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.

翻译：近年来，学习型图像压缩方案凭借其高效的非线性变换、端到端优化框架等优势，在图像保真度指标（如PSNR和MS-SSIM）上已显著超越传统混合图像编码方法。然而，这些方案鲜少考虑人类视觉系统（HVS）的恰可察觉差异（JND）特性，未能针对感知质量优化学习型图像压缩。针对该问题，本文提出一种基于JND的感知质量损失函数。考虑到不同量化参数（QP）下不同训练阶段的压缩图像失真量存在差异，我们开发了失真感知调节器。通过将两者结合，能够在JND引导下更合理地分配压缩图像中的失真，从而保持高感知质量。上述设计使所提方法具备高度可扩展性和即插即用优势，可灵活应用于多种学习型图像压缩方案。在Kodak数据集上的实验结果表明，在相同比特率条件下，该方法相较于基线模型取得了更优的感知质量。