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Complexity Correlation-Based CTU-Level Rate Control with Direction Selection for HEVC

机译:HEVC的基于复杂度关联的CTU级速率控制及方向选择

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Rate control is a crucial consideration in high-efficiency video coding (HEVC). The estimation of model parameters is very important for coding tree unit (CTU)-level rate control, as it will significantly affect bit allocation and thus coding performance. However, the model parameters in the CTU-level rate control sometimes fails because of inadequate consideration of the correlation between model parameters and complexity characteristic. In this study, we establish a novel complexity correlation-based CTU-level rate control for HEVC. First, we formulate the model parameter estimation scheme as a multivariable estimation problem; second, based on the complexity correlation of the neighbouring CTU, an optimal direction is selected in five directions for reference CTU set selection during model parameter estimation to further improve the prediction accuracy of the complexity of the current CTU. Third, to improve their precision, the relationship between the model parameters and the complexity of the reference CTU set in the optimal direction is established by using least square method (LS), and the model parameters are solved via the estimated complexity of the current CTU. Experimental results show that the proposed algorithm can significantly improve the accuracy of the CTU-level rate control and thus the coding performance; the proposed scheme consistently outperforms HM 16.0 and other state-of-the-art algorithms in a variety of testing configurations. More specifically, up to 8.4% and on average 6.4% BD-Rate reduction is achieved compared to HM 16.0 and up to 4.7% and an average of 3.4% BD-Rate reduction is achieved compared to other algorithms, with only a slight complexity overhead.
机译:速率控制是高效视频编码(HEVC)中的关键考虑因素。模型参数的估计对于编码树单元(CTU)级别的速率控制非常重要,因为它将显着影响比特分配,从而影响编码性能。但是,由于未充分考虑模型参数与复杂度特征之间的相关性,CTU级速率控制中的模型参数有时会失败。在这项研究中,我们为HEVC建立了一种基于复杂度相关性的新型CTU级速率控制。首先,我们将模型参数估计方案表述为多变量估计问题;其次,基于相邻CTU的复杂度相关性,在模型参数估计过程中,在五个方向上选择最优方向作为参考CTU集合的选择,以进一步提高当前CTU的复杂度的预测精度。第三,为提高精度,使用最小二乘法建立模型参数与最优方向上参考CTU的复杂度之间的关系,并通过估计当前CTU的复杂度来求解模型参数。 。实验结果表明,该算法可以显着提高CTU级速率控制的准确性,从而提高编码性能。在各种测试配置中,拟议的方案始终优于HM 16.0和其他最新算法。更具体地说,与其他算法相比,与HM 16.0相比,BD-Rate降低了8.4%,平均降低了6.4%,与其他算法相比,BD-Rate降低了3.4%,而复杂度只有很小的开销。

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