Skip to main content
SpringerLink
Log in

Compression through extraction of learned parameters from images in de-correlated image space

  • Research
  • Published:
Iran Journal of Computer Science Aims and scope Submit manuscript

Abstract

Image compression is a class of algorithms that reduces the storage space requirement for a digital image. Lossy image compression techniques achieve higher compression but the visual quality of the decompressed image is degraded many times. Decompressed images lose their visual appeal due to compression artifacts. These compression artifacts are introduced due to the quantization step of the compression phase. We developed a lossy image compression technique that works on the spatial domain and de-correlated color model. For the luminance channel compression, the modified Vector Quantization method is used. In the case of chrominance channels, a feature vector is built for each pixel using the neighborhood statistics and cluster information of the pixel. For all the pixels of the image, using these feature vectors, a training dataset is formed. For the training of an artificial neural network (ANN), a feature vector of a pixel is used as the input and its respective chrominance value is used as the target output. Two training datasets are used to train two ANNs separately—one for the Cb channel and one for the Cr channel. These two trained ANNs are stored as the compressed form for the chrominance channels. During the decompression process, first, the luminance channel is reconstructed. Later, for each chrominance channel, the respective trained ANN predicts the chrominance values for each pixel. Thus, the whole image is reconstructed. The method has been tested on the benchmark images and also color images from the UCID v.2 database. The experimental result shows that the method successfully avoids the blocking artifacts in the reconstructed images.

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

Similar content being viewed by others

Availability of data and materials

Not applicable.

References

  1. Ranjan, R., Kumar, P.: An improved image compression algorithm using 2D DWT and PCA with canonical huffman encoding. Entropy 25(10), 1382 (2023). https://doi.org/10.3390/e25101382

    Article  Google Scholar 

  2. Garg, G., Kumar, R.: Analysis of different image compression techniques: a review. In: International Conference on Innovative Computing & Communication (ICICC) (2022). https://doi.org/10.2139/ssrn.4031725

  3. Hasnat, A., Barman, D.: A proposed multi-image compression technique. J. Intell. Fuzzy Syst. 36(4), 3177–3193 (2019). https://doi.org/10.3233/JIFS-18360

    Article  Google Scholar 

  4. Hernandez-Lopez, F.J., Muñiz-Pérez, O.: Parallel fractal image compression using quadtree partition with task and dynamic parallelism. J. Real-Time Image Proc. 19, 391–402 (2022). https://doi.org/10.1007/s11554-021-01193-w

    Article  Google Scholar 

  5. Hasnat, A., Barman, D., Halder, S., Bhattacharjee, D.: Modified vector quantization algorithm to overcome the blocking artefact problem of vector quantization algorithm. J. Intell. Fuzzy Syst. 32(5), 3711–3727 (2017). https://doi.org/10.3233/JIFS-169304

    Article  Google Scholar 

  6. Mahapatra, D.K., Jena, U.R. Partitional K-means clustering based hybrid DCT-vector quantization for image compression. In: IEEE Conference on ICT, Tamil Nadu, India (2013). https://doi.org/10.1109/CICT.2013.6558278

  7. Karri, C., Jena, U.: Fast vector quantization using a bat algorithm for image compression. Eng. Sci. Technol. Int. J. 19(2), 769–781 (2016). https://doi.org/10.1016/j.jestch.2015.11.003

    Article  Google Scholar 

  8. Chiranjeevi, K., Jena, U.R.: Image compression based on vector quantization using cuckoo search optimization technique. Ain Shams Eng. J. 9(4), 1417–1431 (2018). https://doi.org/10.1016/j.asej.2016.09.009

    Article  Google Scholar 

  9. Omran, M.G., Engelbrecht, A.P., Salman, A.: A color image quantization algorithm based on particle swarm optimization. Informatica 29, 261–269 (2005)

    Google Scholar 

  10. Celebi. M.E., Wen, Q., Chen, J.: Color quantization using c-means clustering algorithms. In: 18th IEEE International Conference on Image Processing, pp. 1729–1732 (2011). https://doi.org/10.1109/ICIP.2011.6115792

  11. Cheng, S.C., Yang, C.K.: A fast and novel technique for color quantization using reduction of color space dimensionality. Pattern Recognit. Lett. 22(8), 845–856 (2011). https://doi.org/10.1016/S0167-8655(01)00025-3

    Article  Google Scholar 

  12. Ozturk, C., Hancer, E., Karaboga, D.: Color image quantization: a short review and an application with artificial bee colony algorithm. Informatica 25(3), 485–503 (2014)

    Article  Google Scholar 

  13. Barman, D., Hasnat, A., Sarkar, S., Rahaman, M.A.: Color image quantization using gaussian particle swarm optimization (CIQ-GPSO). In: IEEE International Conference on Inventive Computation Technologies, India (2017). https://doi.org/10.1109/INVENTIVE.2016.7823295

  14. Hurtik, P., Perfilieva. I.: A hybrid image compression algorithm based on JPEG and fuzzy transform. In: IEEE Internatinal Conference on Fuzzy Systems (2017). https://doi.org/10.1109/FUZZ-IEEE.2017.8015614

  15. Quijas, J., Fuentes, O.: Removing JPEG blocking artifacts using machine learning. In: IEEE Conference on Image Analysis and Interpretation, San Diego, CA (2014). https://doi.org/10.1109/SSIAI.2014.6806033

  16. Wang, Z., Liu, D., Chang, S., Ling, Q., Yang, Y., Huang, T.S.: Deep dual-domain based fast restoration of JPEG-compressed images (2016). arXiv:1601.04149v3 [cs.CV]

  17. Prasetyo H, Wiranto, Winarno: Suppressing JPEG artifact using dot-diffused DC components modification. In: IEEE International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System and Information Technology (2018). https://doi.org/10.1109/ICACOMIT.2017.8253383

  18. Oztan, B., Malik, A., Fan, Z., Eschbach, R.: Removing ringing and blocking artifacts from JPEG compressed document images. US patent: US7634150 B2 (2009)

  19. Chang, H., Ng, M.K., Zeng, T.: Reducing artifacts in JPEG decompression via a learned dictionary. IEEE Trans. Signal Process. 62(3), 718–728 (2013). https://doi.org/10.1109/TSP.2013.2290508

    Article  MathSciNet  Google Scholar 

  20. Ahar, S., Mahmoudpour, S., Watanabe, O., Taubman, D., Schelkens, P.: Parameterization of the quality factor for the high throughput JPEG 2000. In: Proceedings of SPIE Photonics Europe, Photonics and Digital Technologies for Imaging Applications VI, 11353 (2020). https://doi.org/10.1117/12.2557414

  21. Chang, Y.W., Fang, H.C., Cheng, C.C., Chen, C.C., Chen, L.G.: Precompression quality-control algorithm for JPEG 2000. IEEE Trans. Image Process. 15(11), 3279–3293 (2006). https://doi.org/10.1109/TIP.2006.882013

    Article  Google Scholar 

  22. Wang, W., Zhu, E.: A new method of reducing boundary artifacts for JPEG2000 multi-tile coding. In: IEEE International Conference on Imaging Systems and Techniques (IST) (2015). https://doi.org/10.1109/IST.2015.7294570

  23. Wu, M.T.: Efficient reduction of artifact effect based on power and entropy measures. In: IEEE International Conference on Fuzzy System and Knowledge Discovery(FSKD) (2015). https://doi.org/10.1109/FSKD.2015.7382241

  24. Wang, Y., Porikli, F.: Multiple dictionary learning for blocking artifacts reduction. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan (2012). https://doi.org/10.1109/ICASSP.2012.6288086

  25. Schaefer, G., Stich, M.: UCID—An uncompressed colour image database. In: SPIE Storage and Retrieval Methods and Applications for Multimedia, San Jose, USA (2004)

  26. Preedanan, W., Kondo, T., Bunnun, P., Kumazawa, I.: A comparative study of image quality assessment, international workshop on advanced image technology (IWAIT). Chiang Mai (2018). https://doi.org/10.1109/IWAIT.2018.8369657

    Article  Google Scholar 

  27. Sara, U., Akter, M., Uddin, M.S.: Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study. J. Comput. Commun. 7(3), 8–18 (2019). https://doi.org/10.4236/jcc.2019.73002

    Article  Google Scholar 

  28. Ross, T.J.: Fuzzy Logic with Engineering Applications. Wiley, New Delhi (2012)

    Google Scholar 

  29. Kumari, G.V., Rao, G.S., Rao, B.P.: New artificial neural network models for biomedical image compression: biomedical image compression. Int. J. Appl. Metaheuristic Comput. 10(4), 91–111 (2019). https://doi.org/10.4018/IJAMC.2019100106

    Article  Google Scholar 

  30. Hasnat, A., Halder, S., Bhattacharjee, D., Nasipuri, M.: A novel approach for colorization of a grayscale image using soft computing techniques. Int. J. Multimedia Data Eng. Manag. (IJMDEM) 8(4), 13–43 (2017). https://doi.org/10.4018/IJMDEM.2017100102

    Article  Google Scholar 

Download references

Funding

The authors declare that there is no funding/financial assistance received to carry out the study.

Author information

Authors and Affiliations

  1. Government College of Engineering and Textile Technology, Berhampore, West Bengal, India

    Abul Hasnat

  2. Government College of Engineering and Leather Technology, Kolkata, West Bengal, India

    Santanu Halder

  3. Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India

    Debotosh Bhattacharjee

  4. Center for Basic and Applied Science, Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, 500 03, Hradec Kralove, Czech Republic

    Debotosh Bhattacharjee

Authors
  1. Abul Hasnat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Santanu Halder
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Debotosh Bhattacharjee
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Dr. Abul Hasnat and Prof. Santanu Halder carried out the work under the guidance of Prof. Debotosh Bhattacharjee. Prof. Bhattacharjee reviewed the draft article many times and suggested many modifications which were updated by Dr. Hasnat.

Corresponding author

Correspondence to Abul Hasnat.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial or personal interest in the work reported in this study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hasnat, A., Halder, S. & Bhattacharjee, D. Compression through extraction of learned parameters from images in de-correlated image space. Iran J Comput Sci (2024). https://doi.org/10.1007/s42044-024-00173-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42044-024-00173-0

Keywords

Search

Navigation