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Adaptive combination of linear predictors for lossless image compression

IEE PROCEEDINGS-SCIENCE MEASUREMENT AND TECHNOLOGY 2000年第6期147卷 414-419页

作者： Deng, G Ye, H Cahill, LW La Trobe Univ Dept Elect Engn Bundoora Vic 3083 Australia

lossless image coding is an essential requirement for medical imaging applications. lossless image compression techniques usually have two major components: adaptive prediction and adaptive entropy coding. The paper is concerned with adaptive prediction. Recently, several researchers have studied prediction schemes in which the final prediction is formed by a combination of a group of subpredictors. The authors present an overview of this new type of prediction technique. They show that the basic principle of adaptive predictor combination has been extensively studied and applied to many science and engineering problems. They then describe their own combination scheme, which is based on the estimation of the local prediction error variance. Experimental results show that the compression performance of the algorithms that employ this new type of predictor is consistently better than that of state-of-the-art algorithms.

关键词： adaptive prediction adaptive signal processing adaptive entropy coding data compression Biology and medical computing error variance Patient diagnostic methods and instrumentation health physics medical diagnostic imaging Computer vision and image processing techniques image coding Biomedical measurement and imaging adaptive combination final prediction Optical, image and video signal processing algorithms linear predictors lossless image compression state-of-the-art algorithms Medical and biomedical uses of fields, radiations, and radioactivity medical image processing

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Reversible Integer-to-Integer Wavelet Filter Design for lossless image compression

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IEEE ACCESS 2020年 8卷 89117-89129页

作者： Lin, Jianyu Shantou Univ Dept Elect Engn Shantou 515063 Peoples R China Shantou Univ Guangdong Prov Key Lab Digital Signal & Image Pro Shantou 515063 Peoples R China Shantou Univ Minist Educ Key Lab Intelligent Mfg Technol Shantou 515063 Peoples R China

In this paper, factors that influence the lossless compression efficiency of reversible integer-to-integer (ITI) wavelet filters are presented. It is observed that there is a converged region for the bitrate of the compressed subbands. Reaching the converged region, the bitrate increment is equal to bps, where is the increment in ratio of the coefficient magnitude mean. The advantages of the 5/3 filters for lossless compression are then clearly explained. A new quantity, which measures the efficiency of reversible ITI transforms on flat spectrum inputs, is defined and is used in the new reversible filter design. First, keeping the distinctive merits of the 5/3 filters for lossless compression, the half-band product filter series of the Daubechies wavelet series is selected as the analysis high-pass filters. Next, the analysis low-pass filter is found by experimentally minimizing the value. Finally, it is determined that the 17/11 filters, which employ the half-band product filter with 6 vanishing moments, have the highest compression efficiency. Coding results show that the new 17/11 filters lead to a bitrate reduction of 1.7 & x0025;over the 5/3 filters for lossless image compression. With further enhancements on the transform part and combining the enhanced transform with more efficient entropy coding, bitrate reductions of about 3.1 & x0025;and 4 & x0025;respectively over the state-of-the-art lossless compression algorithms JPEG-LS and JPEG2000 lossless mode are achieved.

关键词： Reversible integer-to-integer wavelet transform compression efficiency lossless image compression

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Bayesian Predictor Combination for lossless image compression

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IEEE TRANSACTIONS ON image PROCESSING 2013年第12期22卷 5263-5270页

作者： Martchenko, Andrew Deng, Guang La Trobe Univ Dept Elect Engn Bundoora Vic 3086 Australia

Adaptive predictor combination (APC) is a framework for combining multiple predictors for lossless image compression and is often at the core of state-of-the-art algorithms. In this paper, a Bayesian parameter estimation scheme is proposed for APC. Extensive experiments using natural, medical, and remote sensing images of 8-16 bit/pixel have confirmed that the predictive performance is consistently better than that of APC for any combination of fixed predictors and with only a marginal increase in computational complexity. The predictive performance improves with every additional fixed predictor, a property that is not found in other predictor combination schemes studied in this paper. Analysis and simulation show that the performance of the proposed algorithm is not sensitive to the choice of hyper-parameters of the prior distributions. Furthermore, the proposed prediction scheme provides a theoretical justification for the error correction stage that is often included as part of a prediction process.

关键词： lossless image compression Bayesian learning adaptive prediction entropy coding context modeling

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Prediction capabilities of Boolean and stack filters for lossless image compression

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 1999年第2期10卷 161-187页

作者： Petrescu, D Tabus, I Gabbouj, M Nokia Res Ctr FIN-33720 Tampere Finland Tampere Univ Signal Proc Lab FIN-33101 Tampere Finland

This paper proposes optimal Boolean, stack, and FIR-Boolean hybrid filters for realizing the prediction stage in lossless grey-level image compression. New optimal design procedures for Boolean filters are introduced, where the optimality criterion is the Error Entropy (EE). The use of the EE-optimal and MAE-optimal Boolean and stack filters in the sequential prediction structure is considered, under different instances: global-optimal, block-optimal, adaptive-size-block-optimal and multiresolution. An extensive simulation study is carried out for analyzing and comparing the performances of the newly introduced predictors and various other sequential predictors. The EE-optimal Boolean predictors prove to be the most efficient predictors. More refined filtering structures, such as block-optimal or adaptive-size-block-optimal are suitable for the prediction task when the prediction mask ought to be small. The proposed progressive transmission structure based on optimal Boolean prediction is shown to outperform HINT [1] progressive lossless coding scheme.

关键词： lossless image compression stack filters Boolean filters FIR Boolean hybrid predictors

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A finite mixture model of geometric distributions for lossless image compression

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SIGNAL image AND VIDEO PROCESSING 2016年第4期10卷 671-678页

作者： Masmoudi, Atef Chaoui, Slim Masmoudi, Afif Univ Sfax Lab Elect & Technol Informat Sfax Tunisia Univ Montpellier 2 UMR CNRS 5506 LIRMM F-34392 Montpellier 05 France Univ Carthage Lab Elect Syst & Commun Networks Carthage Tunisia Univ Sfax Lab Stat & Probabil Sfax Tunisia

In this paper, we proposed a new geometric finite mixture model-based adaptive arithmetic coding (AAC) for lossless image compression. Applying AAC for image compression, large compression gains can be achieved only through the use of sophisticated models that provide more accurate probabilistic descriptions of the image. In this work, we proposed to divide the residual image into non-overlapping blocks, and then we model the statistics of each block by a mixture of geometric distributions of parameters estimated through the maximum likelihood estimation using the expectation-maximization algorithm. Moreover, a histogram tail truncation method within each predicted error block is used in order to reduce the number of symbols in the arithmetic coding and therefore to reduce the effect of the zero-occurrence symbols. Experimentally, we showed that using convenient block size and number of mixture components in conjunction with the prediction technique median edge detector, the proposed method outperforms the well known lossless image compressors.

关键词： Adaptive arithmetic coding lossless image compression Expectation-maximization algorithm Mixture of geometric distributions

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Signal-dependent wavelet transform and application to lossless image compression

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ELECTRONICS LETTERS 2002年第4期38卷 170-172页

作者： Yoo, H Joeng, J Hanyang Univ Dept Elect Commun Engn Seoul 133791 South Korea

A signal-dependent wavelet transform based on the lifting scheme is proposed. The transform can be made reversible (i.e. an integer-to-integer transform). The reversible transform. followed by arithmetic coding, is applied to lossless image compression. Simulation results indicate that the proposed method is superior to the S + P method.

关键词： wavelet transforms arithmetic coding simulation lifting scheme data compression image and video coding arithmetic codes reversible transform signal-dependent wavelet transform Integral transforms image coding S+P method integer-to-integer transform lossless image compression Computer vision and image processing techniques image recognition

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A New Semiparametric Finite Mixture Model-Based Adaptive Arithmetic Coding for lossless image compression

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2016年第4期35卷 1163-1186页

作者： Masmoudi, Atef Masmoudi, Afif Puech, William Natl Engn Sch Sfax Lab Elect & Technol Informat Sfax Tunisia Univ Montpellier I CNRS UMR 5506 LIRMM F-34392 Montpellier 05 France Univ Sfax Fac Sci Sfax Lab Probabil & Stat Sfax Tunisia

In this paper, we propose a new approach for block-based lossless image compression by defining a new semiparametric finite mixture model-based adaptive arithmetic coding. Conventional adaptive arithmetic encoders start encoding a sequence of symbols with a uniform distribution, and they update the frequency of each symbol by incrementing its count after it has been encoded. When encoding an image row by row or block by block, conventional adaptive arithmetic encoders provide the same compression results. In addition, images are normally non-stationary signals, which means that different areas in an image have different probability distributions, so conventional adaptive arithmetic encoders which provide probabilities for the whole image are not very efficient. In the proposed compression scheme, an image is divided into non-overlapping blocks of pixels, which are separately encoded with an appropriate statistical model. Hence, instead of starting to encode each block with a uniform distribution, we propose to start with a probability distribution which is modeled by a semiparametric mixture obtained from the distributions of its neighboring blocks. The semiparametric model parameters are estimated through maximum likelihood using the expectation-maximization algorithm in order to maximize the arithmetic coding efficiency. The results of comparative experiments show that we provide significant improvements over conventional adaptive arithmetic encoders and the state-of-the-art lossless image compression standards.

关键词： Arithmetic coding Semiparametric mixture models Expectation-maximization algorithm lossless image compression

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Prediction by partial approximate matching for lossless image compression

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IEEE TRANSACTIONS ON image PROCESSING 2008年第6期17卷 924-935页

作者： Zhang, Yong Adjeroh, Donald A. Methodist Hosp Res Inst Dept Radiol Ctr Biotechnol & Informat Houston TX 77030 USA W Virginia Univ Lane Dept Comp Sci & Elect Engn VIP Lab Morgantown WV 26506 USA

Context-based modeling is an important step in high-performance lossless data compression. To effectively define and utilize contexts for natural images is, however, a difficult problem. This is primarily due to the,huge number of contexts available in natural images, which typically results in higher modeling costs, leading to reduced compression efficiency. Motivated by the prediction by partial matching context model that has been very successful in text compression, we present prediction by partial approximate matching (PPAM), a method for compression and context modeling for images. Unlike the PPM modeling method that uses exact contexts, PPAM introduces the notion of approximate contexts. Thus, PPAM models the probability of the encoding symbol based on its previous contexts, whereby context occurrences are considered in an approximate manner. The proposed method has competitive compression performance when compared with other popular lossless image compression algorithms. It shows a particularly superior performance when compressing images that have common features, such as biomedical images.

关键词： context modeling lossless image compression prediction by partial approximate matching (PPAM) prediction by partial matching (PPM)

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An interpolative subband coding algorithm for lossless image compression

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SIGNAL PROCESSING-image COMMUNICATION 1999年第9期14卷 721-736页

作者： Deng, G La Trobe Univ Sch Elect Engn Bundoora Vic 3083 Australia

This paper presents a new interpolative subband coding (ISC) algorithm for efficient lossless image compression. An investigation of the decorrelation performance of typical interpolation techniques is presented. A new representation of the subband data and a new context modelling scheme are proposed. Simulations have shown that the ISC algorithm results in an average bit-rate very close to that of the CALIC algorithm and almost the same as that of the S+P transform. The decorrelation part of the ISC is also simpler than that of the S+P transform. Thus, the proposed method provides an alternative way for efficient lossless image compression. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： lossless image compression progressive image transmission adaptive entropy coding context modelling

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Invertible update-then-predict integer lifting wavelet for lossless image compression

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2017年第1期2017卷 1-9页

作者： Chen, Dong Li, Yanjuan Zhang, Haiying Gao, Wenpeng Harbin Inst Technol Sch Life Sci & Technol 92 Dazhi West St Harbin 150001 Peoples R China North East Forestry Univ Sch Informat & Comp Engn 26 Hexing St Harbin 150040 Peoples R China Xiamen Univ Software Sch Xiamen 361005 Peoples R China

This paper presents a new wavelet family for lossless image compression by re-factoring the channel representation of the update-then-predict lifting wavelet, introduced by Claypoole, Davis, Sweldens and Baraniuk, into lifting steps. We name the new wavelet family as invertible update-then-predict integer lifting wavelets (IUPILWs for short). To build IUPILWs, we investigate some central issues such as normalization, invertibility, integer structure, and scaling lifting. The channel representation of the previous update-then-predict lifting wavelet with normalization is given and the invertibility is discussed firstly. To guarantee the invertibility, we re-factor the channel representation into lifting steps. Then the integer structure and scaling lifting of the invertible update-then-predict wavelet are given and the IUPILWs are built. Experiments show that comparing with the integer lifting structure of 5/3 wavelet, 9/7 wavelet, and iDTT, IUPILW results in the lower bit-rates for lossless image compression.

关键词： Integer lifting Invertibility lossless image compression Update-then-predict Wavelet

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