Low-Complexity and Hardware-Friendly H.265/HEVC Encoder for Vehicular Ad-Hoc Networks

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.

[1]  Sam Kwong,et al.  CTU-Level Complexity Control for High Efficiency Video Coding , 2018, IEEE Transactions on Multimedia.

[2]  David Flynn,et al.  HEVC Complexity and Implementation Analysis , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[3]  Pooja Sharma,et al.  Performance Analysis of Video Streaming Applications over VANETs , 2015 .

[4]  Azzedine Boukerche,et al.  Video Streaming Over Vehicular Ad Hoc Networks Using Erasure Coding , 2016, IEEE Systems Journal.

[5]  Chia-Hung Yeh,et al.  Efficient CU and PU Decision Based on Motion Information for Interprediction of HEVC , 2018, IEEE Transactions on Industrial Informatics.

[6]  Peter Clifford,et al.  Markov Random Fields in Statistics , 2012 .

[7]  Elias Yaacoub,et al.  QoE Enhancement of SVC Video Streaming Over Vehicular Networks Using Cooperative LTE/802.11p Communications , 2015, IEEE Journal of Selected Topics in Signal Processing.

[8]  Lu Wang,et al.  Quality-Oriented Perceptual HEVC Based on the Spatiotemporal Saliency Detection Model , 2019, Entropy.

[9]  Munchurl Kim,et al.  A Novel Fast CU Encoding Scheme Based on Spatiotemporal Encoding Parameters for HEVC Inter Coding , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  Haibo Wang,et al.  Fast Coding Unit Depth Decision Algorithm for Interframe Coding in HEVC , 2013, 2013 Data Compression Conference.

[11]  Xiaofeng Wang,et al.  AN EFFICIENT COMPLEXITY REDUCTION ALGORITHM FOR CU SIZE DECISION IN HEVC , 2017 .

[12]  Kalyan Goswami,et al.  A Design of Fast High-Efficiency Video Coding Scheme Based on Markov Chain Monte Carlo Model and Bayesian Classifier , 2018, IEEE Transactions on Industrial Electronics.

[13]  Juan-Carlos Cano,et al.  Evaluating H.265 real-time video flooding quality in highway V2V environments , 2014, 2014 IEEE Wireless Communications and Networking Conference (WCNC).

[14]  Sam Kwong,et al.  Two-Stage Fast Inter CU Decision for HEVC Based on Bayesian Method and Conditional Random Fields , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[15]  Lisheng Wang,et al.  Fast Coding Unit Size Decision Based on Probabilistic Graphical Model in High Efficiency Video Coding Inter Prediction , 2016, IEICE Trans. Inf. Syst..

[16]  Jörn Ostermann,et al.  A Comparison of JEM and AV1 with HEVC: Coding Tools, Coding Efficiency and Complexity , 2018, 2018 Picture Coding Symposium (PCS).

[17]  G. Bjontegaard,et al.  Calculation of Average PSNR Differences between RD-curves , 2001 .

[18]  Azzedine Boukerche,et al.  The selective use of redundancy for video streaming over Vehicular Ad Hoc Networks , 2015, Comput. Networks.

[19]  Xingming Sun,et al.  Unimodal Stopping Model-Based Early SKIP Mode Decision for High-Efficiency Video Coding , 2017, IEEE Transactions on Multimedia.

[20]  Gunter Maris,et al.  Three representations of the Ising model , 2016, Scientific Reports.

[21]  Barbara M. Masini,et al.  Cellular aided vehicular named data networking , 2014, 2014 International Conference on Connected Vehicles and Expo (ICCVE).

[22]  Zhe Xu,et al.  A fast inter CU decision algorithm for HEVC , 2018, Signal Process. Image Commun..

[23]  Ting-Lan Lin,et al.  Efficient prediction of CU depth and PU mode for fast HEVC encoding using statistical analysis , 2016, J. Vis. Commun. Image Represent..

[24]  Mónica Aguilar-Igartua,et al.  Performance Comparison of H.265/HEVC, H.264/AVC and VP9 Encoders in Video Dissemination over VANETs , 2016, GOODTECHS.

[25]  Walid M. Yousef,et al.  VEHICLE REWARDING FOR VIDEO TRANSMISSION OVER VANETS USING REAL NEIGHBORHOOD AND RELATIVE VELOCITY ( RNRV ) 1 , 2017 .

[26]  Zongju Peng,et al.  Binary and Multi-Class Learning Based Low Complexity Optimization for HEVC Encoding , 2017, IEEE Transactions on Broadcasting.

[27]  Kebin Jia,et al.  An Adaptive Quad-Tree Depth Range Prediction Mechanism for HEVC , 2018, IEEE Access.

[28]  Xingming Sun,et al.  Low Complexity HEVC Encoder for Visual Sensor Networks , 2015, Sensors.

[29]  Chia-Hung Yeh,et al.  A Fast HEVC Encoding Method Using Depth Information of Collocated CUs and RD Cost Characteristics of PU Modes , 2017, IEEE Transactions on Broadcasting.

[30]  Ammar T. Zahary,et al.  Survey of Context-Aware Video Transmission over Vehicular Ad-Hoc Networks (VANETs) , 2018, EAI Endorsed Trans. Mob. Commun. Appl..

[31]  Jenq-Shiou Leu,et al.  Spatial Correlation-Based Motion-Vector Prediction for Video-Coding Efficiency Improvement , 2019, Symmetry.

[32]  F. Bossen,et al.  Common test conditions and software reference configurations , 2010 .

[33]  King Ngi Ngan,et al.  Fast HEVC Inter CU Decision Based on Latent SAD Estimation , 2015, IEEE Transactions on Multimedia.