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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Vydáno v:IEEE journal of selected topics in signal processing Ročník 15; číslo 2; s. 354 - 365
Hlavní autoři: Brand, Fabian, Seiler, Jurgen, Kaup, Andre
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Artificial neural networks
Coders
conditional autoencoder
Decoding
deep learning
Encoding
Image coding
Image compression
intra prediction
Neural networks
Prediction methods
Redundancy
Training
Video coder
Video compression
ISSN:1932-4553, 1941-0484
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Shrnutí: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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ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2020.3034768