JND-LIC: Learned Image Compression via Just Noticeable Difference for Human Visual Perception

Abstract

Existing human visual perception-oriented image compression methods well maintain the perceptual quality of compressed images, but they may introduce fake details into the compressed images, and cannot dynamically improve the perceptual rate-distortion performance at the pixel level. To address these issues, a just noticeable difference (JND)-based learned image compression (JND-LIC) method is proposed for human visual perception in this paper, in which a weight-shared model is used to extract image features and JND features, and the learned JND features are utilized as perceptual prior knowledge to assist the image coding process. In order to generate a highly compact image feature representation, a JND-based feature transform module is proposed to model the pixel-to-pixel masking correlation between the image features and the JND features. Furthermore, inspired by eye movement research that the human visual system perceives image degradation unevenly, a JND-guided quantization mechanism is proposed for the entropy coding, which adjusts the quantization step of each pixel to further eliminate perceptual redundancies. Extensive experimental results show that our proposed JND-LIC significantly improves the perceptual quality of compressed images with fewer coding bits compared to state-of-the-art learned image compression methods. Additionally, the proposed method can be flexibly integrated with various advanced learned image compression methods, and has robust generalization capabilities to improve the efficiency of perceptual coding.