Abstract
High Efficiency Video Coding (HEVC) doubles the coding efficiency of the prior Advanced Video Coding (AVC) standard but tackling its huge complexity calls for efficient HEVC codec implementations. The recent advances in Graphics Processing Units (GPUs) have made programmable general-purpose GPUs (GPGPUs) a popular option for accelerating various video coding tools. Massively parallel GPU architectures are particularly well suited for hardware-oriented full search (FS) algorithm in HEVC integer motion estimation (IME). This paper analyzes the feasibility of a GPU-accelerated FS implementation in the practical Kvazaar open-source HEVC encoder. According to our evaluations, implementing FS on AMD Radeon RX 480 GPU makes Kvazaar 12.5 times as fast as the respective anchor implemented entirely on an Intel 8-core i7 processor. However, the obtained speed gain is lost when fast IME algorithms are put into use in the anchor. For example, executing the anchor with hexagon-based search (HEXBS) algorithm is almost two times as fast as our GPU-accelerated proposal and the benefit of GPU offloading is reduced to a slight coding gain of 1.2%. Our results show that accelerating IME on a GPU speeds up non-practical encoders due to their enormous inherent complexity but the price paid with practical encoders tends to be too high. Conditional processing schemes of fast IME algorithms can be efficiently executed on processors without any substantial coding loss over that of FS. Nevertheless, we still believe there might be room for exploiting GPU on IME acceleration but GPU-parallelized fast algorithms are needed to get value for additional implementation cost and power budget.
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Acknowledgements
This work was supported in part by the European Celtic-Plus project VIRTUOSE and the Academy of Finland (decision no. 301820). The authors would also like to thank all contributors of Kvazaar open-source project [6].
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Sainio, J., Mercat, A., Vanne, J. (2019). Hardware Deceleration of Kvazaar HEVC Encoder. In: Pnevmatikatos, D., Pelcat, M., Jung, M. (eds) Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2019. Lecture Notes in Computer Science(), vol 11733. Springer, Cham. https://doi.org/10.1007/978-3-030-27562-4_22
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DOI: https://doi.org/10.1007/978-3-030-27562-4_22
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