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相比于高效视频编码（high efficiency video coding，HEVC）标准，新一代编码标准多功能视频编码（versatile video coding，VVC）引入了很多新的技术，其中包括四叉树（quadtree，QT）和多类型树（multi-type tree，MTT）划分，MTT划分由HEVC中的QT划分延伸而来。新划分方法提高了压缩效率，但导致编码时间急剧增加。为了降低编码复杂度，提出了一种结合深度学习方法和MTT方向早期判决的快速帧内编码算法。首先使用轻量级的卷积神经网络（convolutional neural network，CNN）对QT和部分MTT进行预测划分，其余MTT则采用提前预测MTT划分方向的方法作进一步的优化。实验结果表明，所提方法能够大幅降低编码复杂度，相比于原始编码器的编码时间减少了74.3%，且只有3.3%的码率损失，性能优于对比的方法。

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