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检索条件"主题词=Lossless Image Coding"

共 62 条记录，以下是11-20 订阅

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A lossless image coding Method Based on Probability Model Optimization 26

A Lossless Image Coding Method Based on Probability Model Op...

引用

European Signal Processing Conference (EUSIPCO)

作者： Matsuda, Ichiro Ishikawa, Tomokazu Kameda, Yusuke Itoh, Susumu Tokyo Univ Sci Fac Sci & Technol Dept Elect Engn 2641 Yamazaki Noda Chiba 2788510 Japan

ISBN: (纸本)9789082797015

This paper proposes a novel lossless image coding method which directly estimates a probability distribution of image intensity values on a pel-by-pel basis. In the estimation process, several examples, i.e. a set of pels whose neighborhoods are similar to a local texture of the target pel to be encoded, are gathered from a search window located on an already encoded part of the same image. Then the probability distribution is modeled as a weighted sum of the Gaussian functions whose center positions are given by the individual examples. Furthermore, model parameters that control shapes of the Gaussian functions are numerically optimized so that the resulting coding rate of the image intensity values can be a minimum. Simulation results indicate that the proposed method provides comparable coding performance to the state-of-the-art lossless coding schemes proposed by other researchers.

关键词： lossless image coding template matching probability model numerical optimization

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Separable 2D lifting using discrete-time cellular neural networks for lossless image coding

Separable 2D lifting using discrete-time cellular neural net...

引用

International Symposium on Nonlinear Theory and Its Applications

作者： Aomori, H Kawakami, K Otake, T Takahashi, N Yamauchi, M Tanaka, M Sophia Univ Dept Elect & Elect Engn Tokyo 1028554 Japan Tamagawa Univ Dept Media Network Sci Machida Tokyo 1948610 Japan IBM Japan Ltd IBM Engn & Technol Serv Yamato 2428502 Japan

The lifting scheme is an efficient and flexible method for the construction of linear and nonlinear wavelet transforms. In this paper, a novel lossless image coding technique based on the lifting scheme using discrete-time cellular neural networks (DT-CNNs) is proposed. In our proposed method, the image is interpolated by using the nonlinear interpolative dynamics of DT-CNN, and since the output function of DT-CNN works as a multi-level quantization function, our method composes the integer lifting scheme for lossless image coding. Moreover, the nonlinear interpolative dynamics by A-template is used effectively compared with conventional CNN image coding methods using only B-template. The experimental results show a better coding performance compared with the conventional lifting methods using linear filters.

关键词： discrete-time cellular neural networks lifting scheme lossless image coding interpolative dynamics

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DISCRIMINATIVE STRUCTURED SET PREDICTION MODELING WITH MAX-MARGIN MARKOV NETWORK FOR OPTIMAL lossless image coding

DISCRIMINATIVE STRUCTURED SET PREDICTION MODELING WITH MAX-M...

引用

IEEE Visual Communications and image Processing (VCIP)

作者： Dai, Wenrui Xiong, Hongkai Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9781467344050;9781467344067

In this paper, we investigate and propose a novel prediction model for loss less image coding in which the optimal correlated prediction for block of pixels are simultaneously obtained in the sense of the least code length. It not only utilizes the spatial statistical correlation for the optimal prediction directly based on 2-D contexts, but also formulates the data-driven structural interdependencies to make the prediction err or coherent with the underlying probability distribution for coding. Besides the discriminative adaptive pixel-wise prediction, the Markov network is adaptively derived to maintain the coherence of prediction in the blocks and seek the concurrent optimization of set of prediction by relating the loss function to actual code length. The prediction error is shown to be asymptotically upper bounded by the training error under the decomposable loss function. For validation, we apply the proposed model into loss less image coding and experimental results show that the proposed scheme outperforms the best prediction scheme reported.

关键词： Structured set prediction model max-margin Markov network lossless image coding

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Comparing Pixel Predictors for lossless image coding

Comparing Pixel Predictors for Lossless Image Coding

引用

International Workshop on Advanced Imaging Technology (IWAIT)

作者： Kunieda, Aki Takahashi, Keita Fujii, Toshiaki Nagoya Univ Grad Sch Engn Nagoya Aichi Japan

ISBN: (数字)9781510643659

ISBN: (纸本)9781510643659

The efficiency of lossless image coding depends on the pixel predictors, with which unknown pixels are predicted from already-processed pixels. Recent advances in deep learning brought new tools that can be used for pixel prediction, such as deep convolutional neural networks (CNNs). In this paper, we focus on the processing order of the pixels and propose a new pixel predictor constructed using CNNs. Instead of the conventional scanline order, we design a new processing order where the pixels are processed in a progressive, parallelizable manner and the reference pixels are located in all directions with respect to a target pixel. Our pixel predictor is implemented using a CNN architecture that was originally developed for image inpainting, a task of filling in missing pixels from known pixels in an image. We compare the performance of our method against the conventional scanline-based CNN in terms of the potential coding efficiency and computational cost.

关键词： lossless image coding Inpainting Convolutional Neural Network

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Probability Model Adjustment for the CNN-based lossless image coding Method

Probability Model Adjustment for the CNN-based Lossless Imag...

引用

International Workshop on Advanced Imaging Technology (IWAIT)

作者： Kojima, Hiroki Kameda, Yusuke Kita, Yasuyo Matsuda, Ichiro Itoh, Susumu Tokyo Univ Sci Dept Elect Engn 2641 Yamazaki Noda Chiba 2788510 Japan

ISBN: (数字)9781510643659

ISBN: (纸本)9781510643659

Recently, convolutional neural network-based generative models of image signals have been proposed mainly for the purpose of image generation, restoration and compression. For example, PixelCNN++ approximates probability distribution of the image intensity value as a parametric function pel-by-pel, and can be used for lossless image coding tasks. However, such an approach cannot work well for specific images which have statistical properties different from the image dataset used for the network training. In this paper, we improve the coding efficiency by introducing a few parameters for adjusting the probability model generated by PixelCNN++. These parameters are numerically optimized to minimize coding rates of the given image and then encoded as side-information to enable same adjustment at the decoder side

关键词： lossless image coding convolutional neural network probability model optimization

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Performance analysis of wavelets in embedded zerotree-based lossless image coding schemes

Performance analysis of wavelets in embedded zerotree-based ...

引用

International Conference on image Processing

作者： Ramaswamy, VN Namuduri, KR Ranganathan, N Univ of South Florida Tampa United States

ISBN: (纸本)0818681837

The framework for image coding system based on embedded zerotrees consists three stages: (i) wavelet transform (ii) an embedded zerotree encoding and (iii) adaptive arithmetic encoding. In this framework, the selection of wavelet filter becomes an important issue. In this paper, we present a modification to the scanning approach in the set partitioning algorithm proposed in [3] to exploit the correlation in a local neighborhood. Two new criteria are proposed for evaluating the performance of wavelets in lossless image compression applications: zero tree count and monotone spectral ordering of subbands produced after wavelet transform in a multiresolution scheme. We evaluate several wavelet filters to test the evaluation criteria and present experimental results to justify the proposed performance criteria.

关键词： wavelets Wavelet filter lossless image coding evaluation criterion wavelet transforms embedding Performance metrics Frameworks region of an image image invariant image dilation lossless compression lossless image compression

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Improved Probability Modeling for lossless image coding Using Example Search and Adaptive Prediction

Improved Probability Modeling for Lossless Image Coding Usin...

引用

International Workshop on Advanced Imaging Technology (IWAIT)

作者： Kojima, Hiroki Kita, Yasuyo Matsuda, Ichiro Itoh, Susumu Kameda, Yusuke Unno, Kyohei Kawamura, Kei Tokyo Univ Sci Dept Elect Engn 2641 Yamazaki Noda Chiba 2788510 Japan Sophia Univ Tokyo Japan KDDI Res Inc Fujimino Saitama Japan

ISBN: (数字)9781510653320

ISBN: (纸本)9781510653320;9781510653313

We previously proposed a novel lossless image coding method that utilizes example search and adaptive prediction within a framework of probability model optimization. In this paper, the definition of the probability model as well as its optimization procedure is modified to reduce the encoding complexity. In addition, affine predictors used in the adaptive prediction are refined for accurate probability modeling. Simulation results indicate that our modification contributes not only to encoding time reduction, but also to coding efficiency improvement for all of the tested images.

关键词： lossless image coding example search adaptive prediction probability modeling quasi-Newton method

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Embedded zerotree based lossless image coding

Embedded zerotree based lossless image coding

引用

International Conference on image Processing

作者： Egger, O Kunt, M SWISS FED INST TECHNOL SIGNAL PROC LABCH-1015 LAUSANNESWITZERLAND

ISBN: (纸本)0780331222

In this paper the problem of progressive lossless image coding is addressed. Many applications require a lossless compression of the image data. The possibility of progressive decoding of the bitstream adds a new functionality for those applications using data browsing. In practice, the proposed scheme can be of intensive use when accessing large databases of images requiring a lossless compression (especially for medical applications). The international standard JPEG allows a lossless mode. It is based on an entropy reduction of the data using various kinds of estimators followed by source coding. The proposed algorithm works with a completely different philosophy summarized in the following four key points: 1) a perfect reconstruction hierarchical morphological subband decomposition yielding only integer coefficients, 2) prediction of the absence of significant information across scales using zerotrees of wavelet coefficients, 3) entropy-coded successive-approximation quantization, and 4) lossless data compression via adaptive arithmetic coding. This approach produces a completely embedded bitstream. Thus, it is possible to decode only partially the bitstream to reconstruct an approximation of the original image.

关键词： lossless image coding lossless compression bit stream perfect reconstruction original image image data DEcoding embedding

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Bitwise Structured Prediction Model for lossless image coding

Bitwise Structured Prediction Model for Lossless Image Codin...

引用

Data Compression Conference (DCC)

作者： Dai, Wenrui Xiong, Hongkai Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9780769543529

In this paper, we propose the bitwise structured prediction model for the loss less image coding. The prediction problem is handled by decomposing into a series of bitwise prediction problems, where max margin estimation is made for each bit. Furthermore, the structured prediction is proposed for combining such bitwise problems with constraints on their interrelationship and loss function for loss-augmented inference. The proposed methods tend to utilize the inherent dependencies existing in the bitplanes among the neighboring pixels and suppress the fluctuation led by the bitwise decomposition. When building the max margin Markov network for training, the upper bound for the prediction errors is shown to be asymptotically equivalent to the results obtained over the training set. Experimental results also show that the proposed method is superior in coding performance for regular oscillatory patterns.

关键词： Prediction models Error of prediction lossless image coding decomposition Asymptotically equivalent Markov network Tumor margin status LOSS FUNCTION

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Gradient based selective weighting of neighboring pixels for predictive lossless image coding

Gradient based selective weighting of neighboring pixels for...

引用

25th International Conference on Information Technology Interfaces

作者： Knezovic, J Kovac, M Univ Zagreb Fac Elect Engn & Comp HR-10000 Zagreb Croatia

ISBN: (纸本)9539676967

Natural, continuous tone images have a very important property of high correlation of adjacent pixels. This property is cleverly exploited in lossless image compression where, prior to the statistical modeling and entropy coding step, predictive coding as decorrelation toot is used. The use of prediction for current pixel also reduces the model cost of applied statistical model for entropy coding. Linear prediction, where predicted value is linear function of previously encoded pixels (causal template), has proven to give very good results as a decorrelation tool in lossless image compression. In this work we concetrate on adaptive linear predictors used in lossless image coding and propose a new linear prediction method.

关键词： lossless image coding predictive coding linear prediction gradient based prediction image compression

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