  In the past years, learned image compression (LIC) has achieved remarkable
performance. The recent LIC methods outperform VVC in both PSNR and MS-SSIM.
However, the low bit-rate reconstructions of LIC suffer from artifacts such as
blurring, color drifting and texture missing. Moreover, those varied artifacts
make image quality metrics correlate badly with human perceptual quality. In
this paper, we propose PO-ELIC, i.e., Perception-Oriented Efficient Learned
Image Coding. To be specific, we adapt ELIC, one of the state-of-the-art LIC
models, with adversarial training techniques. We apply a mixture of losses
including hinge-form adversarial loss, Charbonnier loss, and style loss, to
finetune the model towards better perceptual quality. Experimental results
demonstrate that our method achieves comparable perceptual quality with HiFiC
with much lower bitrate.
