High Efficiency Intra Video Coding Based on Data-Driven Transform

In this work, we propose a high efficiency intra video coding based on data-driven transform, which is able to learn the source distributions of intra prediction residuals and improve the subsequent transform coding efficiency. Firstly, we model learning based transform design as an optimization problem of maximizing energy compaction or decorrelation. A data-driven Subspace Approximation with Adjusted Bias (Saab) transform is analyzed and compared with the mainstream Discrete Cosine Transform (DCT) on their energy compaction and decorrelation capabilities. Secondly, we propose a Saab transform based intra video coding framework with offline Saab transform learning. Meanwhile, intra mode dependent Saab transform is developed. Then, Rate-Distortion (RD) gain of Saab transform based intra video coding is theoretically and experimentally analyzed in detail. Finally, three strategies that apply the Saab transform to intra video coding are developed to improve the coding efficiency. Experimental results demonstrate that the proposed
$8\times 8$
Saab transform based intra coding can achieve Bjønteggard Delta Bit Rate (BDBR) from −1.19% to −10.00% and −3.07% on average as compared with the mainstream
$8\times 8$
DCT based intra coding. In case of variable size transform unit setting, the proposed algorithm achieves BDBR from −0.17% to −6.09% and −1.80% on average, which outperform the DCT based and the neural network based schemes.