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Intra Picture Prediction for Video Coding with Neural Networks

: Helle, P.; Pfaff, J.; Schäfer, M.; Rischke, R.; Schwarz, H.; Marpe, D.; Wiegand, T.


Bilgin, A. ; University of Arizona; Brandeis University; Microsoft Research; IEEE Signal Processing Society:
Data Compression Conference, DCC 2019. Proceedings : Snowbird, Utah, USA, 26-29 March 2019
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-72810-657-1
ISBN: 978-1-72810-658-8
Data Compression Conference (DCC) <2019, Snowbird/Utah>
Fraunhofer HHI ()

We train a neural network to perform intra picture prediction for block based video coding. Our network has multiple prediction modes which co-adapt during training to minimize a loss function. By applying the l1-norm and a sigmoid-function to the prediction residual in the DCT domain, our loss function reflects properties of the residual quantization and coding stages present in the typical hybrid video coding architecture. We simplify the resulting predictors by pruning them in the frequency domain, thus greatly reducing the number of multiplications otherwise needed for the dense matrix-vector multiplications. Also, by quantizing the network weights and using fixed point arithmetic, we allow for a hardware friendly implementation. We demonstrate significant coding gains over state of the art intra prediction.