Author(s) |
Fan, C., Lin, H., Hosu, V., Zhang, Y., Jiang, Q., Hamzaoui, R., Saupe, D. |

Title |
SUR-Net: Predicting the satisfied user ratio curve for image compression with deep learning |

Abstract |
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. |

Download |
FaLiHo19.pdf |