A New Semiparametric Finite Mixture Model-Based Adaptive Arithmetic Coding for Lossless Image Compression

Abstract : 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.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01347161
Contributor : Isabelle Gouat <>
Submitted on : Wednesday, July 20, 2016 - 2:51:59 PM
Last modification on : Thursday, February 28, 2019 - 12:04:04 PM

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Atef Masmoudi, Afif Masmoudi, William Puech. A New Semiparametric Finite Mixture Model-Based Adaptive Arithmetic Coding for Lossless Image Compression. Circuits, Systems, and Signal Processing, Springer Verlag, 2016, 35 (4), pp.1163-1186. ⟨10.1007/s00034-015-0103-8⟩. ⟨lirmm-01347161⟩

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