Complexity Reduction of ANN Model for CU Size Selection in HEVC

In HEVC compression is performed in Coding Units (CUs) being pixel blocks of a size adaptively chosen according to the local content within a video frame. Nearoptimum selection of the frame partition into CUs is crucial for the coding efficiency. A huge number of partitioning schemes is available and the optimum partitioning scheme is obtained in an iterative computation-heavy procedure in a classic HEVC encoder. In order to reduce the encoding time and the encoding energy, a few approaches have been proposed with the use of neural networks (NNs). These approaches demonstrate a significant reduction of the encoding time and a negligible increase of the bitrate as compared to the traditional iterative approach. Nevertheless, they use very large neural networks whereas it is demonstrated in this paper that much smaller neural networks provide similar results encoding tome reduction with the similar bitrate reduction.