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An Affine-Linear Intra Prediction with Complexity Constraints

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


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society:
IEEE International Conference on Image Processing, ICIP 2019. Proceedings : 22-25 September 2019, Taipei, Taiwan
Taipei, Taiwan: IEEE, 2019
ISBN: 978-1-5386-6249-6
ISBN: 978-1-5386-6250-2
International Conference on Image Processing (ICIP) <2019, Taipei>
Fraunhofer HHI ()

This paper presents a novel method for a data-driven training of affine-linear predictors which perform intra prediction in state-of-the-art video coding. The main aspect of our training design is the use of subband decomposition of both the input and the output of the prediction. Due to this architecture, the same set of predictors can be shared across different block shapes leading to a very limited memory requirement. Also, the computational complexity of the resulting predictors can be limited such that it does not exceed the complexity of the conventional angular intra prediction. In the training itself, a loss function modelling the bit-rate of the DCT-transformed residuals is used. The obtained predictors are incorporated into the Versatile Video Coding Test Model 3 in addition to the conventional intra prediction modes. All-Intra bit-rate savings ranging from 0.8% to 1.4% across different resolutions have been measured in terms of the Bjøntegaard-Delta bit rate (BD-rate).