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检索条件"主题词=Arithmetic coding"

共 492 条记录，以下是371-380 订阅

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A HIGH THROUGHPUT CABAC ALGORITHM USING SYNTAX ELEMENT PARTITIONING

A HIGH THROUGHPUT CABAC ALGORITHM USING SYNTAX ELEMENT PARTI...

引用

16th IEEE International Conference on Image Processing

作者： Sze, Vivienne Chandrakasan, Anantha P. MIT Microsyst Technol Labs Cambridge MA 02139 USA

ISBN: (纸本)9781424456536

Enabling parallel processing is becoming increasingly necessary for video decoding as performance requirements continue to rise due to growing resolution and frame rate demands. It is important to address known bottlenecks in the video decoder such as entropy decoding, specifically the highly serial Context-based Adaptive Binary arithmetic coding (CABAC) algorithm. Concurrency must be enabled with minimal cost to coding efficiency, power, area and delay. This work proposes a new CABAC algorithm for the next generation standard in which binary symbols are grouped by syntax elements and assigned to different partitions which can be decoded in parallel. Furthermore, since the distribution of binary symbols changes with quantization, an adaptive binary symbol allocation scheme is proposed to maximize throughput. Application of this next generation CABAC algorithm on five 720p sequences shows a throughput increase of up to 3x can be achieved with negligible impact on coding efficiency (0.06% to 0.37%), which is a 2 to 4x reduction in coding penalty compared with H.264/AVC and entropy slices. Area cost is also reduced by 2x. This increased throughput can be traded-off for low power consumption in mobile applications.

关键词： arithmetic coding Parallel Processing

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A COMPARISON OF CABAC THROUGHPUT FOR HEVC/H.265 VS. AVC/H.264

A COMPARISON OF CABAC THROUGHPUT FOR HEVC/H.265 VS. AVC/H.26...

引用

IEEE Workshop on Signal Processing Systems (SiPS)

作者： Sze, Vivienne Budagavi, Madhukar MIT Cambridge MA 02139 USA Texas Instruments Inc Cambridge MA USA

ISBN: (纸本)9781467362382

The CABAC entropy coding engine is a well known throughput bottleneck in the AVC/H.264 video codec. It was redesigned to achieve higher throughput for the latest video coding standard HEVC/H.265. Various improvements were made including reduction in context coded bins, reduction in total bins and grouping of bypass bins. This paper discusses and quantifies the impact of these techniques and introduces a new metric called Bjontegaard delta cycles (BD-cycle) to compare the CABAC throughput of HEVC vs. AVC. BD-cycle uses the Bjontegaard delta measurement method to compute the average difference between the cycles vs. bit-rate curves of HEVC and AVC. This metric is useful for estimating the throughput of an HEVC CABAC engine from an existing AVC CABAC design for a given bit-rate. Under the common conditions set by the JCT-VC standardization body, HEVC CABAC has an average BD-cycle reduction of 31.1% for all intra, 24.3% for low delay, and 25.9% for random access, when processing up to 8 bypass bins per cycle.

关键词： HEVC H.265 AVC H.264 CABAC throughput arithmetic coding

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DEPENDENT SCALAR QUANTIZATION FOR NEURAL NETWORK COMPRESSION

DEPENDENT SCALAR QUANTIZATION FOR NEURAL NETWORK COMPRESSION

引用

IEEE International Conference on Image Processing (ICIP)

作者： Haase, Paul Schwarz, Heiko Kirchhoffer, Heiner Wiedemann, Simon Marinc, Talmaj Marban, Arturo Mueller, Karsten Samek, Wojciech Marpe, Detlev Wiegand, Thomas Fraunhofer Heinrich Hertz Inst Berlin Germany Free Univ Berlin Inst Comp Sci Berlin Germany Tech Univ Berlin Dept Telecommun Syst Berlin Germany

ISBN: (纸本)9781728163956

Recent approaches to compression of deep neural networks, like the emerging standard on compression of neural networks for multimedia content description and analysis (MPEG-7 part 17), apply scalar quantization and entropy coding of the quantization indexes. In this paper we present an advanced method for quantization of neural network parameters, which applies dependent scalar quantization (DQ) or trellis-coded quantization (TCQ), and an improved context modeling for the entropy coding of the quantization indexes. We show that the proposed method achieves 5.778% bitrate reduction and virtually no loss (0.37%) of network performance in average, compared to the baseline methods of the second test model (NCTM) of MPEG-7 part 17 for relevant working points.

关键词： dependent scalar quantization trellis-coded quantization entropy coding arithmetic coding neural network compression

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Data Compression via Logic Synthesis

Data Compression via Logic Synthesis

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19th Asia and South Pacific Design Automation Conference (ASP-DAC)

作者： Amaru, Luca Gaillardon, Pierre-Emmanuel Burg, Andreas De Micheli, Giovanni Ecole Polytech Fed Lausanne LSI Lausanne Switzerland Ecole Polytech Fed Lausanne TCL Lausanne Switzerland

ISBN: (纸本)9781479928163

Nowadays, most software and hardware applications are committed to reduce the footprint and resource usage of data. In this general context, lossless data compression is a beneficial technique that encodes information using fewer (or at most equal number of) bits as compared to the original representation. A traditional compression flow consists of two phases: data decorrelation and entropy encoding. Data decorrelation, also called entropy reduction, aims at reducing the autocorrelation of the input data stream to be compressed in order to enhance the efficiency of entropy encoding. Entropy encoding reduces the size of the previously decorrelated data by using techniques such as Huffman coding, arithmetic coding, and others. When the data decorrelation is optimal, entropy encoding produces the strongest lossless compression possible. While efficient solutions for entropy encoding exist, data decorrelation is still a challenging problem limiting ultimate lossless compression opportunities. In this paper, we use logic synthesis to remove redundancy in binary data aiming to unlock the full potential of lossless compression. Embedded in a complete lossless compression flow, our logic synthesis based methodology is capable to identify the underlying function correlating a data set. Experimental results on data sets deriving from different causal processes show that the proposed approach achieves the highest compression ratio compared to state-of-art compression tools such as ZIP, bzip2 and 7zip.

关键词： Entropy coding Logic synthesis lossless compression Data compression decorrelation arithmetic coding binary data Dataset incoming data compression ratio

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An Improved Iterative Decoding Scheme based on Error-resistant arithmetic Code

An Improved Iterative Decoding Scheme based on Error-resista...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Lin, Qiuzhen Wong, Kwok-Wo Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

ISBN: (纸本)9781479934324

In this paper, an improved soft in soft out (SISO) iterative decoding scheme for joint source-channel coding is presented. It is realized as the iterative soft decoding of arithmetic code based on sequential decoding to successively prune the decoding tree. Making use of the forecasted forbidden symbols, an error-resistant arithmetic code with an improved a posteriori probability (APP) metric is adopted to further enhance the error correction performance. Simulation results have validated the superiority of our scheme in terms of packet error rate for the AWGN channel.

关键词： Joint Source-Channel coding arithmetic coding Iterative Decoding

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Lossless image compression by adaptive contextual encoding

Lossless image compression by adaptive contextual encoding

引用

Conference on Image and Video Communications and Processing 2000

作者： Aiazzi, B Alparone, L Baronti, S CNR IROE Nello Carrara Res Inst Electromagnet I-50127 Florence Italy

ISBN: (纸本)0819435929

This paper deals with the reversible intraframe compression of grayscale images. With reference to a spatial DPCM scheme, prediction may be accomplished in a space varying fashion following two main strategies: adaptive, i.e., with predictors recalculated at each pixel position, and classified, in which image blocks, or pixels are preliminarily labeled into a number of statistical classes, for which minimum MSE (MMSE) predictors are calculated. In this paper, a trade off between the above two strategies is proposed, which relies on a classified linear-regression prediction obtained through fuzzy techniques, and is followed by context based statistical modeling of the outcome prediction errors, to enhance entropy coding. A thorough performances comparison with the most advanced methods in the literature highlights the advantages of the fuzzy approach.

关键词： adaptive DPCM arithmetic coding classified DPCM fuzzy logic lossless image compression membership function relaxation labeling statistical context modeling

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Lossy compression of grayscale document images by adaptive-offset quantization

Lossy compression of grayscale document images by adaptive-o...

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8th Annual Document Recognition and Retrieval Conference

作者： Popat, K Xerox Palo Alto Res Ctr Palo Alto CA 94304 USA

ISBN: (纸本)0819439851

This paper describes an adaptive-offset quantization scheme and considers its application to the lossy compression of grayscale document images. The technique involves scalar-quantizing and entropy-coding pixels sequentially, such that the quantizer's offset is always chosen to minimize the expected number of bits emitted for each pixel, where the expectation is based on the predictive distribution used for entropy coding. To accomplish this, information is fed back from the entropy coder's statistical modeling unit to the quantizer. This feedback path is absent in traditional compression schemes. Encouraging but preliminary experimental results are presented comparing the technique with JPEG and with fixed-offset quantization on a scanned grayscale text image.

关键词： document image compression quantization entropy coding arithmetic coding

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Fuzzy blending of relaxation labeled predictors for high-performance lossless image compression

Fuzzy blending of relaxation labeled predictors for high-per...

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Conference on Applications of Artificial Neural Networks in Image Processing V

作者： Aiazzi, B Alparone, L Baronti, S CNR IROE Nello Carrara Res Inst Electromagnet I-50127 Florence Italy

ISBN: (纸本)0819435805

This paper deals with application of fuzzy and neural techniques to the reversible intraframe compression of grayscale images. With reference to a spatial DPCM scheme, prediction may be accomplished in a space varying fashion following two main strategies: adaptive, i.e., with predictors recalculated at each pixel position, and classified, in which image blocks, or pixels are preliminarily labeled into a number of statistical classes, for which minimum MSE predictors are calculated. Here, a trade off between the above two strategies is proposed, which relies on a space-varying linear-regression prediction obtained through fuzzy techniques, and is followed by context based statistical modeling of prediction errors, to enhance entropy coding. A thorough comparison with the most advanced methods in the literature, as well as an investigation of performance trends to work parameters, highlight the advantages of the fuzzy approach.

关键词： adaptive DPCM arithmetic coding classified DPCM computational complexity fuzzy logic image compression lossless compression membership function relaxation labeling statistical context modeling

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Bitcoin Message: Data Insertion on a Proof-of-Work Cryptocurrency System

Bitcoin Message: Data Insertion on a Proof-of-Work Cryptocur...

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International Conference on Cyberworlds (CW)

作者： Sleiman, Matthew D. Lauf, Adrian P. Yampolskiy, Roman Univ Louisville Dept Comp Engn & Comp Sci Louisville KY USA

ISBN: (纸本)9781467394031

The Bitcoin currency is steadily growing in popularity as an alternative to physical currencies. This paper presents an approach for utilizing the Bitcoin system for creating permanent messages that are located on computers worldwide. By manipulating the amount field of Bitcoin transactions, messages can be embedded into the Bitcoin block chain. This approach was implemented by extending the Bitcoin-Qt v0.7.2 application and the source code is freely available.

关键词： arithmetic coding Bitcoin C plus open source message

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An adaptable binary entropy coder

An adaptable binary entropy coder

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Data Compression Conference (DCC 2001)

作者： Kiely, A Klimesh, M CALTECH Jet Prop Lab Pasadena CA 91109 USA

ISBN: (纸本)0769510310

We present a novel entropy coding technique which is based on recursive interleaving of variable-to-variable length binary source codes. The encoding is adaptable in that each bit to be encoded may have an associated probability estimate which depends on previously encoded bits. The technique may have advantages over arithmetic coding. The technique can achieve arbitrarily small redundancy, and admits a simple and fast decoder. We discuss code design and performance estimation methods, as well as practical encoding and decoding algorithms.

关键词： Binary entropy coding ENCODER Statistical analysis decoding algorithm Source code Entropy coding arithmetic coding Performance Evaluation

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