SUR-Net: Predicting the Satisfied User Ratio Curve for Image Compression with Deep Learning
The Satisfied User Ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the probability distribution of the Just Noticeable Difference (JND) level, the smallest distortion level that can be perceived by a subject. We propose the first deep learning approach to predict such SUR curves. Instead of the direct approach of regressing the SUR curve itself for a given reference image, our model is trained on pairs of images, original and compressed. Relying on a Siamese Convolutional Neural Network (CNN), feature pooling, a fully connected regression-head, and transfer learning, we achieved a good prediction performance. Experiments on the MCL-JCI dataset showed a mean Bhattacharyya distance between the predicted and the original JND distributions of only 0.072.
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Citation : C. Fan, H. Lin, V. Hosu, Y. Zhang, Q. Jiang, R. Hamzaoui, D. Saupe, (2019) SUR-Net: Predicting the satisfied user ratio curve for image compression with deep learning. In: Proc. 11th International Conference on Quality of Multimedia Experience (QoMEX), Berlin, June 2019.
Research Institute : Institute of Engineering Sciences (IES)
Peer Reviewed : Yes