We propose conditional perceptual quality, an extension of the perceptual quality defined in \citet{blau2018perception}, by conditioning it on user defined information. Specifically, we extend the original perceptual quality $d(p_{X},p_{\hat{X}})$ to the conditional perceptual quality $d(p_{X|Y},p_{\hat{X}|Y})$, where $X$ is the original image, $\hat{X}$ is the reconstructed, $Y$ is side information defined by user and $d(.,.)$ is divergence. We show that conditional perceptual quality has similar theoretical properties as rate-distortion-perception trade-off \citep{blau2019rethinking}. Based on these theoretical results, we propose an optimal framework for conditional perceptual quality preserving compression. Experimental results show that our codec successfully maintains high perceptual quality and semantic quality at all bitrate. Besides, by providing a lowerbound of common randomness required, we settle the previous arguments on whether randomness should be incorporated into generator for (conditional) perceptual quality compression. The source code is provided in supplementary material.

翻译：我们提出了条件感知质量，这是对\citet{blau2018perception}中定义的感知质量的扩展，通过将其条件化于用户定义的信息。具体来说，我们将原始感知质量$d(p_{X},p_{\hat{X}})$扩展为条件感知质量$d(p_{X|Y},p_{\hat{X}|Y})$，其中$X$是原始图像，$\hat{X}$是重建图像，$Y$是用户定义的侧信息，$d(.,.)$是散度。我们证明了条件感知质量与率失真感知权衡\citep{blau2019rethinking}具有类似的理论性质。基于这些理论结果，我们提出了一种用于条件感知质量保持压缩的最优框架。实验结果表明，我们的编解码器在所有比特率下成功保持了高感知质量和语义质量。此外，通过提供所需公共随机性的下界，我们解决了先前关于是否应将随机性纳入生成器以实现（条件）感知质量压缩的争论。源代码见补充材料。