Skip to main content
SpringerLink
Log in

Image compression using JPEG with reduced blocking effects via adaptive down-sampling and self-learning image sparse representation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Blocking is an annoying effect in image compression using JPEG especially at low bit-rates. In this paper, a two-phase method is proposed for reducing JPEG blocking effects. In the first phase, the image is adaptively down-sampled via assessing the DCT coefficients of the image blocks. Since the blocks have a fixed size, independent to the size of the image, down-sampling can reduce the number of blocks, and hence reduce the blocking effects. Appropriate down-sampling before compression can improve coding performance, especially at low bit-rate. The decoder, after decompression, up-samples the image to its original resolution. Although, down-sampling alleviates the blocking effects, yet some blocking effects are remained in the result and the image is damaged due to resizing. Hence, self-learning sparse representation is applied in the second phase for a better deblocking and also for alleviating the degradation due to resizing. Experimental results demonstrate that the proposed method can efficiently improve the subjective and objective quality of JPEG compressed images at low bit-rates and outperforms the existing methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Aharon M, Elad M, Bruckstein AM (2006) The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311–4322

    Article  MATH  Google Scholar 

  2. Asadi Amiri S, Hassanpour H, Marouzi OR (2016) No-reference image quality assessment based on localized DCT for JPEG compressed images, multimedia tools and applications, (resubmitted with a major revision on July 2016)

  3. Cong Y, Zhang S, Lian Y (2015) K-SVD dictionary learning and image reconstruction based on variance of image patches. International symposium on computational intelligence and design (ISCID), pp 254–257

  4. Dong C, Deng Y, Change Loy C, Tang X, (2015) Compression artifacts reduction by a Deep convolutional network. IEEE international conference on Computer vision (ICCV), pp 576–584

  5. Dong C, Deng Y, Change LC, Tang X (2016) Random walk graph Laplacian based smoothness prior for soft decoding of JPEG images. IEEE Signal Process Soc 26(2):509–524

    MathSciNet  Google Scholar 

  6. Fadili JM, Starck JL, Bobin J, Moudden Y (2010) Image decomposition and separation using sparse representations: an overview. Proc IEEE 98(6):983–994

    Article  Google Scholar 

  7. Gao W, Chen J, Richard C, Huang J (2014) Online dictionary learning for kernel LMS. IEEE Trans Signal Process 62(11):2765–2777

    Article  MathSciNet  Google Scholar 

  8. Hassanpour H, Rostami GA (2013) Image enhancement via reducing impairment effects on image components. IJE Trans B: Appl 26(11):267–1274

    Google Scholar 

  9. Jeong Y, Kim I, Kang H (2000) A practical projection-based postprocessing of block coded images with fast convergence rate. IEEE Trans Circuits Syst Video Technol 10(4):617–623

    Article  Google Scholar 

  10. Jung C, Jiao L, Qi H, Sun T (2012) Image deblocking via sparse representation. Signal Process Image Commun 27(6):663–677

    Article  Google Scholar 

  11. Kang LW, Lin CW, Fu YH (2012) Automatic single-image-based rain streaks removal via image decomposition. IEEE Trans Image Process 21(4):1742–1755

    Article  MathSciNet  MATH  Google Scholar 

  12. Kang LW, Lin CW, Lin CT, Lin YC (2012) Self-learning-based rain streak removal for image/video. IEEE international symposium on Circuits and systems, pp 1871–1874

  13. Kang LW, Hsu CC, Zhuang B, Lin CW, Yeh CH (2015) Learning-based joint super-resolution and Deblocking for a highly compressed image. IEEE Trans Multimedia 17(7):921–934

    Article  Google Scholar 

  14. Kim J, Sim CB (2011) Compression artifacts removal by signal adaptive weighted sum technique. IEEE Trans Consum Electron 57(4):1944–1952

    Article  Google Scholar 

  15. Kim Y, Park CS, Ko SJ (2003) Fast POCS based postprocessing technique for HDTV. IEEE Trans Consum Electron 49(4):1438–1447

    Article  Google Scholar 

  16. Lin W, Dong L (2006) Adaptive downsampling to improve image compression at low bit rates. IEEE Trans Image Process 15(9):2513–2521

    Article  Google Scholar 

  17. Longere P, Zhang X, Delahunt PB, Brainaro DH (2002) Perceptual assessment of demosaicing algorithm performance. Proc IEEE 90(1):123–132

    Article  Google Scholar 

  18. Ma L, Li S, Ngan KN (2011) Perceptual image compression via adaptive block based super-resolution directed down-sampling. IEEE international symposium of Circuits and systems (ISCAS), pp 97–100

  19. Mairal J, Bach F, Ponce J, Sapiro G (2010) Online learning for matrix factorization and sparse coding. J Mach Learn Res 11:19–60

    MathSciNet  MATH  Google Scholar 

  20. Mallat S, Zhang Z (1993) Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process 41(12):3397–3415

    Article  MATH  Google Scholar 

  21. Nejati M, Samavi S, Karimi N, Soroushmehr SMR, Najarian K (2016) Boosted dictionary learning for image compression. IEEE Signal Process Soc 25(10):4900–4915

    MathSciNet  Google Scholar 

  22. Nieves FNM, Lopez-Rubio E, Lopez-Rubio FJ (2013) Adaptive kernel regression and probabilistic self-organizing maps for JPEG image deblocking. Neurocomputing 121:32–39

    Article  Google Scholar 

  23. Paek H, Kim RC, Lee SU (1998) On the POCS-based postprocessing technique to reduce the blocking artifacts in transform coded images. IEEE Trans Circuits Syst Video Technol 8(3):358–367

    Article  Google Scholar 

  24. Shen Y, Li J, Zhu Z, Cao W, Song Y (2015) Image reconstruction algorithm from compressed sensing measurements by dictionary learning. Neurocomputing 151(3):1153–1162

    Article  Google Scholar 

  25. Singha J, Singhb S, Singhc D, Uddin M (2011) A signal adaptive filter for blocking effect reduction of JPEG compressed images. Int J Electron Commun 65:827–839

    Article  Google Scholar 

  26. Tai SC, Chen YY, Shen SF (2005) Deblocking filter for low bit rate MPEG-4 video. IEEE Trans Circuits Syst Video Technol 5(6):733–741

    Google Scholar 

  27. Tsaig Y, Elad M, Golub G, Milanfar P (2003) Optimal framework for low bit-rate block coders. Int Conf Image Process 2:219–222

    Google Scholar 

  28. Vu CT, Phan TD, Chandler DM (2012) S3: a spectral and spatial measure of local perceived sharpness in natural images. IEEE Trans Image Process 21(3):934–945

    Article  MathSciNet  MATH  Google Scholar 

  29. Wang J, Shi Y, Kong D, Ding W, Li C, Yin B (2011) Sparse representation based down-sampling image compression. J Comput Appl Math 236:675–683

    Article  MathSciNet  MATH  Google Scholar 

  30. Wu X, Zhang X, Wang X (2009) Low bit-rate image compression via adaptive down-sampling and constrained least squares Upconversion. IEEE Transactions on Image Processing 18(3):552–561 

  31. Xu M, Li S, Lu J, Zhu W (2014) Compressibility constrained sparse representation with learnt dictionary for low bit-rate image compression. IEEE Circuits Syst Soc 24(10):1743–1757

    Google Scholar 

  32. Yan C, Zhang Y, Dai F, Wang X, Li L, Dai Q (2014) Parallel deblocking filter for HEVC on many-core processor. Electron Lett 50(5):367–368

    Article  Google Scholar 

  33. Yan C, Zhang Y, Xu J, Dai F, Li L, Dai Q, Wu F (2014) A highly parallel framework for HEVC coding unit partitioning tree decision on many-core processors. IEEE Signal Process Lett 21(5):573–576

    Article  Google Scholar 

  34. Yan C, Zhang Y, Xu J, Dai F, Zhang J, Dai Q, Wu F (2014) Efficient parallel framework for HEVC motion estimation on many-core processors. IEEE Trans Circuits Syst Video Technol 24(12):2077–2089

    Article  Google Scholar 

  35. Yang Y, Galatsanos NP, Katsaggelos AK (1993) Regularized reconstruction to reduce blocking artifacts of block discrete cosine transform compressed images. IEEE Trans Circuits Syst Video Technol 3(8):421–432

    Article  Google Scholar 

  36. Yeh CH, Jiang SJF, Ku TF, Chen MJ, Jhu JA (2012) Post-processing deblocking filter algorithm for various video decoders. IET Image Process 6(5):534–547

    Article  MathSciNet  Google Scholar 

  37. Yeh CH, Kang LW, Chiou YW, Lin CW, Fan Jiang SJ (2014) Self-learning-based post-processing for image/video deblocking via sparse representation. J Vis Commun Image Represent 25:891–903

    Article  Google Scholar 

  38. Yu K, Dong C, Change Loy C, Tang X (2016) Deep convolution networks for compression artifacts reduction. Preprint arXiv:1611.07233v1

  39. Zeng B, Venetsanopoulos AN (1993) A JPEG-based interpolative image coding scheme. IEEE international conference on acoustics, speech, and signal processing (ICASSP) 5:393–396

  40. Zhai G, Zhang W, Yang X, Lin W, Xu Y (2008) Efficient image deblocking based on postfiltering in shifted windows. IEEE Transactions on Circuits System and Video Technology 18(1):122–126

    Article  Google Scholar 

  41. Zhang S, Salari E (2003) Reducing artifacts in coded images using a neural network aided adaptive FIR filter. Neurocomputing 50:246–269

    MATH  Google Scholar 

  42. Zhang X, Wu X (2008) Can lower resolution be better. Data compression conference (DCC), pp 302–311

  43. Zhang X, Wu X, Wu F (2007) Image coding on quincunx lattice with adaptive lifting and interpolation. Data compression conference (DCC), pp 193–202

  44. Zhang Y, Salari E, Zhang S (2013) Reducing blocking artifacts in JPEG-compressed images using an adaptive neural network-based algorithm. Neural Comput & Applic 22:3–10

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Engineering & IT, Shahrood University of Technology, Shahrood, Iran

    Sekine Asadi Amiri & Hamid Hassanpour

Authors
  1. Sekine Asadi Amiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamid Hassanpour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamid Hassanpour.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amiri, S.A., Hassanpour, H. Image compression using JPEG with reduced blocking effects via adaptive down-sampling and self-learning image sparse representation. Multimed Tools Appl 77, 8677–8693 (2018). https://doi.org/10.1007/s11042-017-4763-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-4763-1

Keywords

Search

Navigation