360-degree videos contain an omnidirectional view with ultra-high resolution, which will lead to the bandwidth-hungry issue in virtual reality (VR) applications. However, only a part of a 360-degree video is displayed on the head-mounted displays (HMDs). Thus, we propose a viewport-adaptive 360-degree video coding approach based on a novel viewport prediction strategy. Specifically, we firstly introduce a novel viewport prediction model based on deep 3-dimensional convolutional neural networks. In this model, a video saliency encoder and a trajectory encoder are trained to extract the features of video content and the history view path. With the outputs of the two encoders, a video prior analysis network is trained to adaptively determine the best fusion weight to generate the final feature. Moreover, benefiting from the viewport prediction model, a viewport-adaptive rate-distortion optimization (RDO) method is presented to decrease the bitrate and ensure an immersive experience. In addition, we also consider the scaling factor of the area from rectangular plane to spherical surface. Therefore, the Lagrange multiplier and quantization parameter are adaptively adjusted based on the weight of each coding tree unit. The experiments have demonstrated that the proposed RDO method gains considerably better RD performance than the traditional RDO method.
