood and other biological samples. It has enabled detection of breast cancer cells in blood with record, one-in-a-million, sensitivity [2]. Owing to their high real-time throughput, instruments produce a torrent of data - equivalent to several 4K movies per second - that overwhelm data acquisition, storage, and processing operations. This predicament calls for technologies that compress images in optical domain and in real-time. An example of this, based on warped stretch transformation and non-uniform Fourier domain sampling will be reported.

关键词： Optical data processing Ultrafast imaging big data compression

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Time Stretch Imaging with Optical data compression for Label-free Biological Cell Classification

Time Stretch Imaging with Optical Data Compression for Label...

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Opto Electronics Communications Conference (OECC)

作者： Mahjoubfar, Ata Chen, Claire L. Jalali, Bahram Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA Calif NanoSyst Inst Los Angeles CA 90095 USA

ISBN: (纸本)9781467379458

Real-time instruments that acquire large data sets are needed for detection and classification of outliers. A new class of high throughput real-time instruments based on the photonic time-stretch has led to the discovery of optical rogue waves [1], detection of rare cancer cells [2], and the highest analog-to-digital conversion performance ever achieved [3]. One example of these instruments is the time stretch camera, an imaging modality that features continuous operation at about 100 million frames per second and shutter speed of less than a nanosecond. As an imaging flow-through microscope, the technology is in clinical testing for blood screening. While highly useful for collecting large data sets, the instrument's ultrahigh throughput also creates a big data problem. The system produces a large volume of data in a short time equivalent to several 4K movies per second. Such a data fire hose places a burden on data acquisition, storage, and processing operations and calls for technologies that compress images in optical domain and in real-time. An example of this, based on warped stretch transformation and non-uniform Fourier domain sampling has recently been reported [4]. The paper will provide an overview of the time-stretch microscope with real-time optical image compression, and application of this technology in classification of cancer cell lines in blood.

关键词： Optical data processing Ultrafast imaging big data compression

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SELF-ADAPTIVE STRETCH IN ANAMORPHIC IMAGE compression

SELF-ADAPTIVE STRETCH IN ANAMORPHIC IMAGE COMPRESSION

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IEEE International Conference on Image Processing

作者： Mohammad H. Asghari Bahram Jalali Departments of Electrical Engineering University of California

ISBN: (纸本)9781479957521

Discrete Anamorphic Stretch Transform is a newly introduced physics-based algorithm that compresses the image while preserving vital information. This transform is a reversible nonlinear operation both in terms of amplitude and phase. It reduces the space-bandwidth product by performing self-adaptive stretch where sharp features of the image are preferentially stretched without the need for feature detection or iterative algorithms. Here we present mathematical proof of self-adaptive stretch in anamorphic image compression. We also use the Stretched Modulation Distribution to shows the effect of self-adaptive stretch on the image and identify the proper kernel of the transform that leads to data compression. Experimental results presented here show application of our method as pre-compression to enhance WebP and JPEG 2000 formats.

关键词： Time stretch dispersive Fourier transform Space-bandwidth engineering Self-adaptive stretch Warped phase transformation Diffractive data compression big data compression

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Image compression using the Anamorphic Stretch Transform

Image Compression using the Anamorphic Stretch Transform

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IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

作者： Asghari, Mohammad H. Jalali, Bahram Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

ISBN: (纸本)9781479947966

A new method for image compression is introduced. It uses a new physics-based mathematical transformation that increases spatial coherency by causing feature-selective stretch. We show that by reshaping the image with a special class of transfer functions, more samples are allocated to sharp features, where they are needed, and less to coarse features, where they are redundant. The method achieves image space-bandwidth compression without feature detection or prior knowledge of the image.

关键词： Keywords Image compression data compression Space-bandwidth compression Feature-selective stretch Feature selective sampling Feature selective compression warped stretch transform Generalized Anamorphic Stretch Transform Time Stretch Dispersive Fourier Transform diffractive data compression digital data compression big data compression

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