DFPS: a fast pattern selector for depth modeling mode 1 in three-dimensional high-efficiency video coding standard

Abstract. Depth modeling mode 1 (DMM-1) processes its prediction unit (PU) without taking into account its neighborhood. However, an extensive analysis of neighbor pattern selection showed that the processing PU could use the same pattern of its neighbors or an adaptation of these patterns. Therefore, this work proposes the DMM-1 fast pattern selector (DFPS) algorithm that includes lightweight and medium-weight DMM-1 pattern predictors. DFPS starts using the lightweight predictor, whose output is compared with a threshold and then the algorithm employs a DMM-1 refinement or the medium-weight predictor. The result of this last predictor is compared against a new threshold, where the encoder decides if the remaining patterns of the DMM-1 algorithm or only its refinement should be evaluated. Experiments evaluating DFPS encoding efficiency in the all-intra mode demonstrate that by reducing 71% and 84% of the DMM-1 patterns’ evaluation, the complexity is reduced 11.7% and 13.4%, respectively, without significantly affecting the quality of the synthesized views.

[1]  Yun Zhang,et al.  Fast mode decision based on texture–depth correlation and motion prediction for multiview depth video coding , 2013, Journal of Real-Time Image Processing.

[2]  Aljoscha Smolic,et al.  View Synthesis for Advanced 3D Video Systems , 2008, EURASIP J. Image Video Process..

[3]  Gary J. Sullivan,et al.  Overview of the Stereo and Multiview Video Coding Extensions of the H.264/MPEG-4 AVC Standard , 2011, Proceedings of the IEEE.

[4]  Chun-Su Park,et al.  Edge-Based Intramode Selection for Depth-Map Coding in 3D-HEVC , 2015, IEEE Transactions on Image Processing.

[5]  Ying Chen,et al.  Low complexity Neighboring Block based Disparity Vector Derivation in 3D-HEVC , 2014, 2014 IEEE International Symposium on Circuits and Systems (ISCAS).

[6]  Ying Chen,et al.  Disparity vector based advanced inter-view prediction in 3D-HEVC , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

[7]  Ying Chen,et al.  Overview of the Multiview and 3D Extensions of High Efficiency Video Coding , 2016, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  Shuaijun Zhang,et al.  H.264/MVC interleaving for real-time multiview video streaming , 2012, Journal of Real-Time Image Processing.

[9]  Wenyi Liu,et al.  Early termination algorithm for the depth modeling mode in three-dimensional extension of high-efficiency video coding , 2016, J. Electronic Imaging.

[10]  Yong Gan,et al.  Fast intra mode decision for depth coding in 3D-HEVC , 2017, Multidimens. Syst. Signal Process..

[11]  Aljoscha Smolic,et al.  Intermediate view interpolation based on multiview video plus depth for advanced 3D video systems , 2008, 2008 15th IEEE International Conference on Image Processing.

[12]  张云 Fast mode decision based on texture-depth correlation and motion prediction for multiview depth video coding , 2013 .

[13]  Gary J. Sullivan,et al.  Overview of the High Efficiency Video Coding (HEVC) Standard , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[14]  Heiko Schwarz,et al.  3D High-Efficiency Video Coding for Multi-View Video and Depth Data , 2013, IEEE Transactions on Image Processing.

[15]  Xiang Li,et al.  Advanced residual predction in 3D-HEV , 2013, 2013 IEEE International Conference on Image Processing.

[16]  Li Zhang,et al.  ADVANCED RESIDUAL PREDICTION IN 3 D-HEVC , 2013 .

[17]  Bruno Zatt,et al.  A complexity reduction algorithm for depth maps intra prediction on the 3D-HEVC , 2014, 2014 IEEE Visual Communications and Image Processing Conference.

[18]  Bruno Zatt,et al.  Complexity reduction for 3D-HEVC depth maps intra-frame prediction using simplified edge detector algorithm , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[19]  Jianhua Zheng,et al.  Fast Depth Modeling Mode selection for 3D HEVC depth intra coding , 2013, 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW).

[20]  Bruno Zatt,et al.  Solutions for DMM-1 complexity reduction in 3D-HEVC based on gradient calculation , 2016, 2016 IEEE 7th Latin American Symposium on Circuits & Systems (LASCAS).

[21]  Gerard de Haan,et al.  Overview of efficient high-quality state-of-the-art depth enhancement methods by thorough design space exploration , 2015, Journal of Real-Time Image Processing.

[22]  Béatrice Pesquet-Popescu,et al.  Initialization, Limitation, and Predictive Coding of the Depth and Texture Quadtree in 3D-HEVC , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[23]  D. W. Zimmerman Teacher’s Corner: A Note on Interpretation of the Paired-Samples t Test , 1997 .

[24]  Dong Tian,et al.  Boundary Artifact Reduction in View Synthesis of 3D Video: From Perspective of Texture-Depth Alignment , 2011, IEEE Transactions on Broadcasting.

[25]  N. Atzpadin,et al.  Depth map creation and image-based rendering for advanced 3DTV services providing interoperability and scalability , 2007, Signal Process. Image Commun..

[26]  Yaowu Chen,et al.  Hole-filling map-based coding unit size decision for dependent views in three-dimensional high-efficiency video coding , 2016, J. Electronic Imaging.

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

[28]  D. W. Zimmerman Teacher’s Corner: A Note on Interpretation of the Paired-Samples t Test , 1997 .

[29]  Christoph Fehn,et al.  Depth-image-based rendering (DIBR), compression, and transmission for a new approach on 3D-TV , 2004, IS&T/SPIE Electronic Imaging.

[30]  Aljoscha Smolic,et al.  Coding efficiency and complexity analysis of MVC prediction structures , 2007, 2007 15th European Signal Processing Conference.