SG-JND: Semantic-Guided Just Noticeable Distortion Predictor For Image Compression

TL;DR

This paper introduces SG-JND, a novel semantic-guided neural network that predicts image distortion thresholds by incorporating semantic information, significantly improving JND prediction accuracy for image compression.

Contribution

The paper proposes a new SG-JND network that leverages semantic information and multi-layer features for more accurate JND prediction, surpassing traditional pixel-based methods.

Findings

Achieves state-of-the-art performance on JND datasets.

Demonstrates the importance of semantic information in JND prediction.

Highlights the effectiveness of cross-scale attention layers.

Abstract

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.