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.

PO-ELIC 提出了一种感知导向的高效学习图像编码模型，通过采用对抗性训练技术，包括铰链形式的对抗损失、Charbonnier 式损失和样式损失，进一步提高学习图像压缩技术的感知品质，在更低的比特率下提供了与 HiFiC 相当的感知品质。