Sigger, Neetu and Al-Jawad, Naseer and Nguyen, Tuan (2022) Spatial-Temporal Autoencoder with Attention Network for Video Compression. In: International Conference on Image Analysis and Processing, May 23-27 2022, Italy.
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Abstract
Deep learning-based approaches are now state of the art in
numerous tasks, including video compression, and are having a revolutionary
influence in video processing. Recently, learned video compression methods
exhibit a fast development trend with promising results. In this paper, taking
advantage of the powerful non-linear representation ability of neural networks,
we replace each standard component of video compression with a neural network.
We propose a spatial-temporal video compression network (STVC) using the
spatial-temporal priors with an attention module (STPA). On the one hand, joint
spatial-temporal priors are used for generating latent representations and
reconstructing compressed outputs because efficient temporal and spatial
information representation plays a crucial role in video coding. On the other hand,
we also added an efficient and effective Attention module such that the model
pays more effort on restoring the artifact-rich areas. Moreover, we formalize the
rate-distortion optimization into a single loss function, in which the network
learns to leverage the Spatial-temporal redundancy presented in the frames and
decreases the bit rate while maintaining visual quality in the decoded frames. The
experiment results show that our approach delivers the state-of-the-art learning
video compression performance in terms of MS-SSIM and PSNR.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information: | ICIAP 2022: 21st International Conference, Lecce, Italy, May 23–27, 2022, Proceedings, Part III May 2022 |
| Uncontrolled Keywords: | Video Compression ; Deep Learning ; Auto-Encoder ; Rate-Distortion Optimization ; Attention Mechanism |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | School of Computing |
| Depositing User: | Neetu Sigger |
| Date Deposited: | 20 Feb 2023 15:52 |
| Last Modified: | 20 Feb 2023 15:52 |
| URI: | https://bear.buckingham.ac.uk/id/eprint/584 |
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