The next-generation video compression standard H.266/Future Video Coding (FVC) provides high compression efficiency in terms of the cost of computing the optimal intra mode from 67 modes. We propose an intra mode prediction method based on a convolutional neural network (CNN). An input image set of 20 × 20 blocks is used to train the CNN; the CNN is used to predict the best classes of intra mode direction. The CNN architecture comprises two convolutional layers and a fully connected layer. Compared with the default fast search method in FVC, the proposed method can achieve a 0.033% decrease in Bjøntegaard delta bit rate (BDBR) with only a slight increase in time.

就从67种模式中计算最佳帧内模式的成本而言，下一代视频压缩标准H.266 /未来视频编码（FVC）提供了高压缩效率。我们提出了一种基于卷积神经网络（CNN）的帧内模式预测方法。20×20块的输入图像集用于训练CNN；CNN用于预测帧内方向的最佳类别。CNN体系结构包括两个卷积层和一个完全连接的层。与FVC中的默认快速搜索方法相比，该方法可以将Bjøntegaard增量比特率（BDBR）降低0.033％，而时间仅稍有增加。