Soft Computing

, Volume 23, Issue 22, pp 11967–11978 | Cite as

A study of sine–cosine oscillation heterogeneous PCNN for image quantization

  • Zhen YangEmail author
  • Jing Lian
  • Shouliang Li
  • Yanan Guo
  • Yide Ma
Methodologies and Application


A new heterogeneous pulse-coupled neural network (HPCNN) is proposed to prune the boundary effects in image quantization. An oscillating sine–cosine pulse-coupled neural network (SC-PCNN) is combined with the morphological algorithm and two classical PCNNs which have different parameters corresponding to different image regions to form the proposed new HPCNN model (SC-HPCNN). This model retains the natural characteristics of classical PCNN while revealing its own merits; when it is used to accomplish image quantization, the quantization noise and boundary effects are removed dramatically, without significantly degrading image quality. Furthermore, experimental results also show that the proposed model outperforms previous approaches, and it operates in accordance with the characteristics of the human visual system.


PCNN SC-HPCNN Image quantization Boundary effect 



This work is jointly supported by the Natural Science Foundation of Gansu Province (No. 18JR3RA288) and the Fundamental Research Funds for the Central Universities of China (No. lzujbky-2018-it61).

Compliance with ethical standards

Conflict of interest

The authors declare that there are no conflict of interest


  1. Ahalt SC, Krishnamurthy AK, Chen P, Melton DE (1990) Competitive learning algorithms for vector quantization. Neural Netw 3(3):277–290CrossRefGoogle Scholar
  2. Alam MM, Nguyen TD, Hagan MT, Chandler DM (2015) A perceptual quantization strategy for HEVC based on a convolutional neural network trained on natural images. In: SPIE optical engineering \(+\) Applications, International Society for Optics and Photonics, pp 959,918–959,918Google Scholar
  3. Celik MU, Sharma G, Tekalp AM (2003) Gray-level-embedded lossless image compression. Signal Process Image Commun 18(6):443–454CrossRefGoogle Scholar
  4. Chen Y, Park SK, Ma Y, Ala R (2011) A new automatic parameter setting method of a simplified pcnn for image segmentation. IEEE Trans Neural Netw 22(6):880–892CrossRefGoogle Scholar
  5. Chiel H, Beer R, Sterling L (1989) Heterogeneous neural networks for adaptive behavior in dynamic environments. In: Advances in neural information processing systems, pp 577–585Google Scholar
  6. Chiranjeevi K, Jena U, Prasad P (2017) Hybrid cuckoo search based evolutionary vector quantization for image compression. In: Artificial intelligence and computer vision, Springer, Cham, pp 89–114Google Scholar
  7. Eckhorn R, Reitboeck H, Arndt M, Dicke P (1990) Feature linking via synchronization among distributed assemblies: simulations of results from cat visual cortex. Neural Comput 2(3):293–307CrossRefGoogle Scholar
  8. Ekblad U, Kinser JM, Atmer J, Zetterlund N (2004) The intersecting cortical model in image processing. Nuclear Instrum Methods Phys Res Sect A Accel Spectrom Detect Assoc Equip 525(1):392–396CrossRefGoogle Scholar
  9. El-said SA (2015) Image quantization using improved artificial fish swarm algorithm. Soft Comput 19(9):2667–2679CrossRefGoogle Scholar
  10. Feng H, Marcellin MW, Bilgin A (2015) A methodology for visually lossless jpeg2000 compression of monochrome stereo images. IEEE Trans Image Process A Publ IEEE Signal Process Soc 24(2):560–572MathSciNetCrossRefGoogle Scholar
  11. Gao Z, Xiong C, Ding L, Zhou C (2013) Image representation using block compressive sensing for compression applications. J Vis Commun Image Represent 24(7):885–894CrossRefGoogle Scholar
  12. Groach M, Garg A (2012) Dcspiht: image compression algorithm. Int J Eng Res Appl 2(2):560–567Google Scholar
  13. Guo Y, Dong M, Yang Z, Gao X, Wang K, Luo C, Ma Y, Zhang J (2016) A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified pcnn. Computer Methods Progr Biomed 130:31–45CrossRefGoogle Scholar
  14. Haddad Z, Beghdadi A, Serir A, Mokraoui A (2013) Wave atoms based compression method for fingerprint images. Pattern Recognit 46(9):2450–2464CrossRefGoogle Scholar
  15. Hu F, Pu C, Gao H, Tang M, Li L (2016) An image compression and encryption scheme based on deep learning. CoRR abs/1608.05001.
  16. Hu Z, Su Q, Xia X (2016) Multiobjective image color quantization algorithm based on self-adaptive hybrid differential evolution. Comput Intell Neurosci. CrossRefGoogle Scholar
  17. Huang Y, Ma J, Du S, Ma Y (2014) Human visual characteristics inspired adaptive image quantization method. Sampl Theory Signal Image Process 13(1530–6429):111–124MathSciNetzbMATHGoogle Scholar
  18. Huang Y, Ma Y, Li S, Zhan K (2016) Application of heterogeneous pulse coupled neural network in image quantization. J Electron Imaging 25(6):061,603-061,603CrossRefGoogle Scholar
  19. Hussain F, Jeong J (2016) Efficient deep neural network for digital image compression employing rectified linear neurons. J Sens 2016. Google Scholar
  20. Johnson JL, Ritter D (1993) Observation of periodic waves in a pulse-coupled neural network. Opt Lett 18(15):1253–1255CrossRefGoogle Scholar
  21. Kajitani I, Otsu N, Higuchi T (2003)Improvements in myoelectric pattern classification rate with \(\mu \)-law quantization. In: Proceedings of XVII IMEKO world congressGoogle Scholar
  22. Kaur N, Bawa N (2017) Algorithm for fuzzy based compression of gray jpeg images for big data storage. In: International conference on contemporary computing and informatics, pp 518–523Google Scholar
  23. Khaled A, Abdel-Kader RF, Yasein MS (2016) A hybrid color image quantization algorithm based on \(k\)-means and harmony search algorithms. Appl Artif Intell 30(4):331–351CrossRefGoogle Scholar
  24. Kinser JM (1996) Simplified pulse-coupled neural network. In: Aerospace/defense sensing and controls, International Society for Optics and Photonics, pp 563–567Google Scholar
  25. Li W, Zhu XF (2005) A new image fusion algorithm based on wavelet packet analysis and PCNN. In: Proceedings of 2005 international conference on machine learning and cybernetics, vol 9, IEEE, pp 5297–5301Google Scholar
  26. Li H, Jin X, Yang N, Yang Z (2015) The recognition of landed aircrafts based on pcnn model and affine moment invariants. Pattern Recognit Lett 51:23–29CrossRefGoogle Scholar
  27. Li T, Tian X, Xiong C, Tian J (2016) A coding scheme for noisy image based on layer segmentation. Chin J Electron 25(4):700–705CrossRefGoogle Scholar
  28. Lindblad T, Kinser JM, Lindblad T, Kinser J (1998) Image processing using pulse-coupled neural networks. Springer, BerlinCrossRefGoogle Scholar
  29. Liu N, Ye Y, Sun X, Liang J, Sun P (2016) Rotation invariant feature extracting of seal images based on PCNN. Springer, Berlin, pp 531–540Google Scholar
  30. Mainberger M, Weickert J (2011) Edge-based compression of cartoon-like images with homogeneous diffusion. Pattern Recognit 44(9):1859–1873CrossRefGoogle Scholar
  31. Nadenau MJ, Reichel J, Kunt M (2003) Wavelet-based color image compression: exploiting the contrast sensitivity function. IEEE Trans Image Process 12(1):58–70CrossRefGoogle Scholar
  32. Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11(285–296):23–27Google Scholar
  33. Özdemir D, Akarun L (2002) A fuzzy algorithm for color quantization of images. Pattern Recognit 35(8):1785–1791CrossRefGoogle Scholar
  34. Prakash A, Moran N, Garber S, Dilillo A, Storer J (2017) Semantic perceptual image compression using deep convolution networks. In: Data compression conference, pp 250–259Google Scholar
  35. Rufai AM, Anbarjafari G, Demirel H (2014) Lossy image compression using singular value decomposition and wavelet difference reduction. Digital Signal Process 24(1):117–123CrossRefGoogle Scholar
  36. Skourikhine AN, Prasad L, Schlei BR (2000) Neural network for image segmentation. In: Proceedings of SPIE international society for optical engineering, vol 4120, pp 28–35Google Scholar
  37. Toderici G, O’Malley SM, Hwang SJ, Vincent D, Minnen D, Baluja S, Covell M, Sukthankar R (2015) Variable rate image compression with recurrent neural networks. CoRR abs/1511.06085.
  38. Toderici G, Vincent D, Johnston N, Hwang SJ, Minnen D, Shor J, Covell M (2017) Full resolution image compression with recurrent neural networks. In: IEEE conference on computer vision and pattern recognition, pp 5435–5443Google Scholar
  39. Wang Z, Ma Y (2008) Medical image fusion using m-pcnn. Inf Fusion 9(2):176–185CrossRefGoogle Scholar
  40. Xiang T, Yan L, Gao R (2015) A fusion algorithm for infrared and visible images based on adaptive dual-channel unit-linking PCNN in NSCT domain. Infrared Phys Technol 69:53–61CrossRefGoogle Scholar
  41. Xiao B, Lu G, Zhang Y, Li W, Wang G (2016) Lossless image compression based on integer discrete tchebichef transform. Neurocomputing 214(C):587–593CrossRefGoogle Scholar
  42. Yang Z, Dong M, Guo Y, Gao X, Wang K, Shi B, Ma Y (2016) A new method of micro-calcifications detection in digitized mammograms based on improved simplified pcnn. Neurocomputing 218:79–90CrossRefGoogle Scholar
  43. Yi-de M, Fei S, Lian L (2003) A new kind of impulse noise filter based on PCNN. In: Proceedings of the 2003 international conference on neural networks and signal processing, vol 1, IEEE, pp 152–155Google Scholar
  44. Yide Ma RD, Lian L (2002) Automated image segmentation using pulse coupled neural networks and images entropy. J China Inst Commun 23(1):46–50Google Scholar
  45. Zhan K, Zhang H, Ma Y (2009) New spiking cortical model for invariant texture retrieval and image processing. IEEE Trans Neural Netw 20(12):1980–1986CrossRefGoogle Scholar
  46. Zhang C, He X (2013) Image compression by learning to minimize the total error. IEEE Trans Circuits Syst Video Technol 23(4):565–576CrossRefGoogle Scholar
  47. Zhang Y, Reinhard E, Bull DR (2012) Perceptually lossless high dynamic range image compression with jpeg 2000. In: 2012 19th IEEE international conference on image processing (ICIP), IEEE, pp 1057–1060Google Scholar
  48. Zhang Y, Alam MM, Chandler DM (2016) Visually lossless perceptual image coding based on natural-scene masking models. InTech, Rijeka. Google Scholar
  49. Zhou X, Podoleanu AG, Yang Z, Yang T, Zhao H (2012) Morphological operation-based bi-dimensional empirical mode decomposition for automatic background removal of fringe patterns. Opt Express 20(22):24247–24262CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Zhen Yang
    • 1
    Email author
  • Jing Lian
    • 2
  • Shouliang Li
    • 1
  • Yanan Guo
    • 1
  • Yide Ma
    • 1
  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouChina
  2. 2.School of Electronic and Information EngineeringLanzhou Jiaotong UniversityLanzhouChina

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