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context quantization by kernel Fisher discriminant

IEEE TRANSACTIONS ON IMAGE PROCESSING 2006年第1期15卷 169-177页

作者： Xu, MT Wu, XL Fränti, P Univ Joensuu Dept Comp Sci FIN-80101 Joensuu Finland McMaster Univ Dept Elect & Comp Engn Hamilton ON L8G 4K1 Canada

Optimal context quantizers for minimum conditional entropy can be constructed by dynamic programming in the probability simplex space. The main difficulty, operationally, is the resulting complex quantizer mapping function in the context space, in which the conditional entropy coding is conducted. To overcome this difficulty, we propose new algorithms for designing context quantizers in the context space based on the multiclass Fisher discriminant and the kernel Fisher discriminant (KFD). In particular, the KFD can describe linearly nonseparable quantizer cells by projecting input context vectors onto a high-dimensional curve, in which these cells become better separable. The new algorithms outperform the previous linear Fisher discriminant method for context quantization. They approach the minimum empirical conditional entropy context quantizer designed in the probability simplex space, but with a practical implementation that employs a simple scalar quantizer mapping function rather than a large lookup table.

关键词： context quantization entropy coding Fisher discriminants image compression

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context quantization Based on The Modified Genetic Algorithm with K-means

Context Quantization Based on The Modified Genetic Algorithm...

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9th International Conference on Natural Computation (ICNC)

作者： Chen, Min Chen, Jianhua Yunnan Univ Dianchi Coll Kunming Peoples R China Yunnan Univ Dept Elect Engn Kunming Peoples R China

ISBN: (纸本)9781467347143

In this paper, the context quantization for I-ary sources based on a modified genetic algorithm is presented. In this algorithm, the optimal context quantizer is described by the chromosome which contains the optimal number of classes and the corresponding cluster centers. The adaptive code length is used to evaluate the fitness value to find the best chromosome. The rules for the selection, the crossover and the mutation operations are discussed. A K-means operator is incorporated in each iteration to accelerate the convergence of the algorithm. The optimized context quantizer can be obtained without the prior knowledge of the number of classes. Simulations indicate that the proposed algorithm produces results that approximate the best result obtained by the K-means-based context quantization with lower computational complexity.

关键词： Genetic algorithm context quantization K-means

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context quantization Under the Minimum Increment of the Adaptive Code Length

Context Quantization Under the Minimum Increment of the Adap...

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International Conference on Information Technology and Applications (ITA)

作者： Chen, Min Liu, Chen Wang, Fuyan Yunnan Univ Informat Coll Kunming Peoples R China Kunming Univ Informat & Technol Coll Kunming Peoples R China

ISBN: (纸本)9781479928767

In this paper, the context quantization for I-ary source based on the affinity propagation algorithm is presented. In this algorithm, the design objective of the context quantizer is aimed to minimize the adaptive code length of the source sequence. In purpose of finding the optimal number of classes, the increment of the adaptive code length is suggested to be the similarity measure of two conditional probability distributions, by which the similarity matrix is constructed as the input of the affinity propagation algorithm. After the given number of iterations, the optimal quantizer with the optimal number of classes is achieved and the adaptive code length is minimized at the same time. The simulations indicate that the proposed algorithm produces results that are better than the results obtained by the minimum conditional entropy context quantization implemented by K-means with lower computational complexity.

关键词： context quantization affinity propagation increment of the adaptive code length

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Optimal context quantization in lossless compression of image data sequences

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2004年第4期13卷 509-517页

作者： Forchhammer, S Wu, XL Andersen, JD Tech Univ Denmark Res Ctr COM DK-2800 Lyngby Denmark McMaster Univ Dept Elect & Comp Engn Hamilton ON L8G 4M2 Canada

In image compression context-based entropy coding is commonly used. A critical issue to the performance of context-based image coding is how to resolve the conflict of a desire for large templates to model high-order statistic dependency of the pixels and the problem of context dilution due to insufficient sample statistics of a given input image. We consider the problem of finding the optimal quantizer Q that quantizes the K-dimensional causal context C-t = (Xt-t1, Xt-t2,..., Xt-tk) of a source symbol X-t into one of a set of conditioning states. The optimality of context quantization is defined to be the minimum static or minimum adaptive code length of given a data set. For a binary source alphabet an optimal context quantizer can be computed exactly by a fast dynamic programming algorithm. Faster approximation solutions are also proposed. In case of m-ary source alphabet a random variable can be decomposed into a sequence of binary decisions, each of which is coded using optimal context quantization designed for the corresponding binary random variable. This optimized coding scheme is applied to digital maps and a-plane sequences. The proposed optimal context quantization technique can also be used to establish a lower bound on the achievable code length, and hence is a useful tool to evaluate the performance of existing heuristic context quantizers.

关键词： adaptive code length context modeling context quantization dynamic programming entropy coding

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Mutual information-based context quantization

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2010年第1期25卷 64-74页

作者： Cagnazzo, Marco Antonini, Marc Barlaud, Michel TELECOM ParisTech F-75634 Paris France I3S Lab F-06903 Sophia Antipolis France

context-based lossless coding suffers in many cases from the so-called context dilution problem, which arises when, in order to model high-order statistic dependencies among data, a large number of contexts is used. In this case the learning process cannot be fed with enough data, and so the probability estimation is not reliable. To avoid this problem, state-of-the-art algorithms for lossless image coding resort to context quantization (CQ) into a few conditioning states, whose statistics are easier to estimate in a reliable way. It has been early recognized that in order to achieve the best compression ratio, contexts have to be grouped according to a maximal mutual information criterion. This leads to quantization algorithms which are able to determine a local minimum of the coding cost in the general case, and even the global minimum in the case of binary-valued input. This paper surveys the CQ problem and provides a detailed analytical formulation of it, allowing to shed light on some details of the optimization process. As a consequence we find that state-of-the-art algorithms have a suboptimal step. The proposed approach allows a steeper path toward the cost function minimum. Moreover, some sufficient conditions are found that allow to find a globally optimal solution even when the input alphabet is not binary. Even though the paper mainly focuses on the theoretical aspects of CQ a number of experiments to validate the proposed method have been performed (for the special case of segmentation map lossless coding), and encouraging results have been recorded. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Lossless coding context dilution context quantization

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Optimized context quantization for I-ary source 2

Optimized Context quantization for I-ary source

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2nd International Conference on Information Science and Control Engineering

作者： Chen, Min Xue, Jie Yunnan Police Officer Acad Dept Informat Secur Kunming Peoples R China

ISBN: (纸本)9781467368506

In this paper, the optimal context quantization for the I - ary source is present. By considering correlations among values of source symbols, these conditional probability distributions are sorted by values of conditions firstly. Then the dynamic programming is used to implement the context quantization. The description length of the context model is used as the judgment parameter. Based on the criterion that the neighbourhood conditional probability distributions could be merged, our algorithm finds the optimal structure with minimum description length and the optimal context quantization results could be achieved. The experiment results indicate that the proposed algorithm could achieve the similar result with other adaptive context quantization algorithms with reasonable computational complexity.

关键词： context quantization Description Length Dynamic Programming

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context-Based Predictor Blending for Lossless Color Image Compression

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2016年第4期26卷 687-695页

作者： Strutz, Tilo Leipzig Univ Telecommun Deutsch Telekom Inst Commun Engn D-04277 Leipzig Germany

Images are typically nonstationary signals. If prediction is applied in a linear fashion, it must be combined with a technique that takes this characteristic into account. In general, images can either be regarded as piecewise 2-D autoregressive processes or they are handled in a blockwise manner. This paper presents a novel prediction technique, which treats the image data as an interleaved sequence generated by multiple sources. The challenge is to deinterleave the sequence and to compute prediction weights for each subsource separately. The proposed approach adaptively determines the subsources based on the textures surrounding the pixels. The new linear prediction technique is combined with template-matching prediction and a blending method that considers the correlation between the predictors' estimates is proposed. The prediction method is incorporated in a framework for lossless color image compression. In combination with an adaptive color transform and a dedicated coding algorithm, the proposed approach shows a competitive compression performance for a wide range of natural color images.

关键词： context quantization least-mean-square (LMS) filtering lossless image compression predictor blending

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Lossless compression of color mosaic images

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2006年第6期15卷 1379-1388页

作者： Zhang, Ning Wu, Xiaolin McMaster Univ Dept Elect & Comp Engn Hamilton ON L8K 4K1 Canada

Lossless compression of color mosaic images poses a unique and interesting problem of spectral decorrelation of spatially interleaved R, G, B samples. We investigate reversible lossless spectral-spatial transforms that can remove statistical redundancies in both spectral and spatial domains and discover that a particular wavelet decomposition scheme, called Mallat wavelet packet transform, is ideally suited to the task of decorrelating color mosaic data. We also propose a low-complexity adaptive context-based Golomb-Rice coding technique to compress the coefficients of Mallat wavelet packet transform. The lossless compression performance of the proposed method on color mosaic images is apparently the best so far among the existing lossless image codecs.

关键词： context quantization entropy coding digital camera image compression

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New Efficient Image Compression Method For Single Sensor Digital Camera Images

<bold>New Efficient Image Compression Method For Single Sens...

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7th International Conference on Computational Intelligence and Multimedia Applications

作者： Nallaperumal, Krishnan Christopher, Seldev Vinsley, S. S. Selvakumar, R. K. MS Univ Tirunelveli India CSI Inst Technol Thovalai India Narayanaguru Coll Engg Manjalamoodu India KCE & Technol Virudunagar India

ISBN: (纸本)0769530508

Widely used digital cameras nowadays rise single sensor color filter array (CFA) which captures only one component of a pixel among the three components Red, Green and Blue (RGB). This interleaved RGB valued images are called mosaic images. The mosaic images are of one third of the RGB image's size. The mosaic image can be compressed further to reduce storage. In this paper we propose a new efficient method of lossless compression for color mosaic images. First the mosaic image is transformed by 5/3 forward wavelet transforms, which is best suited for color mosaic data. We also proposed a low complexity Adaptive context-based Modified Golomb-Rice coding technique to compress the coefficients of the *** lossless compression performance of the proposed method on color mosaic images is arguably the best so far among the existing lossless image codecs.

关键词： 5/3 wavelet transform context quantization entropy coding image compression

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