Abstract
In this paper, an adaptive model parameters prediction mechanism is proposed to take the place of parameter updating method based on the experience value in HEVC. And, normalized mutual information is exploited to guide the model parameters of α and β prediction. Experimental results show that the proposed algorithm controls the rate error within 0.1%. Compared with HM16.9, it further improves average 0.03% bit rate accuracy. Meanwhile, the proposed algorithm yields average 1.10% BDBR reduction and 0.05 dB BDPSNR enhancement without introducing additional computation. And it demonstrates less bit rate fluctuation, which achieves better adaptability for HEVC in real-time transmission.
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Acknowledgements
This paper is supported by the Project for the National Natural Science Foundation of China under Grants No. 61672064, the Beijing Natural Science Foundation under Grant No. 4172001, the China Postdoctoral Science Foundation under Grants No. 2016T90022, 2015M580029, the Science and Technology Project of Beijing Municipal Education Commission under Grants No. KZ201610005007, Beijing Municipal Education Committee Science Foundation under Grants No. KM201810005030, and Beijing Laboratory of Advanced Information Networks under Grants No. 040000546617002, Beijing Municipal Communications Commission Science and Technology Project under Grants No. 2017058.
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Feng, Z., Liu, P., Jia, K., Duan, K. (2019). An Adaptive Model Parameters Prediction Mechanism for LCU-Level Rate Control. In: Pan, JS., Lin, JW., Sui, B., Tseng, SP. (eds) Genetic and Evolutionary Computing. ICGEC 2018. Advances in Intelligent Systems and Computing, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-13-5841-8_29
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DOI: https://doi.org/10.1007/978-981-13-5841-8_29
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