Abstract
Recently deep learning techniques have shown remarkable progress in image/video super-resolution. These techniques can be employed in a video coding system for improving the quality of the decoded frames. However, different from the conventional super-resolution works, the compression artifacts in the decoded frames should be concerned with. The straightforward solution is to integrate the artifacts removing techniques before super-resolution. Nevertheless, some helpful features may be removed together with the artifacts, and remaining artifacts can be exaggerated. To address these problems, we design an end-to-end restoration-reconstruction deep neural network (RR-DnCNN) using the degradation-aware techniques. RR-DnCNN is applied to the down-sampling based video coding system. In the encoder side, the original video is down-sampled and compressed. In the decoder side, the decompressed down-sampled video is fed to the RR-DnCNN to get the original video by removing the compression artifacts and super-resolution. Moreover, in order to enhance the network learning capabilities, uncompressed low-resolution images/videos are utilized as a ground-truth. The experimental results show that our work can obtain over 8% BD-rate reduction compared to the standard H.265/HEVC. Furthermore, our method also outperforms in reducing compression artifacts in subjective comparison. Our work is available at https://github.com/minhmanho/rrdncnn.
Supported by JST, PRESTO Grant Number JPMJPR1757 Japan.
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Ho, MM., He, G., Wang, Z., Zhou, J. (2020). Down-Sampling Based Video Coding with Degradation-Aware Restoration-Reconstruction Deep Neural Network. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11961. Springer, Cham. https://doi.org/10.1007/978-3-030-37731-1_9
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DOI: https://doi.org/10.1007/978-3-030-37731-1_9
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