Hybrid Intra-Prediction in Lossless Video Coding using Overfitted Neural Networks
Victor F. Sanchez, Miguel Hernández-Cabronero, Joan S. Serra-Sagrista · 2021
Methods based on machine learning (ML) have been recently proposed to improve upon traditional block-based intra-prediction algorithms in modern video codecs [1,2]. Their performance, however, depends on the amount, quality and relevance of the training data. Furthermore, they require signaling the learned parameters to the decoder, thus increasing compressed data volumes. In this work, six new prediction modes based on fully-connected neural networks (FC-NNs) are proposed that avoid the two aforementioned shortcomings. To do so, 1-layer FC-NNs are used, whose parameters are rened by overfitting on the data samples being predicted. This allows to replicate the parameter optimization process at the decoder under a lossless compression regime without requiring any additional side information. Each proposed ML-based mode is based on a 1-layer FC-NN that predicts a block of size k k in a column-wise or row-wise manner using as input a subset of the reference samples used by traditional intra-prediction. Each subset, which varies for each column or row, is computed by averaging a number of reference samples to reduce noise [3]. Experimental results based on several video frames indicate that the proposed ML-based modes are selected as the best modes between 11% and 93% of the times. (see Table 1). When used in a hybrid intra-prediction framework that also includes HEVC's modes, the proposed ML-based modes increase prediction accuracy by between 0.56 dB and 7.01 dB PSNR, with respect to using only HEVC's modes.