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检索条件"主题词=Compression algorithms"

共 2068 条记录，以下是241-250 订阅

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VLSI Implementation of ECG compression Algorithm using Golomb Rice Coding 5

VLSI Implementation of ECG Compression Algorithm using Golom...

引用

IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

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

ISBN: (纸本)9781538663011

A lossless electrocardiogram compression algorithm has been implemented in VLSI circuit design. This structure is based on memory-less model i.e. no RAM is being used in the design. Adaptive linear prediction has been combined with Golomb Rice coding to achieve the lossless data compression. Overall compression ratio for MIT-BIH Arrhythmia database is 3. The design has been synthesized using TSMC 0.18 mu m technology. The implementation results show high working speed with low power while working at the processing speed of 90 MHz.

关键词： Electrocardiography Encoding Very large scale integration compression algorithms Power demand Data compression Random access memory"

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An Efficient Hardware Accelerator For Lossless Data compression 5

An Efficient Hardware Accelerator For Lossless Data Compress...

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5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Mahmoud, Adel Farid, Samuel Maged, Mark Mohamed, Othman Karam, Reham Salah, Khaled El-Kharashi, M. Watheq Ain Shams Univ Dept Comp & Syst Fac Engn Cairo Egypt Siemens Digital Ind Software Fremont CA 94538 USA

ISBN: (纸本)9781665482370

Data compression is a trending field that is used in data storage and data transmission systems. Lossy compression means that data cannot be completely retrieved while in lossless compression the compressed data must be reconstructed exactly. Lossless data compression is used in compressing binary files, telemetry data and high-fidelity medical and scientific images where details are crucial. There is no generic compression algorithm that gives best compression ratio on all data pattern. In this paper, we propose a hybrid lossless hardware architecture that compresses most of data patterns such as repeated data, Gaussian distribution data and images. A profiling-before-compressing and then choosing the right compression hardware is proposed. The proposed design is a highly parallelized architecture that can compress/decompress 64 bytes/cycle with minor overhead. Moreover, it provides high compression ratio on small block sizes as well as large ones.

关键词： Lossless Data compression Lossless Image compression Hardware Accelerator compression algorithms Rice-Golomb Predictive Coding XORing

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Code optimization for code compression

Code optimization for code compression

引用

International Symposium on Code Generation and Optimization

作者： Drinic, M Kirovski, D Vo, H Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90095 USA

ISBN: (纸本)076951913X

With the emergence of software delivery platforms such as Microsoft's WET reduced size of transmitted binaries has become a very important system parameter strongly affecting system performance. In this paper, we present two novel pre-processing steps for code compression that explore program binaries' syntax and semantics to achieve superior compression ratios. The first preprocessing step involves heuristic partitioning of a program binary into streams with high auto-correlation. The second preprocessing step uses code optimization via instruction rescheduling in order to improve prediction probabilities for a given compression engine. We have developed three heuristics for instruction rescheduling that explore tradeoffs of the solution quality versus algorithm run-time. The pre-processing steps are integrated with the generic paradigm of prediction by partial matching (PPM) which is the fundament of our compression codec. The compression algorithm is implemented for x86 binaries and tested on several large Microsoft applications. Binaries compressed using our compression codec are 18-24% smaller than those compressed using the best available off-the-shelf compressor.

关键词： Application software Autocorrelation Bandwidth Codecs compression algorithms Computer architecture Computer science Engines Software performance System performance

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PaSTRI: Error-Bounded Lossy compression for Two-Electron Integrals in Quantum Chemistry

PaSTRI: Error-Bounded Lossy Compression for Two-Electron Int...

引用

IEEE International Conference on Cluster Computing (CLUSTER)

作者： Gok, Ali Murat Di, Sheng Alexeev, Yuri Tao, Dingwen Mironov, Vladimir Liang, Xin Cappello, Franck Northwestern Univ Evanston IL 60208 USA Argonne Natl Lab Argonne IL 60439 USA Univ Alabama Tuscaloosa AL 35487 USA Lomonosov Moscow State Univ Moscow Russia Univ Calif Riverside Riverside CA 92521 USA Univ Illinois Champaign IL USA

ISBN: (纸本)9781538683194

Computation of two-electron repulsion integrals is the critical and the most time-consuming step in a typical parallel quantum chemistry simulation. Such calculations have massive computing and storage requirements, which scale as O(N-4) with the size of a chemical system. Compressing the integral's data and storing it on disk can avoid costly recalculation, significantly speeding the overall quantum chemistry calculations;but it requires a fast compression algorithm. To this end, we developed PaSTRI (Pattern Scaling for Two-electron Repulsion Integrals) and implemented the algorithm in the data compression package SZ. PaSTRI leverages the latent pattern features in the integral dataset and optimizes the calculation of the appropriate number of bits required for the storage of the integral. We have evaluated PaSTRI using integral datasets generated by the quantum chemistry program GAMESS. The results show an excellent 16.8 compression ratio with low overhead, while maintaining 10(-10 )absolute precision based on user's requirement.

关键词： compression algorithms Shape Chemicals Mathematical model Quantum computing Computational modeling

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Texture compression based on looking up table

Texture compression based on looking up table

引用

Asian Simulation Conference/5th International Conference on System Simulation and Scientific Computing

作者： Wang, RJ Li, SW Fan, XD Second Aeronaut Inst AF Changchun 130022 Peoples R China

ISBN: (纸本)7506255715

Texture compression is a powerful way to increase the amount of textures used without dramatically increasing the texture storage requirements. This paper introduces a basic texture compression algorithm based on looking up table technology in detail. Further more, four improved compression algorithms are presented including CC_HI algorithm, CC_CHROMA algorithm, CC_MIXED algorithm and CC_ALPHA algorithm. In the end, a comparison of uncompressed textures and compressed textures is shown in a table. This texture compression algorithm will provide. 8:1 image compression with absolutely no reasonable drawbacks.

关键词： compression algorithms texture Data compression Image compression compression algorithms

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Refined Lightweight Temporal compression for Energy-Efficient Sensor Data Streaming 5

Refined Lightweight Temporal Compression for Energy-Efficien...

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5th IEEE World Forum on Internet of Things (IEEE WF-IoT)

作者： Sarbishei, Omid Motsai Res Dept Res & Dev St Bruno PQ Canada

ISBN: (纸本)9781538649800

Lightweight Temporal compression (LTC) is an energy-efficient lossy compression algorithm that maintains a memory usage and per-sample computational cost in O(1). The method provides a trade-off between compression ratio and accuracy using an error bound. In this paper, we present the Refined LTC (RLTC) algorithm, which uses a binning approach to widen the search space and increase the LTC's compression ratio and reduce its dynamic energy consumption, which is characterized by CPU computations and radio transmissions, without compromising the error bound. The proposed RLTC algorithm adds negligible overhead to the memory usage and latency of LTC. Experimental results on an environmental sensor dataset have shown that the LTC's compressed byte stream can be further reduced in size by up to 18%, while the dynamic energy consumption is reduced by 9.5% on average.

关键词： Internet of Things Indexes compression algorithms Energy consumption Energy efficiency Heuristic algorithms Complexity theory"

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compression and Progressive Visualization of Geometric Models

Compression and Progressive Visualization of Geometric Model...

引用

16th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision

作者： Silva, Frutuoso G. M. Yadav, Pranjul Univ Beira Interior IT Networks & Multimedia Grp Covilha Portugal Indian Inst Technol Gauhati India

ISBN: (纸本)9788086943169

The maximum compression, efficient transmission and fast rendering of geometric models is a complex problem for many reasons, thereby gaining a lot of attention from several areas, like compression and rendering of geometric models. Normally, the stripification algorithms are used to speed up the rendering of geometric models because they reduce the number of vertices sent to the graphics pipeline by exploiting the fact that adjacent triangles share an edge. In this paper, we present a new compression algorithm based on stripification of geometric models that enable us a progressive visualization of the models during its transmission. It occurs because our algorithm encodes and decodes the geometry and the connectivity of the model in an interwoven fashion. The main purpose is the storage of object files as strips files in server computer, which enables faster transmission and display of the models at client side. In fact, our compression algorithm achieves compression ratios above 40:1 over ASCII encoded formats and the triangle strips improve rendering performance.

关键词： compression algorithms Triangle strips Progressive visualization Mesh compression

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Software compression in the client/server environment

Software compression in the client/server environment

引用

Data compression Conference (DCC 2001)

作者： Factor, M Sheinwald, D Yassour, BA IBM Res Lab IL-31905 Haifa Israel

ISBN: (纸本)0769510310

Lempel-Ziv based compression algorithms are universal, not assuming any prior knowledge of the file to be compressed or its statistics. Accordingly the reference dictionary of these textual substitution compression algorithms includes only segments of the already-processed portion of the file. It is often the case, though, that both, compressor and decompressor, even when they reside on different sites: share knowledge of files (e.g., devices managed by a server, or software customers holding older releases of products). For such cases, we suggest to add shared files to the reference dictionary. Preferably, files to be included are those which resemble the file to be compressed. Such an extension of the reference dictionary lengthens the matches found while compressing the file, and thus lessens the number of matches needed to cover the file. We found out that with a careful selection (which can be automated) of the shared files to be included, the advantage of the decrease in the number of matches overwhelms the disadvantage of the increase in the number of bits needed to express the index of each match in the extended dictionary. Altogether, compression attainable by our proposed scheme can be significantly better than with the original Lempel-Ziv dictionary. Maintaining and searching a dictionary that is much larger than the original Lempel-Ziv dictionary demand strong computational resources and suits off-line more than on-line compression. We thus conclude that in the client/server environment, where shared files commonly exist, and the server enjoys extensive computational resources, the scheme suggested is advantageous for transferring files from the server to its clients.

关键词： Dictionaries as Topic Servers compression algorithms Lempel-Ziv SUIT

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Selective Merging-based Reference Frame Memory compression

Selective Merging-based Reference Frame Memory Compression

引用

IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)

作者： Hong, Soongi Chung, Dahjung Choe, Yoonsik Yonsei Univ Sch Elect & Elect Engn Image Informat Lab Seoul 120749 South Korea

ISBN: (纸本)9781457702518

Although IBDI, a coding tool that increases internal bit depth to improve compression performance for high quality video, is able to significantly improve the coding efficiency, the internal memory increment problem occurs because of the necessity of storing reference frames. Therefore, memory compression algorithm is proposed to solve the internal memory increment problem while maintaining the coding performance of IBDI. The memory compression methods have successively reduced the reference frame memory while preserving the coding efficiency by dividing a reference frame into the fixed size processing units and using additional information of each unit. However, additional information of each processing unit also has to be stored in internal frame memory;the amount of additional information could be a limitation of the effectiveness of memory compression scheme. Therefore, to relax this limitation of previous method, we propose a selective merging-based reference frame memory compression algorithm, dramatically reducing the amount of additional information. Simulation results show that the proposed algorithm provides much smaller overhead than that of the previous algorithm while keeping the coding efficiency of IBDI.

关键词： compression algorithms Dynamic range Encoding Heuristic algorithms IBDI coding tool Merging Software Software algorithms coding efficiency coding performance compression performance data compression high quality video internal memory increment problem memory compression algorithm selective merging-based reference frame memory compression storage management video coding

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Learning Based Screen Image compression

Learning Based Screen Image Compression

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14th IEEE International Workshop on Multimedia Signal Processing (MMSP)

作者： Yang, Huan Lin, Weisi Deng, Chenwei Nanyang Technol Univ Sch Comp Engn Singapore Singapore

ISBN: (纸本)9781467345729

There are usually two components in computer screen images: textual and pictorial parts. The pictorial part can be compressed efficiently by classical coding approaches (e. g. JPEG, JPEG2000), while the compression of the textual part is still far away from being satisfactory for the reason that the textual content is usually of high-frequency. In this paper, a learning approach is used to construct a tailored dictionary for text representation. Based on the learned dictionary, a novel screen image compression algorithm is proposed through adopting different basis functions for the textual and pictorial components respectively. The screen images are firstly segmented into textual and pictorial parts. Then we employ traditional discrete cosine transformation (DCT) to facilitate the compression of pictorial part, while the learned dictionary is used to represent the textual part in screen images. Experimental results demonstrate the effectiveness of the proposed compression algorithm.

关键词： Screen text representation compression algorithms Dictionaries as Topic Image compression Data compression JPEG compression image compression algorithm High frequency images

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