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A fast and HEVC-compatible perceptual video coding scheme using a transform-domain Multi-Channel JND model

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Abstract

Coding optimization methods incorporating the just noticeable distortion (JND) model, called perceptual video coding (PVC), have drawn much attention in recent years for better video coding performance. To further remove perceptual redundancy in every channel and improve the coding performance, this paper proposes a fast PVC scheme in the latest High Efficiency Video Coding (HEVC) framework based on our proposed variable block-size transform-domain multi-channel JND model. Firstly, through extensive experiments, we find out for the first time that the contrast masking (CM) effects for chroma channels show a lowpass property in frequency, which differs from the luma channel that has a bypass property. Based on this observation, CM effects in chroma blue (Cb) and chroma red (Cr) channels are modeled as a continuous function for variable-sized blocks, respectively. Secondly, since different characteristics of the human visual system (HVS) exhibit quite distinct effects in luma and chroma channels and effects in chroma channels were not well explored, we develop a new JND model through comprehensive consideration for both luma and chroma channels of five typical HVS effects, with especial focus on parameterized modeling of chroma channels in each effect. Finally, to incorporate the proposed JND model into the latest HEVC coding framework, a multi-channel coefficients suppression method based on JND thresholds and quantization parameter (QP) ranges is proposed in the transform and quantization process, which can decrease the computational complexity. Extensive experimental results show that the proposed PVC scheme implemented in HEVC reference software (HM15.0) can yields significant bit saving of up to 25.91% and on average 9.42% with similar subjective quality, compared to HM15.0, and consistently outperforms two PVC schemes with much reduced bitrate and complexity overhead.

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References

  1. Bae S, Kim M (2014) A novel generalized DCT-based JND profile based on an elaborate CM-JND model for variable block-sized transforms in monochrome images. IEEE Trans Image Process 23(8):3227–3240

    Article  MathSciNet  MATH  Google Scholar 

  2. Bae S, Kim J, Kim M (2016) HEVC-based Perceptually adaptive video coding using a DCT-based local distortion detection probability model. IEEE Trans Image Process 25(7):3343–3357

    Article  MathSciNet  Google Scholar 

  3. Bossen F, Common test conditions and software reference configurations, JCTVC-I1100, JCTVC, May 2012

  4. Chang HW, Zhang QW, Wu QG, Gan Y (2015) Perceptual image quality assessment by independent feature detector. Neurocomputing 151:1142–1152

    Article  Google Scholar 

  5. Chen ZZ, Guillemot C (2010) Perceptually-Friendly H.264/AVC Video Coding Based on Foveated JND Model. IEEE Trans Circuits Syst Vid Technol 20(6):806–819

    Article  Google Scholar 

  6. Chen H, Hu R M, Hu J H, Wang Z Y (2010) Temporal color just noticeable distortion model and its application for video coding, in: Proceedings of IEEE ICME2010, pp. 713–718

  7. Chou C-H, Li Y-C (1995) A Perceptually tuned Subband image coder based on the measure of just-noticeable-distortion profile. IEEE Trans Circuits Syst Vid Technol 5(6):467–476

    Article  Google Scholar 

  8. Deng X, Xu M, Wang ZL (2013) A ROI-based Bit Allocation Scheme for HEVC towards Perceptual Conversational Video Coding, in: Proceedings of 2013 Sixth International Conference on Advanced Computational Intelligence (ICACI), pp. 19–21

  9. Flynn D, Marpe D, Naccari M, Nguyen T, Rosewarne C, Sharman K, Sole J, Xu J (2016) Overview of the range extensions for the HEVC standard: Tools, profiles, and performance. IEEE Trans Circuits Syst Vid Technol 26(1):4–19

    Article  Google Scholar 

  10. Guraya FFE, Alaya Cheikh F (2015) Neural networks based visual attention model for surveillance videos. Neurocomputing 149:1348–1359

    Article  Google Scholar 

  11. Itti L, Koch C (2001) Computational modeling of visual attention. Nat Rev Neurosci 2:194–203

    Article  Google Scholar 

  12. Jarsky T, Cembrowski M, Logan SM, Kath WL, Riecke H, Demb JB, Singer JH (2011) A synaptic mechanism for retinal adaptation to luminance and contrast. J Neurosci 31(30):11003–11015

    Article  Google Scholar 

  13. Kim I-K High efficiency video coding (HEVC) test model 15 (HM15) encoder description, document JCTVC-Q1002 of ITU-T/ISO/IEC, JCTVC, Apr. 2014

  14. Kim J, Bae S, Kim M (2015) An HEVC-compliant perceptual video coding scheme based on JND models for variable block-sized transform kernels. IEEE Trans Circuits Syst Vid Technol 25(11):1786–1800

    Article  Google Scholar 

  15. Li Z, Qin S, Itti L (2011) Visual attention guided bit allocation in video compression. Image Vis Comput 29(1):1–14

    Article  Google Scholar 

  16. Lin Y-C, Lai J-C, Cheng H-C (2016) Coding unit partition prediction technique for fast video encoding in HEVC. Multimed Tool Appl 75(16):9861–9884

    Article  Google Scholar 

  17. Liu T, Zheng N, Ding W, Yuan Z (2008) Video attention: learning to detect a salient object sequence, in: Proceedings of ICPR, pp.1–4.

  18. Luo ZY, Song L, Zheng SB, Nam L (2013) H.264/Advanced Video Control Perceptual Optimization Coding Based on JND-Directed Coefficient Suppression. IEEE Trans Circuits Syst Vid Technol 23(6):935–948

    Article  Google Scholar 

  19. Methodology for the subjective assessment of the quality of television pictures, ITU-R BT.500–11, 2002

  20. Naccari M, Pereira F (2011) Advanced H.264/AVC-Based Perceptual Video Coding: Architecture, Tools, and Assessment. IEEE Trans Circuits Syst Vid Technol 21(6):766–782

    Article  Google Scholar 

  21. Peterson HA, Ahumada AJ, Watson AB, Improved detection model for DCT coefficient quantization, in: Proceedings of SPIE, Human Vision, Visual Processing, and Digital Display IV. 1993, 191–201

  22. Robson J, Graham N (1981) Probability summation and regional variation in contrast sensitivity across the visual field. Vis Res 21(3):409–418

    Article  Google Scholar 

  23. Sullivan GJ, Ohm JR, Han WJ, Wiegand T (2012) Overview of the high efficiency video coding (HEVC) standard. IEEE Trans Circuits Syst Vid Technol 22(12):1649–1668

    Article  Google Scholar 

  24. Tsai D-S, Chen Y-C (2014) Visibility bounds for visual secret sharing based on JND theory. Multimed Tool Appl 70(3):1825–1836

    Article  Google Scholar 

  25. Wei ZY, Ngan KN (2009) Spatio-temporal just noticeable distortion profile for Grey Scale image/video in DCT domain. IEEE Trans Circuits Syst Vid Technol 19(3):337–346

    Article  Google Scholar 

  26. Wu HR, Rao KR (2005) Digital video image quality and perceptual coding. CRC Press, Boca Raton

    Book  Google Scholar 

  27. Wu JJ, Lin WS, Shi GM, Wang XT, Fu L (2013) Pattern masking estimation in image with structural uncertainty. IEEE Transactions Image Processing 22(12):4892–4904

    Article  MathSciNet  MATH  Google Scholar 

  28. Xu M, Xu M, Wang ZL (2014) Region-of-interest based conversational HEVC coding with hierarchical perception model of face. IEEE J Select Topic Signal Process 8(3):475–489

    Article  Google Scholar 

  29. Yan CG, Zhang YD, Xu JZ, Dai F, Li L, Dai QH, Wu F (2014) A highly Parallel framework for HEVC coding unit partitioning tree decision on many-core processors. IEEE Signal Process letts 21(5):573–576

    Article  Google Scholar 

  30. Yan CG, Zhang YD, Xu JZ, Dai F, Zhang J, Dai QH, Wu F (2014) Efficient Parallel framework for HEVC motion estimation on many-core processors. IEEE Trans Circuits Syst Vid Technol 24(12):2077–2089

    Article  Google Scholar 

  31. Yan CG, Zhang YD, Dai F, Wang X, Li L, Dai QH (2014) Parallel deblocking filter for HEVC on many-core processor. Electron Lett 50(5):367–368

    Article  Google Scholar 

  32. Yan CG, Zhang YD, Dai F, Zhang J, Li L, Dai QH (2014) Efficient Parallel HEVC intra prediction on many-core processor. Electron Lett 50(5):805–806

    Article  Google Scholar 

  33. Yang XK, Ling WS, Lu ZK, Ong EP, Yao SS (2005) Just noticeable distortion model and its applications in video coding. Signal Process Image Commun 20(7):662–680

    Article  Google Scholar 

  34. Yang X, Lin W, Lu Z, Ong E, Yao S (2005) Motion-compensated residue preprocessing in video coding based on just-noticeable-distortion profile. IEEE Trans Circuits Syst Vid Technol 15(6):742–752

    Article  Google Scholar 

  35. Zeng HQ, Yang PAS, Ngan KN, Wang MH (2016) Perceptual sensitivity-based rate control method for high efficiency video coding, Multimed Tool Appl 75(17):10383–10596

  36. Zhong GY, He HX, Qing LB (2015) Yue li, a fast inter-prediction algorithm for HEVC based on temporal and spatial correlation. Multimed Tool Appl 74(24):11023–11043

    Article  Google Scholar 

  37. Zhong SH, Liu Y, Ng TY, Liu Y (2016) Perception-oriented video saliency detection via spatio-temporal attention analysis. Neurocomputing:1–11

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Acknowledgments

This work is partially supported by the National Key Research and Development Plan (Grant No.2016YFC0801001) and the NSFC Key Project (No. 61632001).

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Authors and Affiliations

  1. Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing, China

    Gang Wang, Yongfei Zhang, Bo Li, Rui Fan & Mingliang Zhou

  2. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China

    Yongfei Zhang & Bo Li

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  1. Gang Wang
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  2. Yongfei Zhang
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  3. Bo Li
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Correspondence to Yongfei Zhang.

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Wang, G., Zhang, Y., Li, B. et al. A fast and HEVC-compatible perceptual video coding scheme using a transform-domain Multi-Channel JND model. Multimed Tools Appl 77, 12777–12803 (2018). https://doi.org/10.1007/s11042-017-4914-4

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  • DOI: https://doi.org/10.1007/s11042-017-4914-4

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