  In this paper, we propose a class of high-efficiency deep joint
source-channel coding methods that can closely adapt to the source distribution
under the nonlinear transform, it can be collected under the name nonlinear
transform source-channel coding (NTSCC). In the considered model, the
transmitter first learns a nonlinear analysis transform to map the source data
into latent space, then transmits the latent representation to the receiver via
deep joint source-channel coding. Our model incorporates the nonlinear
transform as a strong prior to effectively extract the source semantic features
and provide side information for source-channel coding. Unlike existing
conventional deep joint source-channel coding methods, the proposed NTSCC
essentially learns both the source latent representation and an entropy model
as the prior on the latent representation. Accordingly, novel adaptive rate
transmission and hyperprior-aided codec refinement mechanisms are developed to
upgrade deep joint source-channel coding. The whole system design is formulated
as an optimization problem whose goal is to minimize the end-to-end
transmission rate-distortion performance under established perceptual quality
metrics. Across test image sources with various resolutions, we find that the
proposed NTSCC transmission method generally outperforms both the analog
transmission using the standard deep joint source-channel coding and the
classical separation-based digital transmission. Notably, the proposed NTSCC
method can potentially support future semantic communications due to its
content-aware ability and perceptual optimization goal.
