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New Quality Metric for Compressed Images

    Fatimah S. Abdulsattar Maath Jasem Mahammad Dhafer R. Zaghar

Anbar Journal of Engineering Sciences, 2020, Volume 11, Issue 2, Pages 154-161
10.37649/aengs.2023.176830

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Abstract

The field of image processing has several applications in our daily life. The image quality can be affected by a wide variety of deformations during image acquisition, transmission, compression, etc. Image compression is one of the applications where the quality of the image plays an important role since it can be used to evaluate the performance of various image compression techniques. Many image quality assessment metrics have been proposed. This paper proposes a new metric to assess the quality of compressed images. The principle idea of this metric is to estimate the amount of lost information during image compression process using three components: error magnitude, error location and error distribution. We denote this metric as MLD, which combines the objective assessment (error magnitude) and the subjective assessment (error location and error distribution). First, the metric is used to estimate the quality of compressed images using the JPEG algorithm as this is a standard lossy image compression technique. Then, the metric is used to estimate the quality of compressed images using other compression techniques. The results illustrate that the proposed quality metric is correlated with the subjective assessment better than other well-known objective quality metrics such as SSIM, MSE and PSNR. Moreover, using the proposed metric the JPEG2000 algorithm produces better quality results as compared to the JPEG algorithm especially for higher compression ratios
Keywords:
    Image Quality Assessment Image Compression PSNR MSE SSIM JPEG JPEG2000
Main Subjects:
  • Computer Engineering and Information Technology
  • Electrical Engineering
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(2020). New Quality Metric for Compressed Images. Anbar Journal of Engineering Sciences, 11(2), 154-161. doi: 10.37649/aengs.2023.176830
Fatimah S. Abdulsattar; Maath Jasem Mahammad; Dhafer R. Zaghar. "New Quality Metric for Compressed Images". Anbar Journal of Engineering Sciences, 11, 2, 2020, 154-161. doi: 10.37649/aengs.2023.176830
(2020). 'New Quality Metric for Compressed Images', Anbar Journal of Engineering Sciences, 11(2), pp. 154-161. doi: 10.37649/aengs.2023.176830
New Quality Metric for Compressed Images. Anbar Journal of Engineering Sciences, 2020; 11(2): 154-161. doi: 10.37649/aengs.2023.176830
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[1]      Okarma, K., Extended hybrid image similarity–combined full-reference image quality metric linearly correlated with subjective scores. Elektronika ir Elektrotechnika 2013; 19(10);  129-132.

[2]      Umme Sara, Morium Akter, Mohammad Shorif Uddin, Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study, Journal of Computer and Communications 2019; 7: 8-18.

[3]       Wang, Z., Bovik, A. C., Sheikh, H.R. and Simoncelli, E.P., Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 2004; 13(4): 600-612.

[4]       Wang, Z., Simoncelli, E.P. and Bovik, A.C., November. Multiscale structural similarity for image quality assessment. In the IEEE Thirty-Seventh Asilomar Conference on Signals, Systems & Computers 2003; 2: 1398-1402.

[5]       Sheikh, H.R., Bovik, A.C. and De Veciana, G., An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Transactions on image processing 2005; 14(12): 2117-2128.

[6]       Sheikh, H.R. and Bovik, A.C., May. Image information and visual quality. In IEEE International Conference on Acoustics, Speech, and Signal Processing 2004; 3: iii-709.

[7]       Larson, E.C. and Chandler, D.M., Most apparent distortion: full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging 2010; 19(1); pp.011006(1-21).

[8]       Mou, X., Zhang M, Xue W, Zhang L. Image quality assessment based on edge. In Digital Photography VII, International Society for Optics and Photonics 2011; 7876: pp. 78760N(1-9).

[9]       Attar, A., Shahbahrami, A., Rad, R.M. Image quality assessment using edge-based features. Multimedia Tools and Applications 2019; 75(12): pp.7407-7422.

[10]    Shi Z, Zhang J, Cao Q, Pang K, Luo T. Full-reference image quality assessment based on image segmentation with edge feature. Signal Processing 2018; 145: pp. 99-105.

[11]    Bosse, S., Maniry, D., Wiegand, T., Samek, W. A deep neural network for image quality assessment2016; In IEEE International Conference on Image Processing (ICIP); pp. 3773-3777.

[12]    Talebi H, Milanfar P. NIMA: Neural image assessment. IEEE Transactions on Image Processing 2018; 27(8): pp.3998-4011.

[13]    Mansouri, A. and Mahmoudi-Aznaveh, A., SSVD: Structural SVD-based image quality assessment. Signal Processing: Image Communication2019; 74: pp.54-63.

[14]    Liu, T.J., Lin, W. and Kuo, C.C.J., Image quality assessment using multi-method fusion. IEEE Transactions on image processing 2013; 22(5), pp.1793-1807.

[15]    Oszust, M., Decision fusion for image quality assessment using an optimization approach. IEEE Signal Processing Letters 2016; 23(1): pp.65-69.

[16]    Saha, A. and Wu, Q.J., Full-reference image quality assessment by combining global and local distortion measures. Signal Processing 2016; 128: pp.186-197.

[17]    Hamid R. Sheikh, Zhou Wang, Lawrence Cormack and Alan C. Bovik, Blind, Quality Assessment for JPEG2000 Compressed Images, Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, DOI: 10.1109/ACSSC.2002.1197072, IEEE Pacific Grove, CA, USA, USA, 2002.

[18]    Ratchakit Sakuldee, and Somkait Udomhunsakul, Objective Performance of Compressed Image Quality Assessments, World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering 2007; 1(11).

[19]    Walaa Khalaf, Dhafer Zaghar, and Noor Hashim, Enhancement of Curve-Fitting Image Compression Using Hyperbolic Function, MDPI Symmetry 2019; 11,291.

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