Semantic-aided Parallel Image Transmission Compatible with Practical System
Journal:
arXiv
Published Date:
Apr 30, 2025
Abstract
In this paper, we propose a novel semantic-aided image communication
framework for supporting the compatibility with practical separation-based
coding architectures. Particularly, the deep learning (DL)-based joint
source-channel coding (JSCC) is integrated into the classical separate
source-channel coding (SSCC) to transmit the images via the combination of
semantic stream and image stream from DL networks and SSCC respectively, which
we name as parallel-stream transmission. The positive coding gain stems from
the sophisticated design of the JSCC encoder, which leverages the residual
information neglected by the SSCC to enhance the learnable image features.
Furthermore, a conditional rate adaptation mechanism is introduced to adjust
the transmission rate of semantic stream according to residual, rendering the
framework more flexible and efficient to bandwidth allocation. We also design a
dynamic stream aggregation strategy at the receiver, which provides the
composite framework with more robustness to signal-to-noise ratio (SNR)
fluctuations in wireless systems compared to a single conventional codec.
Finally, the proposed framework is verified to surpass the performance of both
traditional and DL-based competitors in a large range of scenarios and
meanwhile, maintains lightweight in terms of the transmission and computational
complexity of semantic stream, which exhibits the potential to be applied in
real systems.
