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Residual-error prediction based on deep learning for lossless image compression

Residual-error prediction based on deep learning for lossless image compression

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A novel residual-error prediction method based on deep learning with application in lossless image compression is introduced. The proposed method employs machine learning tools to minimise the residual error of the employed prediction tools. Experimental results demonstrate average bitrate savings of 32% over the state-of-the-art in lossless image compression. To the best of the authors’ knowledge, this Letter is the first to propose a deep-learning based method for residual-error prediction.

References

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      • 4. Toderici, G., Vincent, D., Johnston, N., et al: ‘Full resolution image compression with recurrent neural networks’. Proc. Conf. on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, July 2017, pp. 54355443.
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      • 5. ‘UHD Images’, Available at http://www.ultrahdwallpapers.net/nature, accessed 25 August 2017.
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