The paper introduces a novel macro-pixel prediction method based on Convolutional Neural Networks (CNN) for lossless compression of light field images. In the proposed method, each macro-pixel is predicted based on a volume of macro-pixels from its immediate causal neighborhood. The proposed deep neural network operates on these macro-pixel volumes and provides accurate macro-pixel prediction in light field images. The resulting macro-pixel residuals are encoded by a reference codec built based on the {CALIC} codec. A context modeling method for light field images is proposed. Experimental results on a large light field image dataset show that the proposed prediction method systematically and substantially outperforms state-of-the-art predictors. To our knowledge, the paper is the first to introduce deep-learning based prediction of macro-pixels, enabling efficient lossless compression of light field images.
Schiopu, I & Munteanu, A 2018, Macro-pixel prediction based on convolutional neural networks for lossless compression of light field images. in 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings., 8451731, IEEE, Athens, Greece, pp. 445-449, IEEE International Conference on Image Processing 2018, Athens, Greece, 7/10/18. https://doi.org/10.1109/ICIP.2018.8451731
Schiopu, I., & Munteanu, A. (2018). Macro-pixel prediction based on convolutional neural networks for lossless compression of light field images. In 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings (pp. 445-449). Article 8451731 IEEE. https://doi.org/10.1109/ICIP.2018.8451731
@inproceedings{5bb05e14df7a42cdac95dfa494e277f7,
title = "Macro-pixel prediction based on convolutional neural networks for lossless compression of light field images",
abstract = "The paper introduces a novel macro-pixel prediction method based on Convolutional Neural Networks (CNN) for lossless compression of light field images. In the proposed method, each macro-pixel is predicted based on a volume of macro-pixels from its immediate causal neighborhood. The proposed deep neural network operates on these macro-pixel volumes and provides accurate macro-pixel prediction in light field images. The resulting macro-pixel residuals are encoded by a reference codec built based on the {CALIC} codec. A context modeling method for light field images is proposed. Experimental results on a large light field image dataset show that the proposed prediction method systematically and substantially outperforms state-of-the-art predictors. To our knowledge, the paper is the first to introduce deep-learning based prediction of macro-pixels, enabling efficient lossless compression of light field images.",
keywords = "CNN-based prediction, Intra prediction, Light field images, Lossless compression, Macro-pixel",
author = "Ionut Schiopu and Adrian Munteanu",
year = "2018",
month = aug,
day = "29",
doi = "10.1109/ICIP.2018.8451731",
language = "English",
isbn = "978-1-4799-7062-9",
pages = "445--449",
booktitle = "2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings",
publisher = "IEEE",
note = "IEEE International Conference on Image Processing 2018, ICIP 2018 ; Conference date: 07-10-2018 Through 10-10-2018",
url = "https://2018.ieeeicip.org/",
}