Convolutional Neural Networks for Video Intra Prediction Using Cross-component Adaptation
| Autor: | Jonathan Wiesner, Christian Rohlfing, Jens Schneider, Maria Meyer |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer science business.industry Luma 020206 networking & telecommunications Pattern recognition 02 engineering and technology Convolutional neural network Image (mathematics) Convolution Component adaptation Encoding (memory) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | ICASSP |
| Popis: | Recently, neural networks were shown to improve video and image intra prediction significantly. In this paper, the properties of different architectures for neural network-based intra prediction are evaluated. This includes an analysis of the properties of convolutional neural networks used for this purpose, showing that they outperform fully connected ones especially for complex and low resolution content. Also, the usage of separate networks for luma and chroma prediction, which are able to perform a learned cross-component prediction, is proposed as this is clearly beneficial for the prediction quality. Furthermore, a new way of signaling a neural network-based intra prediction mode in HEVC is investigated. In total this improves the compression performance in terms of average BD-rate changes by −2.0% for the luma and by −1.5% for the chroma channels. |
| Databáze: | OpenAIRE |
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