On predicting bottlenecks in wavefront parallel video coding using deep neural networks

Video coding incurs high computational complexity particularly at the encoder side. For this reason, parallelism is used at the various encoding steps. One of the popular coarse grained parallelization tools offered by many standards is wavefront parallelism. Under the scheme, each row of blocks is assigned to a separate thread for processing. A thread might commence encoding a particular block once certain precedence constraints are met, namely, it is required that the left block of the same row and the top and top-right block of the previous row have finished compression. Clearly, the imposed constraints result in processing delays. Therefore, in order to optimize performance, it is of paramount importance to properly identify potential bottlenecks before the compression of a frame starts, in order to alleviate them through better resource allocation. In this paper we present a simulation model that predicts bottlenecks based on the estimated block compression times produced from a regression neural network. Experiments with datasets obtained using the reference encoder of HEVC (High Efficiency Video Coding) illustrate the merits of the proposed model. © Springer Nature Switzerland AG 2019