Overfitted Neural Networks for Block-based Intra-prediction

Block-based intra-prediction is a key process of several state-of-the-art image and video codecs. Recently, supervised machine learning (ML) has improved this type of prediction. However, the performance of these ML-based methods depends on the amount, quality, and relevance of the training data. Furthermore, they require that the model’s parameters are stored so the imaging data can be reconstructed from the compressed bit-stream. Such a requirement, unfortunately, may hinder compression performance. This work departs from the current trend of training deep neural networks (NNs) and instead focuses on overfitted shallow NNs that can be optimized online. Specifically, it introduces the Reversible Regression Network (R2-Net) for block-based intra-prediction of imaging data. The R2-Net is an overfitted fully connected NN that can accurately predict imaging data while allowing for the recovery of such data by reversing the overfitting process. The R2-Net is optimized in an online manner based on parameters initialized to known values. Such an optimization strategy removes the need to store the model’s parameters. Performance evaluations on several benchmark video sequences show that the R2-Net outperforms the traditional block-based intra-prediction strategy with gains of up to 37.61 dB PSNR.