CNN oriented fast QTBT partition algorithm for JVET intra coding
Zhipeng Jin, Ping An, Liquan Shen, Chao Yang · 2017
In this paper, a novel fast coding unit depth decision algorithm based on convolution neural network is presented for JVET future video coding. JVET employs quad-tree plus binary-tree (QTBT) block partitioning structure, which can support much more flexibility for coding units partition shapes, and improve the coding performance significantly than the HEVC standard. However, the flexible partitioning structure also introduces a tremendous computation complexity. To address this issue, we model the QTBT partition depth range as a multi-class classification problem, and try to predict the depth range of 32×32 block directly, rather than to judge split or not at each depth level. To the best of our knowledge, it is the first framework to formulate the QTBT partition range as a multi classification task, and optimized by an end-to-end learning model. For training optimization, we design an objective function consists of class penalty term and L2 HingeLoss function, which leverage the characteristics of category settings, can further boost the classification accuracy. Experimental results demonstrate the effectiveness of our proposed method, which can achieve 42.80% complexity reduction with only 0.65% Bjontegaard Delta bitrate (BD-rate) increase.