  Just noticeable distortion (JND), representing the threshold of distortion in
an image that is minimally perceptible to the human visual system (HVS), is
crucial for image compression algorithms to achieve a trade-off between
transmission bit rate and image quality. However, traditional JND prediction
methods only rely on pixel-level or sub-band level features, lacking the
ability to capture the impact of image content on JND. To bridge this gap, we
propose a Semantic-Guided JND (SG-JND) network to leverage semantic information
for JND prediction. In particular, SG-JND consists of three essential modules:
the image preprocessing module extracts semantic-level patches from images, the
feature extraction module extracts multi-layer features by utilizing the
cross-scale attention layers, and the JND prediction module regresses the
extracted features into the final JND value. Experimental results show that
SG-JND achieves the state-of-the-art performance on two publicly available JND
datasets, which demonstrates the effectiveness of SG-JND and highlight the
significance of incorporating semantic information in JND assessment.
