A DataTrained, AffineLinear IntraPicture Prediction in the Frequency Domain This paper presents a datadriven training of affine linear predictors which perform intrapicture prediction for video coding. The trained predictors use a single line of reconstructed boundary samples as input like the conventional intra prediction modes. For large blocks, the presented predictors initially transform the input samples via Discrete Cosine Transform. This allows to omit high frequency coefficients and consequently reduce the input dimension. The output is the result of a single matrixvector multiplication and offset addition. Here, the predictors only construct certain coefficients in the frequency domain. The final prediction signal is then obtained by inverse transform. The coefficients of the prediction modes need to be stored in advance, requiring 0.273 MB of memory. The training employs a recursive block partitioning, where the loss function targets to approximate the bitrate of the DCTtransformed block residuals. The obtained predictors are incorporated into the Versatile Video Coding Test Model 4. The authors report All Intra bitrate savings ranging from 0.7% to 2.0% across different resolutions in terms of the BjontegaardDelta bit rate (BDrate).

Intra Frame Prediction for Video Coding Using a Conditional Autoencoder Approach Intra prediction is a vital component of most modern image and video codecs. State of the art video codecs like High Efficiency Video Coding (HEVC) or the upcoming Versatile Video Coding (VVC) use a high number of directional modes. With the recent advances in deep learning, it is now possible to use artificial neural networks for intra frame prediction. Previously published approaches usually add additional ANN based modes or replace all modes by training several networks. In our approach, we use a single autoencoder network to first compress the original with help of already transmitted pixels to four parameters. We then use the parameters together with this support area to generate a prediction for the block. This way, we are able to replace all angular intra modes by a single ANN. In the experiments we compare our method with the intra prediction method currently used in the VVC Test Model (VTM). Using our method, we are able to gain up to 0.85 dB prediction PSNR with a comparable amount of side information or reduce the amount of side information by 2 bit per prediction unit with similar PSNR.