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检索条件"主题词=compression algorithms"

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***: Stateful header compression for wireless sensor networks

***: Stateful header compression for wireless sensor network...

引用

Conference on Local Computer Networks (LCN)

作者： Andreas Reinhardt S. Mogre Parag Tobias Koenig Ralf Steinmetz Multimedia Communications Lab Technische Universität Darmstadt Darmstadt Germany Multimedia Communications Lab Technische Universität Darmstadt Rundeturmstr. 10 64283 Darmstadt Germany

Nodes in wireless sensor networks are generally confined in the energy budget available for their operation, hence energy-aware application design is mandatory to achieve long node lifetimes. Radio transmissions represent an inherent, but energetically costly characteristic of sensor networking. Significant reductions in the overall energy consumption can thus be achieved by reducing both the number of packet transmissions as well as the corresponding packet lengths. Data compression is a viable approach to conserve energy by increasing the information density within packets and thus transmitting shorter packets on the radio. We investigate the compression of packet headers in wireless sensor networks in this paper. Inspired by technologies used in the Internet and characteristics stemming from existing sensor network deployments, we propose a novel scheme for stateful header compression. Our scheme is specifically designed to consider both static and mobile leaf nodes, and can thus be applied in a majority of sensor network deployments. We analyze our scheme in different settings and show that its application leads to reductions of the required transmission energy and thus extended node lifetimes.

关键词： Context Wireless sensor networks Mobile communication compression algorithms Encoding IP networks Energy consumption

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A Lossy compression Scheme for Pre-Beamformer and Post-Beamformer Ultrasound Data

A Lossy Compression Scheme for Pre-Beamformer and Post-Beamf...

引用

2010 IEEE International Ultrasonics Symposium

作者： Mohamed Mansour Murtaza Ali Systems and Applications R&D Center Texas Instruments Inc. Dallas TX USA

ISBN: (纸本)9781457703829

We present a framework for lossy compression of ultrasound RF data. The proposed scheme can be deployed either before or after the receiver beamformer. It performs a two-dimensional decorrelation in both the lateral and axial directions. The decorrelation in the lateral direction is done using a Karhun-loeve like transform, e.g., DCT or Hadamard. The decorrelation in the axial direction is done using customized orthogonal wavelet packets that are optimized for a particular ultrasound probe. The effectiveness of the compression algorithm for both pre-beamformer and post-beamformer compression is established by evaluating the performance on a set of simulated ultrasound signals.

关键词： Ultrasound digital signal processing lossy compression transducer lossy compression compression algorithms AXIAL decorrelation Ultrasonic Waves discrete cosine transforms DCT gene Lateral Wavelet packets Frameworks

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Depth compression of 3D Object Represented by Layered Depth Image

Depth Compression of 3D Object Represented by Layered Depth ...

引用

Data compression Conference (DCC)

作者： Sang-Young Park Seong-Dae Kim Department of EE KAIST South Korea

ISBN: (纸本)9781424464258;9780769539942

A Layered Depth Image (LDI) is one of the popular representation and rendering methods for 3D objects with complex geometries. In this paper, we propose the new compression algorithm for depth information of a 3D object represented by LDI. For the purpose, we introduce the concept of partial surfaces to seek highly correlated depth data irrespective of their layer and derive a depth compression algorithm by using them. Partial surfaces are approximated by a Be¿zier patch and residual information is encoded by a shape-adaptive transform. Experimental results show that our proposed compression method achieves a better compression performance than any other previous methods.

关键词： Image coding Rendering (computer graphics) Layout Data mining compression algorithms Solid modeling Data structures Image generation Data compression Geometry

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gFPC: A Self-Tuning compression Algorithm

gFPC: A Self-Tuning Compression Algorithm

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Data compression Conference (DCC)

作者： Martin Burtscher Paruj Ratanaworabhan University of Texas Austin USA Kasetsart University Thailand

ISBN: (纸本)9781424464258;9780769539942

This paper presents and evaluates gFPC, a self-tuning implementation of the FPC compression algorithm for double-precision floating-point data. gFPC uses a genetic algorithm to repeatedly reconfigure four hash-function parameters, which enables it to adapt to changes in the data during compression. Self tuning increases the harmonic-mean compression ratio on thirteen scientific datasets from 22% to 28% with sixteen kilobyte hash tables and from 36% to 43% with one megabyte hash tables. Individual datasets compress up to 1.72 times better. The self-tuning overhead reduces the compression speed by a factor of four but makes decompression faster because of the higher compression ratio. On a 2.93 GHz Xeon processor, gFPC compresses at a throughput of almost one gigabit per second and decompresses at over seven gigabits per second.

关键词： compression algorithms Flexible printed circuits Tuning Data compression Genetic algorithms Throughput Cost function Encoding Decoding Arithmetic

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A Survey On Lossless Image compression Methods

A Survey On Lossless Image Compression Methods

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2010 2nd International Conference on Computer Engineering and Technology(2010年第二届计算机工程与技术国际会议 ICCET 2010)

作者： Jaya Shukla ManojAlwani Anil Kumar Tiwari LNM Scholar The LNM Institute of Information and Technology. Jaipur India. CCE(B.Tech) The LNMIIT. Jaipur India. Assosiate Professor The LNMIIT. Jaipur India.

ISBN: (纸本)9781424463473

In this paper we are describing some important state-of the-art algorithms used for lossless compression of images. These algorithms are broadly classified as prediction based methods and transform based methods. Motivation behind this work is to provide a detailed analysis of such algorithms and to give future research direction based on the analysis to the new researchers.

关键词： Image coding Pixel Image reconstruction Image storage Algorithm design and analysis Image edge detection Entropy coding Data compression compression algorithms Economic forecasting

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A skip-line with threshold algorithm for binary image compression

A skip-line with threshold algorithm for binary image compre...

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Congress on Image and Signal Processing, CISP

作者： Hao Sung Wen-Yan Kuo Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering FuJen Catholic University Taipei Taiwan

Binary images (Bi-level images; Bi-Color images, Two-tone images or Monochrome images) have been widely used in image processing, such as facsimile, letters, map archives, digital libraries, fingerprint databases, and those documents that could not be recognized by OCR. In order to reduce the storage space of binary images, there are many researchers attempted to propose more efficient algorithms for binary image compression. In general, they can be divided into two kinds of methods which are `lossless' and `lossy'. In this research, we base on a lossy image compression method, to propose a compression Algorithm called “Skip-Line Encoding with Threshold” (SLETA). Our results of experiments show that SLETA not only achieve high compression ratio but also keep good recognizability: encoded images are not difficult to recognize after decompressions.

关键词： Image coding Correlation Encoding compression algorithms Transform coding Distance measurement Image recognition

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Lossless image compression using kernel based Global Structure Transform (GST)

Lossless image compression using kernel based Global Structu...

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International Conference on Emerging Technologies, ICET

作者： M. Asif Ali Aftab Khan M. Younus Javed Aasia Khanum Department of Computer Engineering College of Electrical & Mechanical Engineering National University of Science and Technology Islamabad Pakistan

Lossless data compression using the variants of Burrows-Wheeler Transform (BWT) with various compression encoders has proven its effectiveness. This research provides a unique method for lossless compression of color images by improving the Global Structure Transform (GST) stage of the Burrows-Wheeler compression Algorithm (BWCA). The proposed model applies the Move-To-Front (MTF) transform at the GST stage by selecting 2-D block (kernel) of BWT data. This method has resulted in a high occurrence of same gray levels in the kernel. Moreover, the symbol map for the MTF Encoder is generated only for the available gray levels in the kernel. The overall redundancy of the MTF indexes increases at the GST stage of the BWCA which results in increased compression.

关键词： Kernel Image coding Transforms Encoding compression algorithms Indexes Data compression

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Improved Rice Algorithm of Lossless compression for On-Board Images

Improved Rice Algorithm of Lossless Compression for On-Board...

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International Conference on Multimedia Technology

作者： Yong-wang Wei Qing-hai Ding Hai-bo Luo Shenyang Institute of Automation Key Laboratory of Optical-Electronics Information Processing Chinese Academy and Sciences Shenyang China Electrical and Information Engineering College Beihang University Jilin China Chinese Academy and Sciences Beijing China Institut für Informatik Ludwig-Maximilians-Universität München

Aiming at the problems of the relatively lower compression ratio and the difficulty in hardware implementation regarding Rice lossless compression algorithm for the Space-borne Remote-sensing images, an improved Rice method is proposed in which the length of predictor FIFO is determined by the size of input images and the global unicode for differences is used. The improved Rice algorithm is mainly composed of two steps, MED prediction for the whole row data and Rice global uniform entropy coding. The statistical results show that it can significantly reduce the spatial redundancy of the adjacent pixels and the mean value of image differences. The compression bit rate is reduced approximately 0.4581 bpp(bit/pixel) against the original Rice algorithm. Furthermore, the new way reserves the all detail information of the input images throughout the whole encoding process. Because we split the all samples with the same mode, the proportion of the identifier of the splitting mechanic in the output data stream is reduced and the compression ratio is increased. The improvement can reduce the encoding complexity and be easily implemented for hardware application.

关键词： Image coding Manganese Pixel Laboratories Prediction algorithms compression algorithms Remote sensing

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Algorithm for DNA sequence compression based on prediction of mismatch bases and repeat location

Algorithm for DNA sequence compression based on prediction o...

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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Kalyan Kumar Kaipa Ajit S Bopardikar Srikantha Abhilash Parthasarathy Venkataraman Kyusang Lee Taejin Ahn Rangavittal Narayanan SAIT India Laboratory Samsung Bangalore India SAIT Samsung Electronics Company Limited Suwon South Korea

For DNA sequence compression, it has been observed that methods based on Markov modeling and repeats give best results. However, these methods tend to use uniform distribution assumption of mismatches for approximate repeats. We show that these replacements are not uniformly distributed and we can improve compression efficiency by using non uniform distribution for mismatches. We also propose a hash table based method to predict repeat location which works well for block based genomic sequence compression algorithms. The proposed methods give good compression gains. The method can be incorporated into any algorithm that uses approximate repeats to realize similar gains.

关键词： Prediction algorithms Encoding DNA Electronic mail Markov processes compression algorithms Approximation algorithms

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EEG compression using JPEG2000: How much loss is too much?

EEG compression using JPEG2000: How much loss is too much?

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Garry Higgins Stephen Faul Robert P. McEvoy Brian McGinley Martin Glavin William P. Marnane Edward Jones College of Engineering and Informatics National University of Ireland Galway Ireland Department of Electrical & Electronic Engineering University College Cork Cork Ireland

compression of biosignals is an important means of conserving power in wireless body area networks and ambulatory monitoring systems. In contrast to lossless compression techniques, lossy compression algorithms can achieve higher compression ratios and hence, higher power savings, at the expense of some degradation of the reconstructed signal. In this paper, a variant of the lossy JPEG2000 algorithm is applied to Electroencephalogram (EEG) data from the Freiburg epilepsy database. By varying compression parameters, a range of reconstructions of varying signal fidelity is produced. Although lossy compression has been applied to EEG data in previous studies, it is unclear what level of signal degradation, if any, would be acceptable to a clinician before diagnostically significant information is lost. In this paper, the reconstructed EEG signals are applied to REACT, a state-of-the-art seizure detection algorithm, in order to determine the effect of lossy compression on its seizure detection ability. By using REACT in place of a clinician, many hundreds of hours of reconstructed EEG data are efficiently analysed, thereby allowing an analysis of the amount of EEG signal distortion that can be tolerated. The corresponding compression ratios that can be achieved are also presented.

关键词： Electroencephalography Transform coding Image coding Databases compression algorithms Epilepsy Wireless communication

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