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Conference Publication

Macro-pixel prediction based on convolutional neural networks for lossless compression of light field images

Host Publication: IEEE International Conference on Image Processing 2018

Authors: I. Schiopu and A. Munteanu

UsePubPlace: Athens, Greece

Publisher: IEEE

Publication Date: Aug. 2018

Number of Pages: 5

ISBN: 978-1-4799-7062-9


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.

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Dr. Ionut Schiopu

+32 (0)02 629 169

ischiopu@etrovub.be

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