HEVC intra prediction mode classification by deep learning
Hanxiang Wang, Yanfen Li, L. Minh Dang, Hae-Kwang Kim · 2020
In High Efficiency Video Coding (HEVC) standard, the best intra prediction mode is decided by choosing the smallest ratedistortion cost of actual encoding among the total of 35 modes with the MPM (Most Probable Mode) scheme for compression purpose of mode encoding with reference to the adjacent reference blocks of the current prediction unit. This causes heavy computational complexity. In this paper, a deep neural network is conceived and experimented as a probable module for the intra prediction mode decision process inside of the HEVC encoding scheme. The neural network is trained and tested with a ground-truth dataset constructed from actual HEVC Intra encoding of original images. For the performance of the test, accuracy is used as the percentage of the correct mode output by the designed neural network to the ground-truth mode. The experimental results show that the neural network does not give good accuracy for the correct mode. However, accuracy increased when similar angle mode is considered as the correct mode. Also, the special modes of DC and Planar for MPM are analyzed in this paper.