Publication Details
Overview
 
 
, Hongyue Huang, Adrian Munteanu
 

IEEE Transactions on Circuits and Systems for Video Technology

Contribution To Journal

Abstract 

The paper proposes a novel block-wise prediction paradigm based on Convolutional Neural Networks (CNN) for lossless video coding. A deep neural network model which follows a multi-resolution design is employed for block-wise prediction. A set of novel contributions is proposed to improve the neural network training. A first contribution proposes a novel loss function formulation for an efficient network training based on a new approach for patch selection. Another contribution consists in replacing all HEVC-based angular intra-prediction modes with a CNN-based intra-prediction method, where each angular prediction mode is complemented by a CNN-based prediction mode using a specifically trained model. Another contribution consists in an efficient adaptation of the CNN-based intra-prediction residual for lossless video coding. Experimental results on standard test sequences show that the proposed coding system outperforms the HEVC standard with an average bitrate improvement of around 5%. To our knowledge, the paper is the first to replace all the traditional HEVC-based angular intra-prediction modes with an intra-prediction method based on modern Machine Learning techniques.

Reference 
 
 
DOI scopus