Efficient GPU-Based Inter Prediction for Video Decoder

Interpolation is a very important module in inter prediction for any video decoder, e.g. AVS2 [1] and HEVC [2], which occupies most of the time in the whole decoding process . Thus, the real-time decoder is largely limited by the speed of inter prediction. To solve this problem, we propose an effici...

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Bibliographic Details
Published in2019 IEEE International Conference on Image Processing (ICIP) pp. 1109 - 1113
Main Authors Jiang, Bo, Luo, Falei, Wang, Shanshe, Guo, Xiaoqiang, Ma, Siwei
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2019
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Summary:Interpolation is a very important module in inter prediction for any video decoder, e.g. AVS2 [1] and HEVC [2], which occupies most of the time in the whole decoding process . Thus, the real-time decoder is largely limited by the speed of inter prediction. To solve this problem, we propose an efficient GPU-based interpolation framework for inter prediction. Through optimizing shared memory allocation and thread scheduling on the GPU side, GPU are utilized efficiently and inter prediction is accelerated effectively. The experimental results on AVS2 show that for all Ultra HD 4K, WQXGA and full HD video sequences tested, the inter prediction acceleration ratio is over 6 times, and the average processing time is up to 1.25ms, 0.75ms and 0.45ms, respectively, with the NVIDIA GeForce GTX 1080TI GPU.
ISSN:2381-8549
DOI:10.1109/ICIP.2019.8803716