| Session: | Image Coding |
| Location: | Lecture Room |
| Session Time: | Monday, June 25, 13:40 - 15:20 |
| Presentation Time: | Monday, June 25, 14:40 - 15:00 |
| Presentation: |
Lecture
|
| Paper Title: |
CNN-based Prediction for Lossless Coding of Photographic Images |
| Authors: |
Ionut Schiopu; Vrije Universiteit Brussel (VUB), Belgium | | |
| | Yu Liu; Vrije Universiteit Brussel (VUB), Belgium | | |
| | Adrian Munteanu; Vrije Universiteit Brussel (VUB), Belgium | | |
| Abstract: |
The paper proposes a novel prediction paradigm in image coding based on Convolutional Neural Networks (CNN). A deep neural network is designed to provide accurate pixel-wise prediction based on a causal neighbourhood. The proposed CNN prediction method is trained on the high-activity areas in the image and it is incorporated in a lossless compression system for high-resolution photographic images. The system uses the proposed CNN-based prediction paradigm as well as LOCO-I, whereby the predictor selection is performed using a local entropy-based descriptor. The prediction errors are encoded using a CALIC-based reference codec. The experimental results show a good performance for the proposed prediction scheme compared to state-of-the-art predictors. To our knowledge, the paper is the first to introduce CNN-based prediction in image coding, and demonstrates the potential offered by machine learning methods in coding applications. |