Unified Matrix Coding for NN Originated MIP in H.266/VVC
Junyan Huo, Yu Feng Sun, Haixin Wang, Shuai Wan, Fuzheng Yang, Ming Li · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · 2022
Matrix-based Intra Prediction (MIP) is an effective coding algorithm in H.266/Versatile Video Coding (VVC) which is originated by Neural Networks (NN). With the requirement of low complexity, MIP is conducted by a matrix-vector multiplication. To handle with the diversity of video content, 30 matrices are trained and stored to derive predicted samples. Since matrices from training are usually floating-point values, which should be avoided in H.266/VVC, two parameters, shift and offset, are introduced for each matrix to convert floating-point values to integers. This paper designs an efficient algorithm to determine the input vector of MIP, with which the range of the matrices can be minimized, and all matrices can be converted to integers with a unified shift and a unified offset. The proposed algorithm removes the matrix-dependent parameters for integer conversion and saves the memory for storing MIP parameters. Experimental results demonstrate that the proposed algorithm has a similar coding performance with VVC reference software. Due to the unified operation, memory reduction, and no coding loss, the proposed algorithm has been adopted into H.266/VVC.