Jiang Xiantao, Feng Jie, Song Tian, Katayama Takafumi
Department of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
State Key Laboratory of Integrated Services Networks, Department of Telecommunications Engineering, Xidian University, Xi'an 710071, China.
Sensors (Basel). 2019 Apr 24;19(8):1927. doi: 10.3390/s19081927.
Real-time video streaming over vehicular ad-hoc networks (VANETs) has been considered as a critical challenge for road safety applications. The purpose of this paper is to reduce the computation complexity of high efficiency video coding (HEVC) encoder for VANETs. Based on a novel spatiotemporal neighborhood set, firstly the coding tree unit depth decision algorithm is presented by controlling the depth search range. Secondly, a Bayesian classifier is used for the prediction unit decision for inter-prediction, and prior probability value is calculated by Gibbs Random Field model. Simulation results show that the overall algorithm can significantly reduce encoding time with a reasonably low loss in encoding efficiency. Compared to HEVC reference software HM16.0, the encoding time is reduced by up to 63.96%, while the Bjontegaard delta bit-rate is increased by only 0.76-0.80% on average. Moreover, the proposed HEVC encoder is low-complexity and hardware-friendly for video codecs that reside on mobile vehicles for VANETs.
通过车载自组织网络（VANET）进行实时视频流传输一直被视为道路安全应用中的一项关键挑战。本文的目的是降低用于VANET的高效视频编码（HEVC）编码器的计算复杂度。基于一种新颖的时空邻域集，首先通过控制深度搜索范围提出了编码树单元深度决策算法。其次，将贝叶斯分类器用于帧间预测的预测单元决策，并通过吉布斯随机场模型计算先验概率值。仿真结果表明，整体算法能够在编码效率损失合理较低的情况下显著减少编码时间。与HEVC参考软件HM16.0相比，编码时间最多可减少63.96%，而平均而言，Bjontegaard比特率仅增加0.76 - 0.80%。此外，所提出的HEVC编码器对于驻留在VANET移动车辆上的视频编解码器具有低复杂度和硬件友好性。
