@InProceedings{8903128,
  author = {K. Unno and Y. Kameda and I. Matsuda and S. Itoh and S. Naito},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {Lossless Image Coding Exploiting Local and Non-local Information via Probability Model Optimization},
  year = {2019},
  pages = {1-5},
  abstract = {We previously proposed a lossless image coding method based on examples search and probability model optimization. In this paper, we improve coding efficiency of the method by introducing an adaptive prediction technique. Specifically, multiple affine predictors are trained pel-by-pel by using causal neighbor pels, and the predicted values obtained by those predictors are used for generating the probability model. Therefore, both non-local information by the examples search and local information by the adaptive prediction are used together in the probability modeling. Furthermore, an optimization method for the number of examples is also proposed in this paper. Experimental results show that the proposed method achieves better coding rates than the state-of-the-art lossless coding schemes.},
  keywords = {image coding;optimisation;probability;nonlocal information;probability model optimization;lossless image coding method;adaptive prediction technique;multiple affine predictors;probability modeling;optimization method;pel-by-pel training;lossless coding schemes;Lossless coding;Example search;Affine prediction;Probability modeling;Quasi-Newton method},
  doi = {10.23919/EUSIPCO.2019.8903128},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570529561.pdf},
}

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In this paper, we improve coding efficiency of the method by introducing an adaptive prediction technique. Specifically, multiple affine predictors are trained pel-by-pel by using causal neighbor pels, and the predicted values obtained by those predictors are used for generating the probability model. Therefore, both non-local information by the examples search and local information by the adaptive prediction are used together in the probability modeling. Furthermore, an optimization method for the number of examples is also proposed in this paper. Experimental results show that the proposed method achieves better coding rates than the state-of-the-art lossless coding schemes.","keywords":"image coding;optimisation;probability;nonlocal information;probability model optimization;lossless image coding method;adaptive prediction technique;multiple affine predictors;probability modeling;optimization method;pel-by-pel training;lossless coding schemes;Lossless coding;Example search;Affine prediction;Probability modeling;Quasi-Newton method","doi":"10.23919/EUSIPCO.2019.8903128","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570529561.pdf","bibtex":"@InProceedings{8903128,\n author = {K. Unno and Y. Kameda and I. Matsuda and S. Itoh and S. Naito},\n booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},\n title = {Lossless Image Coding Exploiting Local and Non-local Information via Probability Model Optimization},\n year = {2019},\n pages = {1-5},\n abstract = {We previously proposed a lossless image coding method based on examples search and probability model optimization. In this paper, we improve coding efficiency of the method by introducing an adaptive prediction technique. Specifically, multiple affine predictors are trained pel-by-pel by using causal neighbor pels, and the predicted values obtained by those predictors are used for generating the probability model. Therefore, both non-local information by the examples search and local information by the adaptive prediction are used together in the probability modeling. Furthermore, an optimization method for the number of examples is also proposed in this paper. 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