Name
Abstract | ||
|---|---|---|
In the hybrid video coding framework, transform is adopted to exploit the dependency within the input signal. In this paper, we propose a deep learning-based nonlinear transform for intra coding. Specifically, we incorporate the directional information into the residual domain. Then, a convolutional neural network model is designed to achieve better decorrelation and energy compaction than the conventional discrete cosine transform. This work has two main contributions. First, we propose to use the intra prediction signal to reduce the directionality in the residual. Second, we present a novel loss function to characterize the efficiency of the transform during the training. To evaluate the compression performance of the proposed transform, we implement it into the High Efficiency Video Coding reference software. Experimental results demonstrate that the proposed method achieves up to 1.79% BD-rate reduction for natural videos. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/VCIP49819.2020.9301790 | 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) |
Keywords | DocType | ISSN |
Deep learning,High Efficiency Video Coding,intra coding,neural network,transform | Conference | 1018-8770 |
ISBN | Citations | PageRank |
978-1-7281-8069-4 | 0 | 0.34 |
References | Authors | |
5 | 3 | |