A Data-Trained, Affine-Linear Intra-Picture Prediction in the Frequency Domain

Abstract

This paper presents a data-driven training of affine- linear predictors which perform intra-picture 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 matrix-vector 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 bit-rate of the DCT-transformed block residuals. The obtained predictors are incorporated into the Versatile Video Coding Test Model 4. The authors report All- Intra bit-rate savings ranging from 0.7% to 2.0% across different resolutions in terms of the Bjøntegaard-Delta bit rate (BD-rate).