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dividing video frames into Coding Tree Units (CTUs) and Coding Units (CUs) is a critical task of video compression in H.265/HEVC video coding standard. In this paper, we utilize deep learning techniques, especially the deep Convolutional Neural Network (CNN) to speed up the block partitioning process. Deep CNNs have achieved break-through improvements on image recognition tasks such as image classifications, object identifications, and image annotations. However, very few work has been done in applying deep CNN to video encoding. Block partitioning in video coding is highly dependent on the content of the video frames, and thus it is natural to take advantage of the significant capabilities of deep CNN on image content detection and recognition to perform block partitioning and avoid the time-consuming iterative RateDistortion-Optimization (RDO) process. Experimental results have shown that the proposed methodology has largely speed up the coding process and has also achieved coding efficiency comparable to the reference software of H.265/HEVC.

Using Deep Learning Neural Network for Block Partitioning in H.265/HEVC