Image-independent optimal non-negative integer bit allocation technique for the DCT-based image transform coders

Access Full Text

Image-independent optimal non-negative integer bit allocation technique for the DCT-based image transform coders

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

© The Institution of Engineering and Technology
Received 11/10/2018, Accepted 16/09/2019, Revised 19/08/2019, Published 01/10/2019

The optimum non-negative integer bit allocation (ONIBA) is an important technique, which provides optimal quantisation of transform coefficients for the image transform coders (ITCs). However, the existing ONIBA algorithms are still not popular for the discrete cosine transform (DCT)-based ITCs, due to their image-dependent nature and additional side information requirements. Therefore, this study presents a novel image-independent ONIBA (IIONIBA) technique to achieve efficient quantisation for the DCT-ITCs. For the development of the proposed IIONIBA technique, initially, an image-dependent ONIBA algorithm is proposed, which is then mapped into desired image-independent solution via utilisation of a prepared combined image and proposed modified step size mapping technique. Thereafter, a new lookup table for the elements of quantisation tables, obtained from the proposed IIONIBA technique, is established using non-linear regression analysis, to reduce the problem of additional side information requirements. Several experiments are performed to evaluate the performance of the proposed IIONIBA technique based on the visual quality assessment of reconstructed images and the image quality indexes peak signal to noise ratio (PSNR) and mean structural similarity index (MSSIM). The results show that the proposed IIONIBA technique delivers better quantisation and provides significant gains in the image quality indexes as compared to the recent quantisation techniques.

Inspec keywords: image denoising; image coding; quantisation (signal); regression analysis; data compression; discrete cosine transforms; adaptive signal processing; wavelet transforms; image reconstruction; computational complexity

Other keywords: reconstructed images; coders; nonlinear regression analysis; optimal quantisation; existing ONIBA algorithms; prepared combined image; image quality indexes peak signal; image-independent ONIBA technique; quantisation tables; step size mapping technique; recent quantisation techniques; image-dependent ONIBA; image-dependent nature; efficient quantisation; transform coefficients; optimum nonnegative integer bit allocation; IIONIBA technique; desired image-independent solution; DCT-based image; additional side information requirements; nonnegative integer bit allocation technique; DCT-ITCs

Subjects: Computer vision and image processing techniques; Optical, image and video signal processing; Integral transforms; Integral transforms; Image and video coding

References

    1. 1)
      • 1. Manju, V.N., Lenin, F.A.: ‘AC coefficient and K-means cuckoo optimisation algorithm-based segmentation and compression of compound images’, IET Image Process.., 2018, 12, (2), pp. 218–225.
    2. 2)
      • 2. Thakur, V.S., Gupta, S., Thakur, K.: ‘Hybrid WPT-BDCT transform for high-quality image compression’, IET Image Process.., 2017, 11, (10), pp. 899–909.
    3. 3)
      • 3. Wallace, G.K.: ‘The JPEG still picture compression standard’, Commun. ACM, 1991, 34, (4), pp. 30–44.
    4. 4)
      • 4. Chang, H., Ng, M.K., Zeng, T.: ‘Reducing artifacts in JPEG decompression via a learned dictionary’, IEEE Trans. Sig. Proc., 2014, 62, (3), pp. 718–728.
    5. 5)
      • 5. Thai, T.H., Cogranne, R., Retraint, F., et al: ‘JPEG quantization step estimation and its applications to digital image forensics’, IEEE Trans. Infor. Foren. Secur., 2017, 12, (1), pp. 123–133.
    6. 6)
      • 6. Tyagi, V.: ‘Image compression’, in ‘Understanding digital image processing’ (CRC Press, Boca Raton, USA, 2018, 1st edn.), pp. 127–146.
    7. 7)
      • 7. ‘Generic Coding of Moving Pictures and Associated Audio Information-Part 2: Video’, ITU-T Rec. H.262 and ISO/IEC 13818-2 (MPEG-2 Video), version 1, 1994.
    8. 8)
      • 8. ‘Video Coding for Low Bitrate Communication’, ITU-T Rec. H.263, version 1, 1995, version 2, 1998, version 3, 2000.
    9. 9)
      • 9. ‘Advanced Video Coding for Generic Audio-Visual Services’, ITU-T Rec. H.264 and ISO/IEC 14496-10 (AVC), version 1, 2003, version 16, 2012.
    10. 10)
      • 10. Wien, M.: ‘High efficiency video coding: coding tools and specifications’ (Springer, Springer-Verlag Berlin Heidelberg, 2015, 1st edn.).
    11. 11)
      • 11. Ahmed, N., Natarajan, T., Rao, K.R.: ‘Discrete cosine transform’, IEEE Trans. Commun., 1974, 23, (1), pp. 90–93.
    12. 12)
      • 12. Gonzalez, R.C., Woods, R.E.: ‘Image compression’, in ‘Digital image processing’ (Prentice Hall, New Jersey, USA, 2012, 3rd edn.), pp. 526–538.
    13. 13)
      • 13. Huang, J., Schultheiss, P.: ‘Block quantization of correlated Gaussian random variables’, IEEE Trans. Commun. Syst., 1963, 11, (3), pp. 289–296.
    14. 14)
      • 14. Fox, B.: ‘Discrete optimization via marginal analysis’, Manage. Sci., 1966, 13, (3), pp. 210–216.
    15. 15)
      • 15. Farber, B., Zeger, K.: ‘Quantization of multiple sources using nonnegative integer bit allocation’, IEEE Trans. Inf. Theory, 2006, 52, (11), pp. 4945–4964.
    16. 16)
      • 16. Gazzah, H., Khandani, A. K.: ‘Optimum non-integer rate allocation using integer programming’, Electron. Lett., 1997, 33, (24), p. 2034, 1997.
    17. 17)
      • 17. Guo, L., Meng, Y.: ‘Round-up of integer bit allocation’, Electron. Lett., 2002, 38, (10), pp. 466–467.
    18. 18)
      • 18. Hachicha, W., Kaaniche, M., Beghdadi, A., et al: ‘Efficient inter-view bit allocation methods for stereo image coding’, IEEE Trans. Multimed., 2015, 17, (6), pp. 765–777.
    19. 19)
      • 19. Pak, M., Bayazıt, U.: ‘JPEG2000 bit allocation with edge-texture and fixation duration maps’. 24th Signal Processing and Communication Application Conf. (SIU), Zonguldak, 2016, pp. 281–284.
    20. 20)
      • 20. Bera, P., Gupta, R.: ‘Real-time compression of electrocardiogram using dynamic bit allocation strategy’. IEEE First Int. Conf. on Control, Measurement and Instrumentation (CMI), Kolkata, 2016, pp. 21–25.
    21. 21)
      • 21. Hachicha, W., Kaaniche, M., Beghdadi, A., et al: ‘Efficient inter-view bit allocation methods for stereo image coding’, IEEE Trans. Multimed., 2015, 17, (6), pp. 765–777.
    22. 22)
      • 22. Preihs, S., Ostermann, J.: ‘Globally optimized dynamic bit-allocation strategy for subband ADPCM-based low delay audio coding’. IEEE Intern. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, QLD, 2015, pp. 399–403.
    23. 23)
      • 23. Cheng, H., Lerner, C.: ‘Bit allocation for lossy image set compression’. 2015 IEEE Pacific Rim Conf. on Communications, Computers and Signal Processing (PACRIM), Victoria, BC, 2015, pp. 52–57.
    24. 24)
      • 24. Wu, J.F., Chan, S.C., Liu, A.L., et al: ‘An efficient rate allocation algorithm for transmission and storage of compressed biomedical signals in wireless health monitoring systems'. IEEE Region 10 Conf. (TENCON), Singapore, 2016, pp. 2694–2697.
    25. 25)
      • 25. Hatam, M., Shirazi, M.A.M.: ‘Optimum nonnegative integer bit allocation for wavelet based signal compression and coding’, Inf. Sci., 2015, 297, (10), pp. 332–334.
    26. 26)
      • 26. Hatam, M., Shirazi, M.A.M.: ‘Analytical method for optimum non-negative integer bit allocation’, IET Signal Process.., 2016, 10, (8), pp. 936–946.
    27. 27)
      • 27. Jayaraman, S., Esakkirajan, S., Veerakumar, T.: ‘Image compression’, in ‘Digital image processing’ (Tata McGraw Hill Education, New York, USA, 2009, 1st edn.), pp. 444–541.
    28. 28)
      • 28. Sun, C., Yang, E.H.: ‘An efficient DCT-based image compression system based on Laplacian transparent composite model’, IEEE Trans. Image Process., 2015, 24, (3), pp. 886–900.
    29. 29)
      • 29. Abu, N.A., Basari, A.S.H., Hussin, B.: ‘A novel psychovisual model on a standard resolution for video compression’, ARPN J. Eng. Appl. Sci., 2016, 11, (5), pp. 3259–3264.
    30. 30)
      • 30. Simone, F.D., Frossard, P., Wilkins, P., et al: ‘Geometry-driven quantization for omnidirectional image coding’. Picture Coding Symp. (PCS), Nuremberg, 2016, pp. 1–5.
    31. 31)
      • 31. SIPI Image Database (2019). Available at: http://sipi.usc.edu/database/..
    32. 32)
      • 32. Wang, Z., Bovik, A.C., Sheikh, H.R., et al: ‘Image quality assessment: from error visibility to structural similarity’, IEEE Trans. Image Process., 2004, 13, (4), pp. 600–612.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.6302
Loading

Related content

content/journals/10.1049/iet-ipr.2018.6302
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading