Full Resolution Image Compression with Recurrent Neural Networks

CVPR 2017 George TodericiDamien VincentNick JohnstonSung Jin HwangDavid MinnenJoel ShorMichele Covell

This paper presents a set of full-resolution lossy image compression methods based on neural networks. Each of the architectures we describe can provide variable compression rates during deployment without requiring retraining of the network: each network need only be trained once... (read more)

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