Full Resolution Image Compression with Recurrent Neural Networks

CVPR 2017 • George Toderici • Damien Vincent • Nick Johnston • Sung Jin Hwang • David Minnen • Joel Shor • Michele 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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METHOD	TYPE
Sigmoid Activation	Activation Functions
Tanh Activation	Activation Functions
Adam	Stochastic Optimization
1x1 Convolution	Convolutions
Convolution	Convolutions
LSTM	Recurrent Neural Networks
Masked Convolution	Convolutions
PixelRNN	Generative Models
Residual GRU	Recurrent Neural Networks
Residual Connection	Skip Connections
GRU	Recurrent Neural Networks

METHOD

TYPE

Sigmoid Activation