  The semantic information of the image for intelligent tasks is hidden behind
the pixels, and slight changes in the pixels will affect the performance of
intelligent tasks. In order to preserve semantic information behind pixels for
intelligent tasks during wireless image transmission, we propose a joint
source-channel coding method based on semantics of pixels, which can improve
the performance of intelligent tasks for images at the receiver by retaining
semantic information. Specifically, we first utilize gradients of intelligent
task's perception results with respect to pixels to represent the semantic
importance of pixels. Then, we extract the semantic distortion, and train the
deep joint source-channel coding network with the goal of minimizing semantic
distortion rather than pixel's distortion. Experiment results demonstrate that
the proposed method improves the performance of the intelligent classification
task by 1.38% and 66% compared with the SOTA deep joint source-channel coding
method and the traditional separately source-channel coding method at the same
transmission ra te and signal-to-noise ratio.
