  Recently, learned image compression schemes have achieved remarkable
improvements in image fidelity (e.g., PSNR and MS-SSIM) compared to
conventional hybrid image coding ones due to their high-efficiency non-linear
transform, end-to-end optimization frameworks, etc. However, few of them take
the Just Noticeable Difference (JND) characteristic of the Human Visual System
(HVS) into account and optimize learned image compression towards perceptual
quality. To address this issue, a JND-based perceptual quality loss is
proposed. Considering that the amounts of distortion in the compressed image at
different training epochs under different Quantization Parameters (QPs) are
different, we develop a distortion-aware adjustor. After combining them
together, we can better assign the distortion in the compressed image with the
guidance of JND to preserve the high perceptual quality. All these designs
enable the proposed method to be flexibly applied to various learned image
compression schemes with high scalability and plug-and-play advantages.
Experimental results on the Kodak dataset demonstrate that the proposed method
has led to better perceptual quality than the baseline model under the same bit
rate.
