Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 28, Issue -, Pages 464-468Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2021.3059202
Keywords
Convolution; Optimization; Feature extraction; Gray-scale; Histograms; Kernel; Superresolution; Convolutional neural network; reversible data hiding; global optimization capability
Categories
Funding
- NSFC [61772234]
Ask authors/readers for more resources
The letter proposes a novel CNN-based prediction approach trained on ImageNet for enhancing image prediction performance. Experimental results demonstrate that this method can effectively utilize surrounding pixels to improve prediction performance.
How to predict images is an important issue in the reversible data hiding (RDH) community. In this letter, we propose a novel CNN-based prediction approach by luminously dividing a grayscale image into two sets and applying one set to predict the other set for data embedding. The proposed CNN predictor is a lightweight and computation-efficient network with the capabilities of multi receptive fields and global optimization. This CNN predictor can be trained quickly and well by using 1000 images randomly selected from ImageNet. Furthermore, we propose a two stages of embedding scheme for this predictor. Experimental results show that the CNN predictor can make full use of more surrounding pixels to promote the prediction performance. Furthermore, in the experimental way we have shown that the CNN predictor with expansion embedding and histogram shifting techniques can provide better embedding performance in comparison with those classical linear predictors.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available