  As one novel approach to realize end-to-end wireless image semantic
transmission, deep learning-based joint source-channel coding (deep JSCC)
method is emerging in both deep learning and communication communities.
However, current deep JSCC image transmission systems are typically optimized
for traditional distortion metrics such as peak signal-to-noise ratio (PSNR) or
multi-scale structural similarity (MS-SSIM). But for low transmission rates,
due to the imperfect wireless channel, these distortion metrics lose
significance as they favor pixel-wise preservation. To account for human visual
perception in semantic communications, it is of great importance to develop new
deep JSCC systems optimized beyond traditional PSNR and MS-SSIM metrics. In
this paper, we introduce adversarial losses to optimize deep JSCC, which tends
to preserve global semantic information and local texture. Our new deep JSCC
architecture combines encoder, wireless channel, decoder/generator, and
discriminator, which are jointly learned under both perceptual and adversarial
losses. Our method yields human visually much more pleasing results than
state-of-the-art engineered image coded transmission systems and traditional
deep JSCC systems. A user study confirms that achieving the perceptually
similar end-to-end image transmission quality, the proposed method can save
about 50\% wireless channel bandwidth cost.
