Intra-Frame Coding Using a Conditional Autoencoder
Exploiting spatial redundancy in images is responsible for a large gain in the performance of image and video compression. The main tool to achieve this is called intra-frame prediction. In most state-of-the-art video coders, intra prediction is applied in a block-wise fashion. Up to now angular pre...
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| Published in: | IEEE journal of selected topics in signal processing Vol. 15; no. 2; pp. 354 - 365 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 1932-4553, 1941-0484 |
| Online Access: | Get full text |
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| Summary: | Exploiting spatial redundancy in images is responsible for a large gain in the performance of image and video compression. The main tool to achieve this is called intra-frame prediction. In most state-of-the-art video coders, intra prediction is applied in a block-wise fashion. Up to now angular prediction was dominant, providing a low-complexity method covering a large variety of content. With deep learning, however, it is possible to create prediction methods covering a wider range of content, being able to predict structures which traditional modes can not predict accurately. Using the conditional autoencoder structure, we are able to train a single artificial neural network which is able to perform multi-mode prediction. In this paper, we derive the approach from the general formulation of the intra-prediction problem and introduce two extensions for spatial mode prediction and for chroma prediction support. Moreover, we propose a novel latent-space-based cross component prediction. We show the power of our prediction scheme with visual examples and report average gains of 1.13% in Bjøntegaard delta rate in the luma component and 1.21% in the chroma component compared to VTM using only traditional modes. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 1932-4553 1941-0484 |
| DOI: | 10.1109/JSTSP.2020.3034768 |