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Optimal entropy-constrained non-uniform scalar quantizer design for low bit-rate pixel domain DVC

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Abstract

In this paper, an optimal entropy-constrained non-uniform scalar quantizer is proposed for the pixel domain DVC. The uniform quantizer is efficient for the hybrid video coding since the residual signals conforming to a single-variance Laplacian distribution. However, the uniform quantizer is not optimal for pixel domain distributed video coding (DVC). This is because the uniform quantizer is not adaptive to the joint distribution of the source and the SI, especially for low level quantization. The signal distribution of pixel domain DVC conforms to the mixture model with multi-variance. The optimal non-uniform quantizer is designed according to the joint distribution, the error between the source and the SI can be decreased. As a result, the bit rate can be saved and the video quality won’t sacrifice too much. Accordingly, a better R-D trade-off can be achieved. First, the quantization level is fixed and the optimal RD trade-off is achieved by using a Lagrangian function J(Q). The rate and distortion components is designed based on P(Y|Q). The conditional probability density function of SI Y depend on quantization partitions Q, P(Y|Q), is approximated by a Guassian mixture model at encocder. Since the SI can not be accessed at encoder, an estimation of P(Y|Q) based on the distribution of the source is proposed. Next, J(Q) is optimized by an iterative Lloyd-Max algorithm with a novel quantization partition updating algorithm. To guarantee the convergence of J(Q), the monotonicity of the interval in which the endpoints of the quantizer lie must be satisfied. Then, a quantizer partition updating algorithm which considers the extreme points of the histogram of the source is proposed. Consequently, the entropy-constrained optimal non-uniform quantization partitions are derived and a better RD trade-off is achieved by applying them. Experiment results show that the proposed scheme can improve the performance by 0.5 dB averagely compared to the uniform scalar quantization.

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References

  1. Aaron A, Setton E, Girod B (2003) Towards practical Wyner-Ziv coding of video. Proc. IEEE International Conference on Image Processing, ICIP 2003, Barcelona, Spain, Sept.

  2. Aaron A, Rane S, Setton E, Girod B (2004) Transform-domain Wyner-Ziv codec for video. Proc. Visual Communications and Image Processing (VCIP) 2004, San Jose, CA, Jan.

  3. Artigas X, Ascenso J, Dalai M, Klomp S, Kubasov D, Ouaret M (2007) The DISCOVER codec: architecture, techniques and evaluation. Proc. Picture Coding Symposium (PCS), Lisboa, Portugal, Nov.

  4. Becker-Lakus A, Leung K-M, Ma Z (2010) Bitwise prediction error correction for distributed video coding. Picture Coding Symposium (PCS) 2010, pp382–385, 8–10 Dec.

  5. Berger T (1972) Optimum quantizers and permutation codes. IEEE Trans Inf Theory 18:759–765

    Article  MATH  Google Scholar 

  6. Berrou C, Glavieux A (1996) Near optimum error correcting coding and decoding: turbo-codes. IEEE Trans. Comm., pp. 1261–1271, Oct.

  7. Brites C, Ascenso J, Pereira F (2006) Modeling correlation noise statistics at decoder for pixel based Wyner-Ziv video coding. Proc. Picture Coding Symposium (PCS), Beijing, China, April.

  8. Bruce R (1964) Optimum quantization. Sc.D. thesis, M.I.T., May 14

  9. Fang S, Li X, Zhang L (2007) A Lloyd-Max-based non-uniform quantization scheme for distributed video coding. Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, SNPD 2007, Vol. 1, pp. 848–853, July.

  10. Huang B, Ma J (2007) On asymptotic solutions of the Lloyd-Max scalar quantization. Information Communications & Signal Processing, 6th International Conference on, pp. 1–6.

  11. Kraskov A, Stogbauer H, Grassberger P (2004) Estimating joint distribution. Phys Rev E 69:066138

    Article  MathSciNet  Google Scholar 

  12. Kwong S, Xu L, Zhang Y, Zhao D (2012) Low-complexity encoder framework for window-level rate control optimization. IEEE Trans Ind Electron PP(99): Publication Year: Page(s): 1

  13. Lahsini C, Zaibi S, Pyndiah R, Bouallegue A (2011) Distributed video coding in pixel domain using spatial correlation at the decoder. Data Compression Conference (DCC) 2011, pp. 382–385, Mar.

  14. Lloyd S (1982) Least squares quantization in PCM. IEEE Trans Inf Theory IT-28:129–137

    Article  MathSciNet  Google Scholar 

  15. Max J (1960) Quantizing for minimum distortion. IEEE Trans Inf Theory IT-6:7–12

    Article  MathSciNet  Google Scholar 

  16. Muresan D, Effros M (2008) Quantization as histogram segmentation: optimal scalar quantizer design in network systems. IEEE Trans Inf Theory 54:344–366

    Article  MathSciNet  Google Scholar 

  17. Ou T-S, Huang Y-H, Chen HH (2011) SSIM-based perceptual rate control for video coding. IEEE Trans. Circuits Syst Video Technol

  18. Rebollo-Monedero D, Zhang R, Girod B (2003) Design of optimal quantizers for distributed source coding. in Proc. IEEE Data Compression Conf., Snowbird, UT, pp. 13–22, Mar.

  19. Roca A, Morbee M, Prades-Nebot J, Delp E (2008) Rate control algorithm for pixel-domain Wyner-Ziv video coding. Proc. Visual Communications and Image Processing (VCIP) 2008, San Jose, USA, Jan.

  20. Sheinin V, Jagmohan A, He D (2006) Uniform threshold scalar quantizer performance in Wyner-Ziv coding with memoryless, additive laplacian correlation channel. Proc. IEEE Conf. Acoust. Speech Sig. Proc., pp. 217–221, May.

  21. Slepian D, Wolf J (1973) Noiseless coding of correlated information sources. IEEE Trans Inf Theory 19:471–480

    Article  MATH  MathSciNet  Google Scholar 

  22. Swaroop KVS, Rao KR (2010) Performance analysis and comparison of JM 15.1 and Intel IPP H.264 encoder and decoder. System Theory (SSST), 2010 42nd Southeastern Symposium on, Publication Year: Page(s): 371–375

  23. Tu Z, Li TJ, Blum RS (2006) On scalar quantizer design with decoder side information. Information Sciences and Systems, 2006 40th Annual Conference on, pp. 224–229, Mar.

  24. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600–612

    Article  Google Scholar 

  25. Weerakkody WAR, Fernando WAC, Badem MB, Kondoz AM (2009) Nonlinear quantisation for pixel domain DVC. Electron Lett 45:261–262

    Article  Google Scholar 

  26. Wu X (1991) Optimal quantization by matrix searching. J Algorithms 12(4):663–673

    Article  MATH  MathSciNet  Google Scholar 

  27. Wu X, Zhang K (1993) Quantizer monotonicities and globally optimal scalar quantizer design. IEEE Trans Inf Theory 39:1049–1053

    Article  MATH  Google Scholar 

  28. Wu B, Guo X, Zhao D, Gao W, Wu F (2006) An optimal non-uniform scalar quantizer for distributed video coding. Proc. IEEE International Conference on Multimedia and Expo, ICME 2006, Toronto, Ontario, Canada, pp. 165–168, Jul.

  29. Wyner A, Ziv J (1976) The rate-distortion function for source coding with side information at the decoder. IEEE Trans Inf Theory IT-22(1):1–10

    Article  MathSciNet  Google Scholar 

  30. Zhang Y, Xiong H, He Z, Yu S, Chen CW (2011) Reconstruction for distributed video coding: a context-adaptive markov random field approach. IEEE Trans Circuits Syst Video Technol 21(8):1100–1114

    Article  Google Scholar 

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61103064) and the Science and Technology Program of Beijing Municipal Commission of Education (No.KM201010005011).

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Correspondence to Nan Zhang.

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Wu, B., Zhang, N., Ma, S. et al. Optimal entropy-constrained non-uniform scalar quantizer design for low bit-rate pixel domain DVC. Multimed Tools Appl 70, 1799–1824 (2014). https://doi.org/10.1007/s11042-012-1210-1

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