Dr. Igor Piljic Profile

Dr. Igor Piljic

at Univ. of Zagreb

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Proceedings Article | 10 November 2020 Paper

Highly parallel GPU accelerator for HEVC transform and quantization

Mate Cobrnic, Alen Duspara, Leon Dragic, Igor Piljic, Mario Kovac

Proceedings Volume 11584, 115840V (2020) https://doi.org/10.1117/12.2581228

KEYWORDS: Video acceleration, Video, Video compression, Quantization, Video coding, Computing systems, Data processing, Video processing

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When analysing Internet traffic today it can be found that digital video content prevails. Its domination will continue to grow in the upcoming years and reach 82% of all traffic by 2021. If converted to Internet video minutes per second, this equals about one million video minutes per second. Providing and supporting improved compression capability is therefore expected from video processing devices. This will relieve the pressure on storage systems and communication networks while creating preconditions for further development of video services. Transform and quantization is one of the most compute-intensive parts of modern hybrid video coding systems where coding algorithm itself is commonly standardized. High Efficiency Video Coding (HEVC) is state-of-the-art video coding standard which achieves high compression efficiency at the cost of high computational complexity. In this paper we present highly parallel GPU accelerator for HEVC transform and quantization which targets most common heterogeneous computing CPU+GPU system. The accelerator is implemented using CUDA programming model. All the relevant state-of-the-art techniques related to kernel vectorization, shared memory optimization and overlapping data transfers with computation were investigated, customized and carefully combined to obtain a performance efficient solution across all applicable transform sizes. The proposed solution is compared against reference implementation which uses NVIDIA cuBLAS library to perform the same work. Obtained speedup factors for DCI 4K frame are 2.46 times for largest transform size and 130.17 times for smallest transform size what revealed substantial performance gap of this library when targeting GPU of the Kepler architecture. Achieved processing time of frame transform and quantization are up to 4.82 ms.

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