Generative image compression by prediction of optimal realism levels

• A novel LIC model uses a predictor that automatically determines realism levels • We propose a two-stage training framework for stable training of the LIC model • Optimal realism levels determine the quality of distortion and perception of the image • We achieved significant results in perceptual metrics Learned image compression (LIC) methods have demonstrated high-quality reconstruction, and even surpassed the traditional codec algorithms at rate-distortion performance. However, reconstructing natural and realistic textures remains a challenge. The main problem is the distortion-realism trade-off. Realism deteriorates quickly if we minimize distortion, and vice versa. The existing methods manually determine and apply a realism level, which is a degree of the trade-off, to an entire image, resulting in time-consuming training and suboptimal quality. In this paper, we propose a novel LIC model equipped with a predictor that automatically determines realism levels for regions of the image. Thus, we reconstruct images using the optimal realism levels. Moreover, we developed a two-stage training framework for stable training of the proposed model. The experimental results show that the proposed model achieved significant perceptual quality while the PSNR is comparable to the existing methods.