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检索条件"主题词=entropy coding"

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A Simple and Efficient Approach for Adaptive entropy coding over Large Alphabets

A Simple and Efficient Approach for Adaptive Entropy Coding ...

引用

Data Compression Conference (DCC)

作者： Amichai Painsky Saharon Rosset Meir Feder Statistics Department School of Mathematical Sciences Electrical Engineering Department - Systems Tel Aviv University Israel

Encoding a sequence of independent symbols over a large alphabet size is a challenging problem with applications in many fields. The most widely used adaptive entropy coding techniques (namely, arithmetic and Huffman coding) are known to achieve an average codeword length which may be significantly greater than the empirical entropy of the sequence, as the alphabet size increases. In this work we introduce an efficient and easy-to-implement method for large alphabet adaptive encoding. We propose a conceptual framework in which a sequence of symbols, over a large alphabet size, is decomposed into multiple "almost independent" sequences over a smaller alphabet. Then each of these sequences is encoded separately. This way, we allow encoding of small alphabet sequences, at the cost of the "remaining dependence" among the sequences. We demonstrate the advantages of our suggested scheme through a series of theorems and experiments, showing it reduces both the average codeword length and the compression runtime in many large alphabet setups.

关键词： entropy entropy coding Source coding Redundancy Decoding Probability distribution

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First-order multiplexed source codes for error-resilient entropy coding

First-order multiplexed source codes for error-resilient ent...

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IEEE International Symposium on Information Theory

作者： H. Jegou C. Guillemot IRISA University of Rennes I France IRISA INRIA USA

This paper describes a new class of variable length codes (VLCs) that allow to exploit first-order source statistics while still being resilient to transmission errors. This paper extends the work of [H. Jegou et al., (2003)] to take into account the source conditional probabilities. Theoretical performances in terms of compression efficiency and error resilience are analyzed.

关键词： entropy coding Error analysis Probability Resilience Performance analysis Signal generators Information resources Warranties Error correction codes Statistics

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Motion estimation with entropy coding considerations in H.264/AVC

Motion estimation with entropy coding considerations in H.26...

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IEEE International Conference on Image Processing

作者： Zhen Li Alexis Michael Tourapis Image Technology Research Dolby Laboratories Burbank CA USA Image Technology Research Dolby Laboratories Burbank CA

In many modern video encoders, motion estimation is usually formulated as a Lagrangian cost function that balances motion prediction accuracy and the rate to transmit motion vectors. In H.264/AVC video encoders, including the H.264 reference software, in an effort to reduce computational complexity, the motion vector rate is often estimated using a look-up table based on universal variable length coding regardless of the entropy coding method used. However, the estimated rate could be different from the actual rate used for encoding. In this paper we present an empirical study on the motion vector rate estimation mismatch caused by different entropy coding assumptions and discuss its impact on motion estimation and coding efficiency. We also present a more accurate motion vector rate estimation method for encoding with the context adaptive binary arithmetic coding (CABAC) method while still maintaining low computational complexity. The proposed motion vector rate estimation can provide up to 2.26% in rate savings for low bit rate applications when all modes are considered for the encoding. Results for single mode encoding, which can be useful for fast encoding applications, show that we can achieve benefits of up to 27.15% and 7.39% in rate savings for low and medium to high bit rate applications respectively.

关键词： Motion estimation entropy coding Automatic voltage control Encoding Computational complexity Bit rate Lagrangian functions Cost function Accuracy Table lookup

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More scanning patterns for entropy coding for H.264

More scanning patterns for entropy coding for H.264

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IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Yang Tao Yuxing Peng Zhiming Liu School of Computer Science National University of Defense Technology Changsha China Department of Computer Nanhua University Hengyang China

In order to reduce the bit rate and to improve the compression performance in video coding in H.264, this paper presents more scanning patterns for entropy coding. Firstly, we investigate the two designs of entropy coding, termed CA VLC and CABAC. And then according to the DCT transform, we deduce that it is possible to adopt more scanning patterns in entropy coding in H.264 to reduce the coding gain at the cost of bit rate. So this paper deduces some different features of an original or residual block, and proposes eight scanning patterns for entropy coding and the corresponding pattern decision method. In the pattern decision method, we make a full search according to the bit rate cost to select the best pattern out of all scanning patterns. Experimental results based on the H.264 JIT reference software of JM 10.1 show that employing the proposed scanning patterns bit rate reduction by up to 4~7% will be obtained with neglectable computation complexity increased and a better performance is also obtained.

关键词： entropy coding Bit rate Automatic voltage control Arithmetic Discrete cosine transforms Computer science Video coding Costs MPEG 4 Standard Signal processing algorithms

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Rank-based image transformation for entropy coding efficiently

Rank-based image transformation for entropy coding efficient...

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International Conference on Computer and Information Science (ACIS)

作者： Deuk-Su Han Myung-Jae Lee Kang-Soo You E.S. Jang Hoon-Sung Kwak Department of Image Engineering Chonbuk National University of Korea Jeonju South Korea School of Information and Communications Hanyang University Seoul South Korea

In this paper, we introduce a rank-based image transformation which is pre-processing method for gray-level images to be compressed more efficiently by entropy encoder. Before entropy encoding a stream of gray-level values in an image, the proposed method counts co-occurrence frequencies for neighboring pixel values. Then, it replaces each pairs of adjacent gray values with particular ordered number based on the investigated co-occurrence frequencies. Finally, the method transmits the adjusted data which has ordered number to entropy encoder. Because statistical characteristic is more enhanced by this preprocessing step, it is possible to improve performance of entropy coding. From the simulation result using 8 bits gray-scale images, it is verified that the proposed method can reduce bit rate by up to 37.85% than plain entropy coders.

关键词： entropy coding Image coding Pixel Digital images Gray-scale Frequency Bit rate Image converters Digital arithmetic Electronic mail

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End-to-end Learned Image Compression with Conditional Latent Space Modeling for entropy coding

End-to-end Learned Image Compression with Conditional Latent...

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European Signal Processing Conference (EUSIPCO)

作者： Aziz Berkay Yeşilyurt Fatih Kamışlı Middle East Technical University Ankara Turkey

ISBN: (数字)9789082797053

ISBN: (纸本)9781728150017

The use of neural networks in image compression enables transforms and probability models for entropy coding which can process images based on much more complex models than the simple Gauss-Markov models in traditional compression methods. All at the expense of higher computational complexity. In the neural-network based image compression literature, various methods to model the dependencies in the transform domain/latent space are proposed. This work uses an alternative method to exploit the dependencies of the latent representation. The joint density of the latent representation is modeled as a product of conditional densities, which are learned using neural networks. However, each latent variable is not conditioned on all previous latent variables as in the chain rule of factoring joint distributions, but only on a few previous variables, in particular the left, upper and upperleft spatial neighbor variables based on a Markov property assumption for a simpler model and algorthm. The compression performance is comparable with the state- of-the-art compression models, while the conditional densities require a much simpler network and training time due to their simplicity and less number of parameters then its counterparts.

关键词： Image coding Computational modeling Neural networks Rate-distortion Transforms Markov processes entropy coding

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Classified adaptive prediction and entropy coding for lossless coding of images

Classified adaptive prediction and entropy coding for lossle...

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IEEE International Conference on Image Processing

作者： F. Golchin K.K. Paliwal School of Microelectronic Engineering Griffith University Brisbane QLD Australia

Natural images often consist of many distinct regions with individual characteristics. Adaptive image coders exploit this feature of natural images to obtain better compression results. In this paper, we propose a classification-based scheme for both adaptive prediction and entropy coding in a lossless image coder. In the proposed coder, blocks of image samples (in the PCM domain) are classified to select an appropriate linear predictor from finite set of predictors. Once the predictors have been determined, the image is DPCM coded. A second classification is then performed to select a suitable entropy coder for each block of DPCM samples. These classification schemes are designed using two separate clustering procedures which attempt to minimize the bit-rate of the encoded image. The coder was tested on a set of monochrome images and was found to produce very promising results.

关键词： entropy coding Image coding Image storage Microelectronics Australia Phase change materials System testing Biomedical imaging Satellites Costs

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Motion vector forecast and mapping (MV-FMap) method for entropy coding based video coders

Motion vector forecast and mapping (MV-FMap) method for entr...

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IEEE Workshop on Multimedia Signal Processing

作者： Julien Le Tanou Jean-Marc Thiesse Joël Jung Marc Antonini Orange Laboratories Issy-les-Moulineaux France I3S Laboratory CNRS and University of Nice-Sophia Antipolis Sophia-Antipolis France

Since the finalization of the H.264/AVC standard and in order to meet the target set by both ITU-T and MPEG to define a new standard that reaches 50% bit rate reduction compared to H.264/AVC, many tools have efficiently improved the texture coding and the motion compensation accuracy. These improvements have resulted in increasing the proportion of bit rate allocated to motion information. Thus, the bit rate reduction of this information becomes a key subject of research. This paper proposes a method for motion vector coding based on an adaptive redistribution of motion vector residuals before entropy coding. Motion information is gathered to forecast a list of motion vector residuals which are redistributed to unexpected residuals of lower coding cost. Compared to H.264/AVC, this scheme provides systematic gain on tested sequences, and 2.3% in average, reaching up to 4.9% for a given sequence.

关键词： Bit rate Automatic voltage control Diamond-like carbon Motion estimation entropy coding Transform coding

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A lossless image coder with context-based minimizing MSE prediction and entropy coding

A lossless image coder with context-based minimizing MSE pre...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： M. Kwon Hyo Joon Kim Choong Woong Lee Sang Uk Lee Institute of New Media and Communications Seoul National University Seoul South Korea

In this paper, a novel gray-scale lossless image coder, based on context-based minimizing mean squared error (MMSE) prediction and entropy coding, is proposed. The proposed algorithm consists of a prediction stage and an entropy coding stage. To obtain the context of prediction, classification of directional edges is employed, which characterizes two-dimensional image behaviors, such as edge and smooth region. Based on the context model, adaptive DPCM prediction coefficients are obtained in the MMSE sense. The MMSE method based on a context-by-context basis decorrelates the neighboring pixels very effectively in the prediction stage. In the entropy coding stage, the context modeling techniques also provide an improved coding gain. To exploit the statistical redundancy still remaining in the residual image, many contexts are preset in the entropy coding stage, and merged into a small number of contexts for complexity reduction. From experimental results, it is found that the proposed lossless coding scheme slightly outperforms the CALIC which is known to achieve the highest compression ratio in entropy coding.

关键词： entropy coding Context modeling Image coding Predictive models Merging Pixel Electronic mail Gray-scale Prediction algorithms Decorrelation

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Machine Learning Based Symbol Probability Distribution Prediction For entropy coding In Av1

Machine Learning Based Symbol Probability Distribution Predi...

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IEEE International Conference on Image Processing

作者： Mingliang Chen Hui Su Sai Deng Yaowu Xu University of Maryland College Park MD USA Google Inc. Mountain View CA USA

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

entropy coding is a lossless data compression technique that is widely applied in video codecs to encode syntax elements into bitstreams. Efficient entropy coding requires accurate prediction of the probability distribution of the encoded symbols. In AV1, multi-symbol arithmetic coding is adopted. The symbol probability is derived with handcrafted context models and lookup tables that store the predicted probabilities corresponding to different entropy contexts. The lookup table based scheme has some fundamental deficiencies. The entropy context features have to be discrete so that they can be used to index the lookup tables. To reduce the size of the lookup table, the number of contexts cannot be very large. Moreover, the probability distributions stored in the lookup tables are maintained separately without taking their correlations into consideration. In this paper, we propose a machine learning based scheme that achieves more accurate symbol probability prediction for entropy coding. The proposed approach is implemented in AV1 for the entropy coding of intra prediction modes. Experimental results demonstrate that it can improve the efficiency of entropy coding significantly.

关键词： entropy coding Probability distribution Computational modeling entropy Decoding Codecs

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