Recent deep learning methods have led to increased interest in solving
high-efficiency end-to-end transmission problems. These methods, we call
nonlinear transform source-channel coding (NTSCC), extract the semantic latent
features of source signal, and learn entropy model to guide the joint
source-channel coding with variable rate to transmit latent features over
wireless channels. In this paper, we propose a comprehensive framework for
improving NTSCC, thereby higher system coding gain, better model versatility,
and more flexible adaptation strategy aligned with semantic guidance are all
achieved. This new sophisticated NTSCC model is now ready to support large-size
data interaction in emerging XR, which catalyzes the application of semantic
communications. Specifically, we propose three useful improvement approaches.
First, we introduce a contextual entropy model to better capture the spatial
correlations among the semantic latent features, thereby more accurate rate
allocation and contextual joint source-channel coding are developed accordingly
to enable higher coding gain. On that basis, we further propose response
network architectures to formulate versatile NTSCC, i.e., once-trained model
supports various rates and channel states that benefits the practical
deployment. Following this, we propose an online latent feature editing method
to enable more flexible coding rate control aligned with some specific semantic
guidance. By comprehensively applying the above three improvement methods for
NTSCC, a deployment-friendly semantic coded transmission system stands out
finally. Our improved NTSCC system has been experimentally verified to achieve
considerable bandwidth saving versus the state-of-the-art engineered VTM + 5G
LDPC coded transmission system with lower processing latency