Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

The Appraised Structure for Improving Quality in the Compressed Image Using EQI-AC Algorithm

  • Conference paper
  • First Online:
Image Processing and Capsule Networks (ICIPCN 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1200))

Included in the following conference series:

  • 857 Accesses

Abstract

The electronic form of data is compressed practically using techniques to acquire information which are not redundant is called image compression. After compression, the image gains high visual quality with reduced bit rate compared to the raw image. In the Internet of Things (IoT) environment, efficient transmission and storage of images are required for security purposes. Block splitting (BS) and discrete cosine transform (DCT) is applied initially on the raw images to reduce the actual bit rate which minimizes the mean square value (MSE) and the noise in the near-constant region. In this paper, EQI-AC (Enhanced Quality Image After Compression) algorithm is used to enhance contrast, intensity and brightness of the compressed image, which is balanced, for visualization. Peak signal noise ratio (PSNR) values are calculated and found that 1/3rd of the psnr value is decreased comparing to the original image as displayed in Table 1. Structure similarity index (SSIM) values are calculated and found that there is slight degrade from 0.01 to 0.1 range in the quality of the compressed image compared to the original image.

Table 1. Compression table shows the name of the raw image, its actual size, the compressed image with its shrunk file size.
Full size table

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Chung, S.-W., Kim, B.-K., Song, W.-J.: Removing chromatic aberration by digital image processing. Opt. Eng. 49(6) (2010). https://doi.org/10.1117/1.3455506

  2. Uhrina, M., Bienik, J., Mizdos, T.: Chroma subsampling influence on the perceived video quality for compressed sequences in high resolutions. Digit. Image Process. Comput. Graph. 15(4) (2017). https://doi.org/10.15598/aeee.v15i4.2414

  3. Alam, L., Dhar, P.K., Hasan, M.A.R., Bhuyan, M.G.S., Daiyan, G.M.: An improved JPEG image compression algorithm by modifying luminance quantization table (2019)

    Google Scholar 

  4. Temmermans, F., Bruylants, T., Schelkens, P., Ebrahimi, T.: An introduction to JPEG standards for digitization and archiving applications (2009)

    Google Scholar 

  5. Barni, M., Nowroozi, E., Tondi, B.: Detection of adaptive histogram equalization robust against JPEG compression (2018). https://doi.org/10.1109/iwbf.2018.8401564

  6. Chen, Z., Zhao, Y., Ni, R.: Detection for operation chain: histogram equalization and dither-like operation. KSII Trans. Internet Inf. Syst. 9(9), 3751–3770 (2015). ISSN 1976-7277. https://doi.org/10.3837/tiis.2015.09.026

  7. Cozzolino, D., Gragnaniello, D., Verdoliva, L.: Image forgery detection through residual-based local descriptors and block-matching. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 5297–5301. IEEE (2014)

    Google Scholar 

  8. Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., ter Haar Romeny, B., Zimmerman, J.B., Zuiderveld, K.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39(3), 355–368 (1987). ISSN 0734-189X. https://doi.org/10.1016/S0734-189X(87)80186-X

  9. Cao, G., Zhao, Y., Ni, R.: Forensic estimation of gamma correction in digital images. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 2097–2100. IEEE (2010)

    Google Scholar 

  10. Cao, G., Zhao, Y., Ni, R., Li, X.: Contrast enhancement-based forensics in digital images. IEEE Trans. Inf. Forensics Secur. 9(3), 515–525 (2014)

    Article  Google Scholar 

  11. Barni, M., Fontani, M., Tondi, B.: A universal technique to hide traces of histogram-based image manipulations. In: Proceedings of the on Multimedia and Security, pp. 97–104. ACM (2012)

    Google Scholar 

  12. De Rosa, A., Fontani, M., Massai, M., Piva, A., Barni, M.: Second-order statistics analysis to cope with contrast enhancement counter-forensics. IEEE Sig. Process. Lett. 22(8), 1132–1136 (2015)

    Article  Google Scholar 

  13. Pan, X., Zhang, X., Lyu, S.: Exposing image forgery with blind noise estimation. In: Proceedings of the Thirteenth ACM Multimedia Workshop on Multimedia and Security, pp. 15–20. ACM (2011)

    Google Scholar 

  14. Li, H., Luo, W., Qiu, X., Huang, J.: Identification of various image operations using residual-based features. IEEE Trans. Circuits Syst. Video Technol. 28, 31–45 (2016)

    Article  Google Scholar 

  15. Singh, N., Gupta, A.: Analysis of contrast enhancement forensics in compressed and uncompressed images. In: 2016 International Conference on Signal Processing and Communication (ICSC), pp. 303–307. IEEE (2016)

    Google Scholar 

  16. Barni, M., Nowroozi, E., Tondi, B.: Higher-order, adversary-aware, double jpeg-detection via selected training on attacked samples. In: 2017 25th European Signal Processing Conference (EUSIPCO), pp. 281–285, August 2017

    Google Scholar 

  17. Bestagini, P., Allam, A., Milani, S., Tagliasacchi, M., Tubaro, S.: Video codec identification. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2257–2260. IEEE (2012)

    Google Scholar 

  18. Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digitalimages. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)

    Article  Google Scholar 

  19. Verdoliva, L., Cozzolino, D., Poggi, G.: A feature-based approach for image tampering detection and localization. In: 2014 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 149–154. IEEE (2014)

    Google Scholar 

  20. Goljan, M., Fridrich, J., Cogranne, R.: Rich model for steganalysis of color images. In: 2014 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 185–190. IEEE (2014)

    Google Scholar 

  21. Kodovsky, J., Fridrich, J., Holub, V.: Ensemble classifiers for steganalysis of digital media. IEEE Trans. Inf. Forensics Secur. 7(2), 432–444 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Bharathidasan University, Tiruchirapalli, Tamilnadu, India

    M. Durairaj & J. Hirudhaya Mary Asha

Authors
  1. M. Durairaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Hirudhaya Mary Asha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. Hirudhaya Mary Asha .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Dayeh University, Changhua, Taiwan

    Joy Iong-Zong Chen

  2. Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. Department of Electronics and Computer Engineering, Tribhuvan University, Lalitpur, Nepal

    Subarna Shakya

  4. College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

    Abdullah M. Iliyasu

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Durairaj, M., Hirudhaya Mary Asha, J. (2021). The Appraised Structure for Improving Quality in the Compressed Image Using EQI-AC Algorithm. In: Chen, J.IZ., Tavares, J.M.R.S., Shakya, S., Iliyasu, A.M. (eds) Image Processing and Capsule Networks. ICIPCN 2020. Advances in Intelligent Systems and Computing, vol 1200. Springer, Cham. https://doi.org/10.1007/978-3-030-51859-2_19

Download citation

Publish with us

Policies and ethics

Search

Navigation