In this paper, we extend our prior research named DKIC and propose the perceptual-oriented learned image compression method, PO-DKIC. Specifically, DKIC adopts a dynamic kernel-based dynamic residual block group to enhance the transform coding and an asymmetric space-channel context entropy model to facilitate the estimation of gaussian parameters. Based on DKIC, PO-DKIC introduces PatchGAN and LPIPS loss to enhance visual quality. Furthermore, to maximize the overall perceptual quality under a rate constraint, we formulate this challenge into a constrained programming problem and use the Linear Integer Programming method for resolution. The experiments demonstrate that our proposed method can generate realistic images with richer textures and finer details when compared to state-of-the-art image compression techniques.

翻译：本文在先前名为DKIC的工作基础上进行扩展，提出感知导向型学习图像压缩方法PO-DKIC。具体而言，DKIC采用基于动态核的动态残差块组增强变换编码，并构建非对称空间-通道上下文熵模型以优化高斯参数估计。在DKIC基础上，PO-DKIC引入PatchGAN和LPIPS损失函数提升视觉质量。此外，为在码率约束下最大化整体感知质量，我们将该问题建模为约束规划问题，并采用线性整数规划方法求解。实验结果表明，与现有最先进图像压缩技术相比，所提方法能生成纹理更丰富、细节更精细的真实感图像。