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Histogram-Equalized Hypercube Adaptive Linear Regression for Image Quality Assessment

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Abstract

Image Quality Assessment (IQA) becomes intensely salient in several applications, namely, acquisition of images, watermarking, image compression, image transmission, enhancement of images and so on, due to the extensive use of digital images. In the past decades, considerable advancements have been developed in IQA using Region of Interest (ROI). However, ROI localization is a labour-intensive process that takes multiple passes of sliding-window in search of proper ROI. The efficiency of examination, reduction in the time taken for ROI localization by multiple passes and the quality of the image can be improved by the proposed method, Histogram-Equalized Hypercube Adaptive Linear Regression (HE-HALR) scheme. HE-HALR scheme first performs the pre-processing step for input images. In this step, the features used to describe the quality of images are analysed using Histogram-Equalization-based Contrast Masking (HE-CM) model. The HE-CM model performs ROI localization with the parallelization programming that identifies the contrast masking and luminance value in a parallel manner. With the resultant feature vectors, dimensional reduction is performed using machine learning technique, namely, hypercubical neighbourhood. Finally, IQA is performed with the dimensionality-reduced features using Adaptive Linear Regression.

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References

  1. Wu L, Cheng J Z, Li S, Lei B, Wang T and Ni D 2017 FUIQA: fetal ultrasound image quality assessment with deep convolutional networks. IEEE Trans. Cybern. 47(5): 1336–1349

    Article  Google Scholar 

  2. Yang J, Lin Y, Ou B and Zhao X 2016 Image decomposition-based structural similarity index for image quality assessment. EURASIP J. Image Video Process. https://doi.org/10.1186/s13640-016-0134-5

    Article  Google Scholar 

  3. Barcena-Gonzalez G, Guerrero-Lebrero M P, Guerrero E, Yanez A, Fernandez-Reyes D, Gonzalez D and Galindo P L 2017 Evaluation of high-quality image reconstruction techniques applied to high-resolution Z-contrast imaging. Ultramicroscopy 182: 283–291

    Article  Google Scholar 

  4. Rahman S, Rahman M M, Abdullah-Al-Wadud M, Al-Quaderi G D and Shoyaib M 2016 An adaptive gamma correction for image enhancement. EURASIP J. Image Video Process. https://doi.org/10.1186/s13640-016-0138-1

    Article  Google Scholar 

  5. Singh G and Singh K 2017 Improved JPEG anti-forensics with better image visual quality and forensic undetectability. Forensic Sci. Int. 277: 133–147

    Article  Google Scholar 

  6. Yang C N, Hsu S C and Kim C 2017 Improving stego image quality in image interpolation based data hiding. Comput. Stand. Interfaces 50: 209–215

    Article  Google Scholar 

  7. Chow L S and Rajagopal H 2017 Modified-BRISQUE as no reference image quality assessment for structural MR images. Magn. Reson. Imaging 43: 74–87

    Article  Google Scholar 

  8. Abdel-Hamid L, El-Rafei A and Michelson G 2017 No-reference quality index for color retinal images. Comput. Biol. Med. 90(1): 68–75

    Article  Google Scholar 

  9. Siahaan E, Hanjalic A and Redi J A 2017 Semantic-aware blind image quality assessment. Sig. Process. Image Commun. 60: 237–252

    Article  Google Scholar 

  10. Guo X, Li Y, Suo T, Liu H and Zhang C 2017 Dynamic deformation image de-blurring and image processing for digital imaging correlation measurement. Opt. Lasers Eng. 98: 23–30

    Article  Google Scholar 

  11. Lee W J and Lee S W 2016 Improved spatio-temporal noise reduction for very low light environments. IEEE Trans. Circuits Syst. II Express Briefs 63(9): 888–892

    Article  Google Scholar 

  12. Ma K, Liu W, Liu T, Wang Z and Tao D 2017 dipIQ: blind image quality assessment by learning-to-rank discriminable image pairs. IEEE Trans. Image Process. 26(8): 3951–3964

    Article  MathSciNet  Google Scholar 

  13. Kim S, Chang Y and Ra J B 2017 Cardiac image reconstruction via non-linear motion correction based on partial angle reconstructed images. IEEE Trans. Med. Imaging 36(5): 1151–1161

    Article  Google Scholar 

  14. Zhang Y, NgiNgan K, Ma L and Li H 2017 Objective quality assessment of image retargeting by incorporating fidelity measures and inconsistency detection. IEEE Trans. Image Process. 26(12): 5980–5993

    Article  MathSciNet  Google Scholar 

  15. Song P, Manduca A, Trzasko J D and Chen S 2017 Ultrasound small vessel imaging with block-wise adaptive local clutter filtering. IEEE Trans. Med. Imaging 36(1): 251–262

    Article  Google Scholar 

  16. Hanhart P, Bernardo M V, Pereira M, Pinheiro A M G and Ebrahimi T 2015 Benchmarking of objective quality metrics for HDR image quality assessment. EURASIP J. Image Video Process. https://doi.org/10.1186/s13640-015-0091-4

    Article  Google Scholar 

  17. Liu T J, Liu K H, Lin J Y, Lin W and Jay Kuo C C 2017 A ParaBoost method to image quality assessment. IEEE Trans. Neural Netw. Learn. Syst. 28(1): 107–121

    Article  Google Scholar 

  18. Prasada Kumari K S 2016 Self-adaptive image processing using blind image quality assessment technique. Perspect. Sci. 8: 639–641

    Article  Google Scholar 

  19. Wang S, Gu K, Zeng K, Wang Z and Lin W 2018 Objective quality assessment and perceptual compression of screen content images. IEEE Comput. Graph. Appl. 38(1): 47–58

    Article  Google Scholar 

  20. Ma K, Duanmu Z, Wu Q, Wang Z, Yong H, Li H and Zhang L 2017 Waterloo exploration database: new challenges for image quality assessment models. IEEE Trans. Image Process. 26(2): 1004–1016

    Article  MathSciNet  Google Scholar 

  21. Sheikh H R, Sabir M F and Bovik A C 2006 A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans. Image Process. 15(11): 3440–3451

    Article  Google Scholar 

  22. Ponomarenko N, Carli M, Lukin V, Egiazarian K, Astola J and Battisti F 2008 Color image database for evaluation of image quality metrics. In: Proceedings of the 10th IEEE Workshop on Multimedia Signal Processing, Australia, pp. 403–408, https://doi.org/10.1109/mmsp.2008.4665112

  23. Balakrishnan N and Shantharajah S P 2014 Image denoising and contrast via intensity histogram equalization method. Int. Rev. Comput. Softw. 9(6): 988–996

    Google Scholar 

  24. Zhang L, Zhang L, Mou X and Zhang D 2011 FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8): 2378–2386

    Article  MathSciNet  Google Scholar 

  25. Zhang L, Shen Y and Li H 2014 VSI: a visual saliency induced index for perceptual image quality assessment. IEEE Trans. Image Process. 23(10): 4270–4281

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Department of Master of Computer Applications, Sona College of Technology, Salem, India

    N Balakrishnan

  2. School of Information Technology and Engineering, VIT University, Vellore, India

    S P Shantharajah

Authors
  1. N Balakrishnan
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  2. S P Shantharajah
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Correspondence to N Balakrishnan.

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Balakrishnan, N., Shantharajah, S.P. Histogram-Equalized Hypercube Adaptive Linear Regression for Image Quality Assessment. Sādhanā 44, 162 (2019). https://doi.org/10.1007/s12046-019-1148-3

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  • DOI: https://doi.org/10.1007/s12046-019-1148-3

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