Skip to main content
Log in

The effect of using minimum decreasing technique on enhancing the quality of lossy compressed images

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

Abstract

With the wide use of social media platforms, the critical matter is to reduce the image size while maintaining the image quality to achieve faster transfer speeds over the networks and save space on storage devices. The compression techniques are categorized into lossless and lossy. Lossless techniques produced high-quality compressed images with no loss of any part of the images, but it has low performance compared to the lossy technique with high distortion rates. This paper studies the effects of applying the Minimum Decreasing Technique (MDT) over a set of lossy compression techniques and evaluates the impact on the image quality and size. This was achieved by applying specific steps that decrease the minimum pixel values from the pixel values inside the image. We implemented the MDT technique first before using the lossy ones on several images wildly used in the image processing field. The results were obtained based on quality standard metrics (MSE, MAE, PSNR, and CR). The MDT technique managed to keep the image quality as is without increasing or decreasing in the metrics when used with the lossy techniques, whether alone or hybrid; it also managed to reduce the compression ratio due to the MDT mechanism, which depends on the other arrays included with the compressed image. Moreover, the results showed the highest compression ratio obtained by the proposed technique with 2–8% impartments compared to the other single or hybrid methods.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Abdelghany HM, Morsy M, Elzalbany M (2017) Hybrid Image Compression Using DWT, DCT and Arithmetic Coding. International Journal for Research in Applied Science and Engineering Technology (IJRASET) 5:169–175

    Article  Google Scholar 

  2. Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering.: Springer

  3. Abualigah L, Diabat A (2021) Advances in sine cosine algorithm: a comprehensive survey. Artif Intell Rev 54:1–42

    Article  Google Scholar 

  4. Abualigah L, Dulaimi AJ (2021) A novel feature selection method for data mining tasks using hybrid Sine Cosine Algorithm and Genetic Algorithm. Clust Comput 24:1–16

    Article  Google Scholar 

  5. Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795

    Article  Google Scholar 

  6. Abualigah LM, Khader AT, Hanandeh ES (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. Journal of Computational Science 25:456–466

    Article  Google Scholar 

  7. Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609

    Article  MathSciNet  MATH  Google Scholar 

  8. Abualigah L, Elaziz MA, Sumari P, Geem ZW, Gandomi AH (2022) Reptile search algorithm (RSA): a nature-inspired meta-heuristic optimizer. Expert Syst Appl 191:116158

    Article  Google Scholar 

  9. Abuowaida SFA, et al. (2021) A novel instance segmentation algorithm based on improved deep learning algorithm for multi-object images. Jordan J Comput Inf Technol (JJCIT), 7(01)

  10. Agushaka JO, Ezugwu AE, Abualigah L (2022) Dwarf mongoose optimization algorithm. Comput Methods Appl Mech Eng 391:114570

    Article  MathSciNet  MATH  Google Scholar 

  11. Ahar A, Barri A, Schelkens P (2017) From sparse coding significance to perceptual quality: a new approach for image quality assessment. IEEE Trans Image Process 27(2):879–893

    Article  MathSciNet  MATH  Google Scholar 

  12. Al Shami AL (2018) And M. Otair, enhancing quality of Lossy compressed images using minimum decreasing technique. Int J Adv Comput Sci Appl 9(3):397–404

    Google Scholar 

  13. Amirshahi SA, Pedersen M, Beghdadi A (2018) Reviving traditional image quality metrics using CNNs. In color and imaging conference. 2018. Soci Imaging Sci Technol

  14. Bhateja V, et al. (2018) Information Systems Design and Intelligent Applications: Proceedings of Fourth International Conference INDIA 2017. Vol. 672.: Springer

  15. Chang CC, Lin CC, Tseng CS, Tai WL (2007) Reversible hiding in DCT-based compressed images. Inform Sci 177(13):2768–2786

    Article  Google Scholar 

  16. Deng L, Sun H, Li C (2020) JDF-DE: a differential evolution with Jrand number decreasing mechanism and feedback guide technique for global numerical optimization. Appl Intell 51:1–18

    Google Scholar 

  17. Ha M, Kim K, Yoo H (2016) Lossless preprocessing of floating-point data for 3D geometry data compression. in Workshop on Image Processing and Image Understanding (IPIU2016)

  18. Haque NI, et al. (2018) A technique to enrich the secrecy level of high capacity data hiding steganography technique in JPEG compressed image. In 2018 5th international conference on networking, systems and security (NSysS). IEEE

  19. Hasan TS (2017) Image compression using discrete wavelet transform and discrete cosine transform. Journal of Applied Sciences Researches 13:1–8

    Google Scholar 

  20. Hilles SM, Hossain MA (2018) Classification on image compression methods. International Journal of Data Science Research 1(1):1–7

    Google Scholar 

  21. Irmak E, Ertas AH (2016) A review of robust image enhancement algorithms and their applications. 2016 IEEE Smart Energy Grid Engineering (SEGE), pp 371–375

  22. Kouadria N, Mechouek K, Harize S, Doghmane N (2019) Region-of-interest based image compression using the discrete Tchebichef transform in wireless visual sensor networks. Comput Electr Eng 73:194–208

    Article  Google Scholar 

  23. Langdon WB, Dolado J, Sarro F, Harman M (2016) Exact mean absolute error of baseline predictor, MARP0. Inf Softw Technol 73:16–18

    Article  Google Scholar 

  24. Menassel R, Nini B, Mekhaznia T (2018) An improved fractal image compression using wolf pack algorithm. Journal of Experimental & Theoretical Artificial Intelligence 30(3):429–439

    Article  Google Scholar 

  25. Otair M, Shehadeh F (2016) Lossy image compression by rounding the intensity followed by dividing (rifd). Res J Appl Sci Eng Technol 12(6):680–685

    Article  Google Scholar 

  26. Oyelade ON, Ezugwu AES, Mohamed TIA, Abualigah L (2022) Ebola optimization search algorithm: a new nature-inspired metaheuristic optimization algorithm. IEEE Access 10:16150–16177

    Article  Google Scholar 

  27. Padmapriya VM, Thenmozhi K, Praveenkumar P, Amirtharajan R (2020) ECC joins first time with SC-FDMA for Mission “security.” Multimed Tools Appl 79(25):17945–17967

  28. Patin F (2003) An introduction to digital image processing. online]: https://www.programmersheaven.com/articles/patin/ImageProc.pdf. Accessed 1-4-2022

  29. Phamila YAV, Amutha R (2014) Discrete cosine transform based fusion of multi-focus images for visual sensor networks. Signal Process 95:161–170

    Article  Google Scholar 

  30. Preparata FP, Shamos MI (2012) Computational geometry: an introduction.: Springer Science & Business Media

  31. Raghavendra C, Sivasubramanian S, Kumaravel A (2019) Improved image compression using effective lossless compression technique. Clust Comput 22(2):3911–3916

    Article  Google Scholar 

  32. Rajan PVS, Fred AL (2019) An efficient compound image compression using optimal discrete wavelet transform and run length encoding techniques. J Intell Syst 28(1):87–101

    Article  Google Scholar 

  33. Ren W, Liu S, Ma L, Xu Q, Xu X, Cao X, Yang MH (2019) Low-light image enhancement via a deep hybrid network. IEEE Trans Image Process 28(9):4364–4375

    Article  MathSciNet  MATH  Google Scholar 

  34. Ren W, Pan J, Zhang H, Cao X, Yang MH (2020) Single image dehazing via multi-scale convolutional neural networks with holistic edges. Int J Comput Vis 128(1):240–259

    Article  Google Scholar 

  35. Richardson D (2007) Zero tests for constants in simple scientific computation. Math Comput Sci 1(1):21–37

    Article  MathSciNet  MATH  Google Scholar 

  36. Said A, Pearlman WA (1996) An image multiresolution representation for lossless and lossy compression. IEEE Trans Image Process 5(9):1303–1310

    Article  Google Scholar 

  37. Shehab M, Daoud MS, AlMimi HM, Abualigah LM, Khader AT (2019) Hybridising cuckoo search algorithm for extracting the ODF maxima in spherical harmonic representation. International Journal of Bio-Inspired Computation 14(3):190–199

    Article  Google Scholar 

  38. Singh M, Kumar S, Singh S, Shrivastava M (2016) Various image compression techniques: Lossy and lossless. International Journal of Computer Applications 142(6):23–26

    Article  Google Scholar 

  39. Su Q, Chen B (2018) Robust color image watermarking technique in the spatial domain. Soft Comput 22(1):91–106

    Article  Google Scholar 

  40. Tang Z, Zheng Y, Gu K, Liao K, Wang W, Yu M (2018) Full-reference image quality assessment by combining features in spatial and frequency domains. IEEE Trans Broadcast 65(1):138–151

    Article  Google Scholar 

  41. Uthayakumar J, Elhoseny M, Shankar K (2020) Highly reliable and low-complexity image compression scheme using neighborhood correlation sequence algorithm in WSN. IEEE Trans Reliab 69:1398–1423

    Article  Google Scholar 

  42. Vyas A, Yu S, Paik J (2018) Image Restoration, in Multiscale Transforms with Application to Image Processing. Springer. p. 133–198

  43. Wang S, Gu K, Zeng K, Wang Z, Lin W (2016) Objective quality assessment and perceptual compression of screen content images. IEEE Comput Graph Appl 38(1):47–58

    Article  Google Scholar 

  44. Yam KL, Papadakis SE (2004) A simple digital imaging method for measuring and analyzing color of food surfaces. J Food Eng 61(1):137–142

    Article  Google Scholar 

  45. Yousri D, Abd Elaziz M, Abualigah L, Oliva D, al-qaness MAA, Ewees AA (2021) COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions. Appl Soft Comput 101:107052

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Laith Abualigah.

Ethics declarations

Conflict of interest

There is no conflict of interest.

Additional information

Publisher’s note

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

Appendix

Appendix

Appendix (A): Set of the images used in the experiments.

Gray 8-bit.

figure a

Colour RGB.

figure b

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Otair, M., Hasan, O.A. & Abualigah, L. The effect of using minimum decreasing technique on enhancing the quality of lossy compressed images. Multimed Tools Appl 82, 4107–4138 (2023). https://doi.org/10.1007/s11042-022-13404-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13404-y

Keywords

Navigation