Brand F, Seiler J, Kaup A (2020)
Publication Type: Journal article
Publication year: 2020
Book Volume: 15
Pages Range: 354 - 365
Journal Issue: 2
DOI: 10.1109/JSTSP.2020.3034768
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 Bjontegaard delta rate in the luma component and 1.21% in the chroma component compared to VTM using only traditional modes.
APA:
Brand, F., Seiler, J., & Kaup, A. (2020). Intra-Frame Coding Using a Conditional Autoencoder. IEEE Journal of Selected Topics in Signal Processing, 15(2), 354 - 365. https://dx.doi.org/10.1109/JSTSP.2020.3034768
MLA:
Brand, Fabian, Jürgen Seiler, and André Kaup. "Intra-Frame Coding Using a Conditional Autoencoder." IEEE Journal of Selected Topics in Signal Processing 15.2 (2020): 354 - 365.
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