Analysis and Exploitation of CTU-Level Parallelism in the HEVC Mode Decision Process Using Actor-Based Modeling

Abstract

The new High-Efficiency Video Coding (HEVC) standard achieves much better compression ratios than previous ones by offering multiple coding modes, albeit with a significant increase over the required computational power especially at the encoder side. As the first major contribution, we propose a fine-grained parallelization of the encoding mode decision process using a SystemC actor-based model, exploiting multi-core platforms. Second, based on this model, we analyze achievable speedups compared to the single core sequential implementation of the HM-16.0 reference software. Using four different video sequences, we find that our approach achieves an equivalent rate-distortion performance for different quantization parameter values with a simulated encoding time improvement factor of up to $9\times$ for a maximally parallelized mode decision process. Third, an HEVC encoder has a huge number of different standard-complying encoding modes to choose from for each encoded frame, making the exploration space almost impossible to be fully covered by a brute-force search. Here, we systematically investigate the trade-off in encoding time versus required number of processor cores by proposing a multi-objective Design Space Exploration (DSE) of the mapping of the parallelized mode decision tasks to processing resources, taking as optimization objectives the resulting bitrate, image quality, number of processor cores used, execution time, and total energy consumption.