SaLIC: Saliency-Enhanced Learned Image Compression for Balanced Quality

Perception-optimized Learned Image Compression (LIC) methods have recently made significant progress. They surpass both traditional image compression algorithms and non-perceptually optimized LIC methods in image sharpness, detail representation, and subjective perception, even at similar or lower bitrates. However, LIC methods optimized for perception often generate false details and textures in reconstructions, leading to underperformance in objective metrics like PSNR and MS-SSIM, which limits their applicability. In this paper, we introduce a comprehensive loss metric based on saliency detection that aids in achieving exceptional perceptual quality while minimizing distortions. By applying this metric in training, we develop the SaLIC model, i.e., Saliency-Enhanced Learned Image Compression. User study results indicate that, at similar or lower bitrates, SaLIC exhibits better human perceptual quality compared to HiFiC and VVC; quantitative results show that the PSNR of SaLIC significantly outperforms HiFiC (by 1-2dB), and the MS-SSIM of SaLIC even surpasses VVC, achieving a balance between perception and distortion.