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检索条件"主题词=Arithmetic coding"

共 492 条记录，以下是91-100 订阅

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Compression of sparse matrices by arithmetic coding

Compression of sparse matrices by arithmetic coding

引用

Data Compression Conference

作者： Bell, T McKenzie, B Univ Canterbury Dept Comp Sci Christchurch 1 New Zealand

ISBN: (纸本)0818684062

The compression of matrices where the majority of the entries are a fixed constant (most typically zero), usually referred to as sparse matrices, has received much attention. We evaluate the performance of existing methods, and consider how arithmetic coding can be applied to the problem to achieve better compression. The result is a method that gives better compression than existing methods, and still allows constant-time access to individual elements if required. Although for concreteness we express our method in terms of two-dimensional matrices where the majority of the values are zero, it is equally applicable to matrices of any number of dimensions and where the fixed known constant is any value. We assume that the number of dimensions and their ranges are known, but will not assume that any information is available externally regarding the number of non-zero entries.

关键词： arithmetic coding Sparse matrix compression entry dimensions Zero

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Piecewise integer mapping for arithmetic coding

Piecewise integer mapping for arithmetic coding

引用

Data Compression Conference

作者： Stuiver, L Moffat, A Univ Melbourne Dept Comp Sci Parkville Vic 3052 Australia

ISBN: (纸本)0818684062

By asking afresh exactly what it is the arithmetic coder must do, we show how much of the complexity of current coders can be dispensed with. In particular, we eliminate all multiplicative operations in both the encoder and decoder, replacing them by comparisons and additions. The essence of the proposal is a simple piecewise integer mapping. Graf (1997) has made use of a similar integer mapping in his proposal for a fast entropy coder. Our work is related to but independent of his. As in all non-exact coders, some inefficiency is introduced. We give an analysis that shows the average loss caused by the revised coder to be bounded in an expected sense by 0.0861 bits per symbol, which for most compression applications is just one or two percent. As an additional modification, we discuss a mechanism that allows multi-bit output of codewords without compromising the precision of the probability estimates that may be employed. Finally, we give performance results that show that in combination the two improvements yield a coder as much as 40% faster than previous benchmark arithmetic coders.

关键词： arithmetic coding Integer Piecewise Essences

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Efficient arithmetic coding for wavelet image compression

Efficient arithmetic coding for wavelet image compression

引用

1997 Conference on Visual Communications and Image Processing

作者： Xiong, ZX Ramchandran, K Orchard, MT Princeton Univ. (United States) Univ. of Illinois/Urbana-Champaign (United States)

ISBN: (纸本)0819424358

We address efficient context modeling in arithmetic coding for wavelet image compression. Quantized highpass wavelet coefficients are first mapped into a binary source, followed by high order context modeling in arithmetic coding. A blending technique is used to combine results of context modeling of different orders into a single probability estimate. Experiments show that an arithmetic coder with efficient context modeling is capable of achieving a 10% bitrate saving (or 0.5 dB gain in PSNR) over a zeroth order adaptive arithmetic coder in high performance wavelet image coders.

关键词： context modeling entropy arithmetic coding wavelet

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CONVOLUTIONAL NEURAL NETWORK-BASED arithmetic coding OF DC COEFFICIENTS FOR HEVC INTRA coding 25

CONVOLUTIONAL NEURAL NETWORK-BASED ARITHMETIC CODING OF DC C...

引用

25th IEEE International Conference on Image Processing (ICIP)

作者： Ma, Changyue Liu, Dong Peng, Xiulian Wu, Feng Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei 230027 Anhui Peoples R China Microsoft Res Asia Beijing 100080 Peoples R China

ISBN: (纸本)9781479970612

In the state-of-the-art video coding standard-High Efficiency Video coding (HEVC), context-adaptive binary arithmetic coding (CABAC) is adopted as the entropy coding tool. In CABAC, the binarization processes are manually designed, and the context models are empirically crafted, both of which incur that the probability distribution of the syntax elements may not be estimated accurately, and restrict the coding efficiency. In this paper, we adopt a convolutional neural network-based arithmetic coding (CNNAC) strategy, and conduct studies on the coding of the DC coefficients for HEVC intra coding. Instead of manually designing binarization process and context model, we propose to directly estimate the probability distribution of the value of the DC coefficient using densely connected convolutional networks. The estimated probability together with the real DC coefficient are then input into a multi-level arithmetic codec to fulfill entropy coding. Simulation results show that our proposed CNNAC leads to on average 22.47% bits saving compared with CABAC for the bits of DC coefficients, which corresponds to 1.6% BD-rate reduction than the HEVC anchor.

关键词： arithmetic coding CABAC Convolutional neural networks DC coefficient HEVC

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Weighted-based arithmetic coding For Satellite Image Compression

Weighted-based Arithmetic Coding For Satellite Image Compres...

引用

Conference on Satellite Data Compression, Communications, and Processing VI

作者： Wu, Jiaji Gao, Shuai Jiao, Licheng Jeong, Jechang Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Hanyang Univ Dept Elect & Comp Engn Coll Engn Seoul 133791 South Korea

ISBN: (纸本)9780819483065

Aiming at the problems of the context-dilution and complicated context-quantization of high-order context, this paper proposes a new context arithmetic coding using weighted-based context modeling. By classifying the weights with non-uniform quantization, conventional high-order context-based arithmetic coding method can be approximated as low-order arithmetic coding. Compared with the existing high-order context modeling, the proposed method not only decreases the complexity of computation but also effectively improves the performance of entropy coding. Experimental results show that the algorithm with proposed weighted-based arithmetic coding method performs better than SPECK, SPIHT and JPEG2000.

关键词： arithmetic coding context-based high-order context context-dilution weighted-based classification

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JOINT SECURITY AND PERFORMANCE ENHANCEMENT FOR SECURE arithmetic coding

JOINT SECURITY AND PERFORMANCE ENHANCEMENT FOR SECURE ARITHM...

引用

15th IEEE International Conference on Image Processing (ICIP 2008)

作者： Zhou, Jiantao Au, Oscar C. Fan, Xiaopeng Wong, Peter H. Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781424417650

This paper studies the joint security and performance enhancement of secure arithmetic coding (AC) for digital rights management applications. The proposed cryptosystem incorporates the interval splitting AC with a simple bit-wise XOR operation step. Security analysis results show that the proposed scheme provides satisfactory level of security against the cipher-only attack, the chosen-plaintext attack and the chosen-ciphertext attack. Due to the elimination of the input symbol-wise permutation step, our proposed scheme can be extended conveniently to any context-based coding scenarios. In addition, the implementation complexity of our proposed scheme is lower than the original secure AC. Finally, we suggest a selective encryption version of our proposed scheme, which further reduces the implementation complexity.

关键词： Encryption arithmetic coding compression digital rights management

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Distributed arithmetic coding for Memory Sources 2019

Distributed Arithmetic Coding for Memory Sources

引用

International Conference on Image, Video and Signal Processing (IVSP)

作者： Zhou, Junwei Qi, Mingwei Yang, Yanchao Xiong, Shengwu Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9781450361750

Slepian-Wolf problem regards the compression of multiple correlated information sources that do not communicate with each other. In real applications, the information sources such as video sequences are usually with memory where they are interdependent between symbols. Existing researches mainly consider the memory-less sources and employ channel coding to solve the Slepian-Wolf problem. In this paper, we use distributed arithmetic coding instead of channel coding to solve the Slepian-Wolf problem, benefiting the advantages of source coding in memory sources by eliminating redundancy between symbols. The proposed scheme is very competitive comparing to the existing schemes when applied to memory sources. Simulation results show that the proposed scheme has performance gains when applied to the first-order memory sources with the different overlapping factor. We also analyzed the performance applied to the second-order memory sources with different block lengths.

关键词： arithmetic coding Source coding Channel coding Memory Sources Distributed arithmetic coding

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Using arithmetic coding for Reduction of Resulting Simulation Data Size on Massively Parallel GPGPUs

Using Arithmetic Coding for Reduction of Resulting Simulatio...

引用

15th European Parallel-Virtual-Machine-and-Message-Passing-Interface-Users-Group Meeting (PVM/MPI)

作者： Balevic, Ana Rockstroh, Lars Wroblewski, Marek Simon, Sven Inst Parallel & Distributed Syst D-70596 Stuttgart Germany

ISBN: (纸本)9783540874744

The popularity of parallel platforms, such as general purpose graphics processing units (GPGPUs) for large-scale simulations is rapidly increasing, however the I/O bandwidth and storage capacity of these massively-parallel cards remain the major bottle necks. We propose a novel approach for post-processing of simulation data directly on CPGPUs by efficient data size reduction immediately after simulation that can considerably reduce the influence of these bottlenecks on the overall simulation performance, and present current performance results.

关键词： HPC GPGPU I/O Bandwidth Data Compression arithmetic coding

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Speech Recognition using arithmetic coding and MFCC for Telugu Language 3

Speech Recognition using Arithmetic Coding and MFCC for Telu...

引用

3rd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Kumar, Archek Praveen Kumar, Neeraj Kumar, Cheruku Sandesh Yadav, Ashwani Kumar Sharma, Abhay Amity Univ Rajasthan Jaipur Rajasthan India

ISBN: (纸本)9789380544199

This paper presents speech feature extraction of Telugu language through proper compression. Compression is provided to speech using Digital arithmetic coding and features are extracted by MFCC then classification is done by ANN. Speech feature extraction and feature classification are the major steps in ASR. This paper presents a technique to extract the speech features after speech compression. A technique with arithmetic coding and MFCC is done by reducing the average number of bits. arithmetic coding and MFCC stands out in terms of magnificence and potency. A text dependent Telugu ASR is designed. Features extraction process is done for 140 bits/frame and 80 bits /frame and features extracted arc LSP, Pitch prediction filter, code base indexes, gain, synchronization, FEC, future expansion. The proposed technique AC with MFCC has been compared with various existing techniques like ADPCM, LD-CELP, CS-AELP, CELP, LPC, and MFCC. The performance of the proposed technique has proved better in terms of bit rate, word error rate and compression ratio.

关键词： arithmetic coding MFCC Speech Recognition

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Lossless Medical Image Compression through lightweight Binary arithmetic coding 40

Lossless Medical Image Compression through lightweight Binar...

引用

Conference on Applications of Digital Image Processing XL

作者： Bartrina-Rapesta, Joan Sanchez, Victor Serra-Sagrista, Joan Marcellin, Michael W. Auli-Llinas, Francesc Blanes, Ian Univ Autonoma Barcelona Dept Informat & Commun Engn ETSE E-08193 Barcelona Cerdanyola Del Spain Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England Univ Arizona Elect & Comp Engn Tucson AZ 85721 USA

ISBN: (数字)9781510612501

ISBN: (纸本)9781510612501;9781510612495

A contextual lightweight arithmetic coder is proposed for lossless compression of medical imagery. Context definition uses causal data from previous symbols coded, an inexpensive yet efficient approach. To further reduce the computational cost, a binary arithmetic coder with fixed-length codewords is adopted, thus avoiding the normalization procedure common in most implementations, and the probability of each context is estimated through bitwise operations. Experimental results are provided for several medical images and compared against state-of-the-art coding techniques, yielding on average improvements between nearly 0.1 and 0.2 bps.

关键词： Medical Image Compression CCSDS-123 Lossless coding arithmetic coding

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