Advertisement

Digital image self-recovery algorithm based on improved joint source-channel coding optimizer

  • Yuying Gu
  • Hongmei YangEmail author
  • Bin Yan
  • Xiaodong Wang
  • Zhongying Zhao
Article
  • 10 Downloads

Abstract

The purpose of the digital image self-recovery is to restore high quality images as much as possible when the image is tampered. Existing algorithms can only achieve high recovered image quality with tiny tampering rate. To obtain high recovered image quality with large tampering rate, this paper proposes a digital image self-recovery algorithm based on improved JSCC (Joint Source-Channel Coding) optimizer. The algorithm performs quadtree decomposition of original grayscale image corresponding to different decomposition factors γ and performs bit-plane layering according to the block class obtained by quadtree decomposition. Size of each bit-plane is the number of pixels of each block class. Then, the original image is compressed by SPIHT, and the compressed bit-stream of SPIHT is segmented into bit-plane according to the size in order. The bit-planes are protected by different RS (Reed-Solomon) coders to get optimal decomposition result of corresponding γ. Finally, JSCC optimization is designed to get an optimal quality of recovered image. Experimental results show that, using our algorithm, for 2-LSB embedding, when the tampering rate is less the minimum TTR (Tolerable Tempering Rate), the PSNR is improved by 4.93dB. When the tampering rate is larger the minimum TTR, the PSNR is improved by 2dB. When 3-LSB watermark are embedded, the PSNR of recovered image is improved by 2.58dB on average. It shows that our improved optimizer effectively improves the quality of the recovered image at high tampering rates, compared with the similar algorithms.

Keywords

Image authentication recovery SPIHT coding RS coding Quadtree decomposition 

Notes

Acknowledgments

This work was funded by National Statistical Research of China (No. 2015LZ59), Key Projects of National Natural Science Foundation of China (No. 61433012), Qingdao Scientific Development Plan of China (No. KJZD-13-28-JCH), Natural Science Foundation of Shandong Province of China (No. ZR2014JL044). The authors is deeply grateful to everyone who contributed to this work, including the respectable reviewers for constructive comments, editors’ hard working, Mrs Yang for theoretical supporting, structural construction, experimental analysis and logical expression, Mr Yan for language editing and writing assistance, Mr Wang for technical editing, Mr Yan, Mrs Zhao and Mrs Yang for helping with acquisition of funding.

References

  1. 1.
    Baig N, Riaz MM, Ghafoor A, Siddiqui AM (2016) Image Dehazing Using Quadtree Decomposition and Entropy-Based Contextual Regularization. IEEE Signal Processing Letters 23(6):853–857CrossRefGoogle Scholar
  2. 2.
    Chen C, Wang L, Liu S (2018) The design of protograph LDPC codes as source codes in a JSCC system. IEEE Commun Lett 22(4):672–675CrossRefGoogle Scholar
  3. 3.
    Chung HW, Sadler BM, Zheng L, Hero AO (2017) Unequal error protection querying policies for the noisy 20 questions problem. Journal PP(99):1–1zbMATHGoogle Scholar
  4. 4.
    Dadkhah S, Abd Manaf A, Hori Y, Hassanien AE, Sadeghi S (2014) An effective SVD-based image tampering detection and self-recovery using active watermarking. Signal Process Image Commun 29(10):1197–1210CrossRefGoogle Scholar
  5. 5.
    Fridrich J, Goljan M (1999) Images with self-correcting capabilities. Int Conference Image Proc 3:792–796Google Scholar
  6. 6.
    He HJ, Zhang JS, Chen F (2009) Adjacent-block based statistical detection method for self-embedding watermarking techniques. Signal Process 89(8):1557–1566CrossRefGoogle Scholar
  7. 7.
    Jin L, Yang H (2018) Joint source-channel polarization with side information. IEEE Access PP(99):1–1Google Scholar
  8. 8.
    Korus P, Dziech A (2011) A novel approach to adaptive image authentication. IEEE international conference on image processing, pp 2765–2768Google Scholar
  9. 9.
    Korus P, Dziech A (2013) Efficient method for content reconstruction with self-embedding. IEEE Trans Image Process 22(3):1134–1147MathSciNetCrossRefGoogle Scholar
  10. 10.
    Ling D u, Cao X, Zhang W, Zhang X, Liu N (2016) Semi-fragile watermarking for image authentication based on compressive sensing. Sci China 59 (5):1–3Google Scholar
  11. 11.
    Liu M, Nie L, Wang X, Tian Q, Chen B (2019) Online data organizer: Micro-video categorization by structure-guided multimodal dictionary learning[J]. IEEE Trans Image Process 28(3):1235–1247MathSciNetCrossRefGoogle Scholar
  12. 12.
    Liu Z, Zhang F, Wang J, Wang H, Huang J (2016) Authentication and recovery algorithm for speech signal based on digital watermarking. Signal Process 123(C):157–166CrossRefGoogle Scholar
  13. 13.
    Nie L, Song X, Chua TS (2016) Learning from multiple social networks[J]. Synthesis Lectures on Information Concepts Retrieval & Services 8(2):118CrossRefGoogle Scholar
  14. 14.
    Nocedal J, Wright SJ (2006) Numerical optimization[M]. Springer, BerlinzbMATHGoogle Scholar
  15. 15.
    Qian Z, Feng G, Zhang X, Wang S (2011) Image self-embedding with high-quality restoration capability. Digital Signal Process 21(2):278–286CrossRefGoogle Scholar
  16. 16.
    Qian Z, Zhang X, Feng G (2016) Reversible data hiding in encrypted images based on progressive recovery. IEEE Signal Process Lett 23(11):1672–1676CrossRefGoogle Scholar
  17. 17.
    Qin C, Ji P, Wang J, Chang CC (2017) Fragile image watermarking scheme based on VQ index sharing and self-embedding. Multimed Tools Appl 76(2):2267–2287CrossRefGoogle Scholar
  18. 18.
    Qin C, Ji P, Zhang X, Dong J, Wang J (2017) Fragile image watermarking with pixel-wise recovery based on overlapping embedding strategy. Signal Process 138 (C):280–293CrossRefGoogle Scholar
  19. 19.
    Qin C, Zhang X (2015) Effective reversible data hiding in encrypted image with privacy protection for image content. J Vis Commun Image Represent 31(C):154–164CrossRefGoogle Scholar
  20. 20.
    Said A, Pearlman WA (1996) A new fast and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans Circuits Syst Video Technol 6(3):243–250CrossRefGoogle Scholar
  21. 21.
    Sarreshtedari S, Abbasfar A, Akhaee MA (2017) A joint source-channel coding approach to digital image self-recovery. Signal Image & Video Processing 11(7):1–8CrossRefGoogle Scholar
  22. 22.
    Sarreshtedari S, Akhaee MA (2015) A source-channel coding approach to digital image protection and self-recovery. IEEE Trans Image Process 24(7):2266–2277MathSciNetCrossRefGoogle Scholar
  23. 23.
    Shapiro JM (1993) Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans Signal Process 41(12):3445–3462CrossRefGoogle Scholar
  24. 24.
    Singh P, Agarwal S (2016) An efficient fragile watermarking scheme with multilevel tamper detection and recovery based on dynamic domain selection. Multimed Tools Appl 75(14):8165–8194CrossRefGoogle Scholar
  25. 25.
    Wicker SB, Bhargava VK (1999) Reed-solomon codes and their applications, vol 583-584. Wiley, HobokenCrossRefGoogle Scholar
  26. 26.
    Yin Z, Abel A, Tang J, Zhang X, Luo B (2017) Reversible data hiding in encrypted images based on multi-level encryption and block histogram modification. Multimed Tools Appl 76(3):3899–3920CrossRefGoogle Scholar
  27. 27.
    Yin Z, Niu X, Zhou Z, Tang J, Luo B (2016) Improved reversible image authentication scheme. Congnitive Computation 8(5):1–10Google Scholar
  28. 28.
    Zhang X, Wang S, Qian Z, Feng G (2011) Self-embedding watermark with flexible restoration quality. Multimed Tools Appl 54(2):385–395CrossRefGoogle Scholar
  29. 29.
    Bows2 (at) gipsa-lab (dot) grenoble-inp (dot) fr (2012) The dataset from the 2nd bows contest. [DB/OL]. Available: http://bows2.ec-lille.fr/

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Computer Science and EngineeringShandong University of Science and TechnologyQingdaoPeople’s Republic of China
  2. 2.College of Electronics, Communication and PhysicsShandong University of Science and TechnologyQingdaoPeople’s Republic of China
  3. 3.College of Material Science and EngineeringShandong University of Science and TechnologyQingdaoPeople’s Republic of China

Personalised recommendations