  We propose a joint source and channel coding (JSCC) technique for wireless
image transmission that does not rely on explicit codes for either compression
or error correction; instead, it directly maps the image pixel values to the
complex-valued channel input symbols. We parameterize the encoder and decoder
functions by two convolutional neural networks (CNNs), which are trained
jointly, and can be considered as an autoencoder with a non-trainable layer in
the middle that represents the noisy communication channel. Our results show
that the proposed deep JSCC scheme outperforms digital transmission
concatenating JPEG or JPEG2000 compression with a capacity achieving channel
code at low signal-to-noise ratio (SNR) and channel bandwidth values in the
presence of additive white Gaussian noise (AWGN). More strikingly, deep JSCC
does not suffer from the ``cliff effect'', and it provides a graceful
performance degradation as the channel SNR varies with respect to the SNR value
assumed during training. In the case of a slow Rayleigh fading channel, deep
JSCC learns noise resilient coded representations and significantly outperforms
separation-based digital communication at all SNR and channel bandwidth values.
