Abstract
3D-HEVC is the state-of-the-art video coding standard for 3D video, and it is an extension of high efficiency video coding (HEVC) standard. Besides the original HEVC coding tools, 3D-HEVC adopts some advanced coding tools, such as disparity vector (DV), inter-view prediction and inter-component prediction. However, these advanced tools lead to extremely high encoding complexity at the same time, thus it cannot be well applied in real-time multimedia systems. In this paper, we propose a motion and disparity vectors early determination algorithm to reduce 3D-HEVC computational complexity. First, based on the statistical analyses, the spatial and temporal motion vector (MV) candidates are adaptively reduced for the prediction unit (PU) with the Merge mode. Then, for the PU with the Inter mode, the combination of spatial and temporal candidates is used to early determine the final MV. Finally, an adaptive optimization algorithm is adopted to select the valid inter-view disparity vectors (DV) candidates. Moreover, if the difference between candidate vectors is within a conditional range, current PU will be encoded with the Merge mode to skip unnecessary coding process. Experimental results show that for the texture views encoding, the proposed algorithm achieves an average of 33.03% encoding time saving, and an average of 0.47% BD-Rate increases.
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Ahn S, Lee B, Kim M (2015) A novel fast CU encoding scheme based on spatiotemporal encoding parameters for HEVC inter coding. IEEE Trans Circ Syst Video Technol 25(3):422–435
Ayad M, Voyles R, Bae J (2016) Locally selectable protocol for sparse, highly-volatile, robotic wireless video sensor networks. Int J Sensor Netw 20(2):70–83
Balota G, Saldanha M, Sanchez G, Zatt B, Porto M, Agostini L (2014) Overview and quality analysis in 3d-HEVC emergent video coding standard. In: IEEE Latin American symposium on circuits and systems, pp 1–4
Bjøntegaard G (2008) Improvements of the BD-PSNR Model. ITU-T SG16 Q.6. Document VCEG-AI11, Berlin
Chang W, Lin Y (2015) A simple merge mode/candidate decision for HEVC. In: International symposium on communications and information technologies, pp 65–68
Chen Y (2015) Test Mode 11 of 3D-HEVC and MV-HEVC. JCT-VC of ITU-T SG16 WP3 and ISO/IEC JTC1/SC29/WG11. JCTVC-K1003, Genera
Chen Y, Zhao X, Zhang L, Kang J (2016) Multiview and 3D video compression using neighboring block based disparity vectors. IEEE Trans Multimed 18(4):576–589
Chew L, Chia W, Ang L (2012) Low-memory video compression architecture using strip-based processing for implementation in wireless multimedia sensor networks. Int J Sensor Netw 11(1):33–47
Choi H, Lim S, Kim J (2014) An efficient expression technique for promotional video production based on iot(the internet of things) in cultural art institutions. Multimed Tools Appl 6(1):1–14
Dinh M, Long V, Van X, Trieu D (2016) Improving 3d-TV view synthesis using motion compensated temporal interpolation. In: International conference on advanced technologies for communications, pp 312–317
Hannuksela M, Yan Y, Huang X, Li H (2015) Overview of the multiview high efficiency video coding (MV-HEVC) standard. In: IEEE international conference on image processing, pp 2154–2158
Jeong J, Kim S, Kim YH (2016) Early skip decision based on merge index of SKIP for HEVC encoding. Adv Sci Tech Lett 139:287–292
Jin I, Jiang X, Song T, Leu J (2015) Efficient prediction motion vector candidate selection algorithm for HEVC. In: International technical conference on circuits systems, computers and communications, pp 402–403
Jun D, Kim HY (2018) Low complexity based ultra-high quality video compression method for multimedia-centric internet of things (IoT) services. Multimed Tools Appl 77(4):4661–4675
Kaur J, Kaur K (2017) A Fuzzy Approach for an IoT-based Automated Employee Performance Appraisal. Comput Mater Contin 53(1):23–36
Lin J, Chen Y, Tsai Y, Huang Y (2011) Motion vector coding techniques for HEVC. In: IEEE international workshop on multimedia signal processing, pp 1–6
Mora E, Jung J, Pesquet-Popescu B, Cagnazzo M (2013) Modification of the disparity vector derivation process in 3D-HEVC. IEEE Int Work Multimed Signal Process 15(1):206–211
Nie Q, Weng J, Xu X, Feng B (2018) Defining Embedding Distortion for Intra Prediction Mode-based Video Steganography. Comput Mater Contin 55(1):59–59
Pan Z, Kwong S, Sun M, Lei J (2014) Early merge mode decision based on motion estimation and hierarchical depth correlation for HEVC. IEEE Trans Broadcast 60(2):405–412
Pan Z, Lei J, Zhang Y, Sun X (2016) Fast motion estimation based on content property for low-complexity H.265/HEVC encoder. IEEE Trans Broadcast 62(3):675–684
Pan Z, Lei J, Zhang Y, Wang FL (2018) Adaptive fractional-pixel motion estimation skipped algorithm for efficient HEVC motion estimation. Acm Trans Multimed Comput Commun Appl 14(1):1–19
Qi X, Zhang T, Ye F, Men A (2012) Intra prediction with enhanced inpainting method and vector predictor for HEVC. In: IEEE international conference on acoustics, speech and signal processing, pp 1217–1220
Rusanovskyy D, Mueller K, Vetro A (2013) Common test conditions of 3DV core experiments. JCT3V-E1100, Vienna
Schwarz H, Wiegand T (2012) Inter-view prediction of motion data in multiview video coding. Picture Coding Symp 8355(3):101–104
Silva T, Agostini L, Cruz L (2016) Fast intra prediction algorithm based on texture analysis for 3D-HEVC encoders. J Real-Time Image Proc 12(2):357–368
Smolic A, Mueller K, Merkle P, Fehn C (2006) 3D video and free viewpoint video-technologies, applications and MPEG standards. In: IEEE international conference on multimedia and expo, pp 2161–2164
Song Y, Jia K (2015) Early merge mode decision for texture coding in 3D-HEVC. J Vis Commun Image Represent 33(C):60–68
Vanne J, Viitanen M, Hämäläinen T (2014) Efficient mode decision schemes for HEVC inter prediction. IEEE Trans Circ Syst Video Technol 24(9):1579–1593
Xiong J, Li H, Wu Q, Meng F (2014) A fast hevc inter CU selection method based on pyramid motion divergence. IEEE Trans Multimed 16(2):559–564
Zheng A, Au O, Yuan Y, Yang H (2015) Intra prediction with adaptive CU processing order in HEVC. In: IEEE international conference on image processing, pp 3724–3728
Zhang L, Chen Y, Karczewicz M (2013) Disparity vector based advanced inter-view prediction in 3D-HEVC. In: IEEE international symposium on circuits and systems, pp 1632–1635
Zhang N, Chen Y, Lin J, Fan X (2014) Improved disparity vector derivation in 3D-HEVC. In: IEEE visual communications and image processing, pp 1–5
Zhang Q, Chang H, Huang X, Huang L, Su R, Gan Y (2016) Adaptive early termination mode decision for 3D-HEVC using inter-view and spatio-temporal correlations. AEU-Int J Electron Commun 70(5):727–737
Zhang Y, Pan Z, Zhou Y, Zhu L (2017) Allowable depth distortion based fast mode decision and reference frame selection for 3D depth coding. Multimed Tools Appl 76(1):1101–1120
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 61501246, in part by the Natural Science Foundation of Jiangsu Province of China under Grant BK20150930, in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 17KJB520021, in part by the Project through the Priority Academic Program Development of Jiangsu Higher Education Institutions, in part by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology, in part by Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Fund, China.
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Pan, Z., Yi, X. & Chen, L. Motion and disparity vectors early determination for texture video in 3D-HEVC. Multimed Tools Appl 79, 4297–4314 (2020). https://doi.org/10.1007/s11042-018-6830-7
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DOI: https://doi.org/10.1007/s11042-018-6830-7