Fast HEVC CU/PU mode decision based on ANN and texture analysis

Abstract

HEVC (high efficiency video coding), as the latest video coding standard, is more efficient than H.264/AVC, nevertheless it also brings in a very high computational complexity. To reduce the time of CU (coding unit) splitting or PU (prediction unit) mode deciding, a fast algorithm based on ANN (artificial neural network) and texture analysis is proposed in this paper. First, we acquire and then label the CUs of the training set with “split” and “unsplit” according to the quad-tree CU depth. Second, the texture features of “split” and “unsplit” CUs are quantified and compiled, based on which we can set the texture thresholds to judge whether or not to split the current CU or which prediction mode should be taken preliminarily. Finally, in terms of the CUs we can't judge from texture, we use ANN or the original algorithm of HM (HEVC test model) software to decide. Compared to HM15.0, the proposed algorithm can save 51.85% encoding time on average with negligible coding efficiency loss.