Lossy image compression aims to represent images in as few bits as possible while maintaining fidelity to the original. Theoretical results indicate that optimizing distortion metrics such as PSNR or MS-SSIM necessarily leads to a discrepancy in the statistics of original images from those of reconstructions, in particular at low bitrates, often manifested by the blurring of the compressed images. Previous work has leveraged adversarial discriminators to improve statistical fidelity. Yet these binary discriminators adopted from generative modeling tasks may not be ideal for image compression. In this paper, we introduce a non-binary discriminator that is conditioned on quantized local image representations obtained via VQ-VAE autoencoders. Our evaluations on the CLIC2020, DIV2K and Kodak datasets show that our discriminator is more effective for jointly optimizing distortion (e.g., PSNR) and statistical fidelity (e.g., FID) than the PatchGAN of the state-of-the-art HiFiC model. On CLIC2020, we obtain the same FID as HiFiC with 30-40\% fewer bits.

翻译：有损图像压缩旨在以尽可能少的比特数表示图像，同时保持与原始图像的保真度。理论结果表明，优化失真指标（如PSNR或MS-SSIM）必然会导致原始图像与重建图像在统计特性上存在差异，尤其是在低比特率时，这种差异常表现为压缩图像模糊。先前的研究利用对抗鉴别器来改善统计保真度。然而，这些从生成式建模任务中借鉴的二元鉴别器可能并非图像压缩的理想选择。本文提出一种非二元鉴别器，该鉴别器以通过VQ-VAE自编码器获得的量化局部图像表示为条件。我们在CLIC2020、DIV2K和Kodak数据集上的评估表明，与最先进HiFiC模型中的PatchGAN相比，我们的鉴别器在联合优化失真（如PSNR）和统计保真度（如FID）方面更为有效。在CLIC2020上，我们以比特数减少30-40%实现了与HiFiC相同的FID值。