Image Quality Assessment Based on Fuzzy Similarity Measure and Wavelet Transform

Abstract:

Based on the characteristics of wavelet coefficients of images and fuzzy similarity measure, a novel image quality assessment is proposed in this paper. Firstly, the reference image and the distorted images are decomposed into several levels by means of wavelet transform respectively. The approximation and detail coefficients of the reference image (the distorted images) are as the reference sequences (the comparative sequences). Secondly, select the right membership function to map the referenced sequences and the comparative sequences to a membership value between 0 and 1 respectively. And calculate the fuzzy similarity measure values between the reference sequences and the comparative sequences respectively. Moreover, image quality assessment matrix of every distorted image can be constructed based on the fuzzy similarity measure values and image quality can be assessed. The algorithm makes full use of perfect integral comparison mechanism of fuzzy similarity measure and the well matching of discrete wavelet transform with multi-channel model of human visual system. Experimental results show that the proposed algorithm can not only evaluate the integral and detail quality of image fidelity accurately but also bears more consistency with the human visual system than the traditional method PSNR.

• Periodical: Advanced Materials Research (Volumes 181-182)
• Pages: 31-36
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.181-182.31
• Online since: January 2011
• Authors: Jun Feng Li, Wen Zhan Dai, Hui Jiao Wang
• Keywords: Fuzzy Similarity Measure, Human Visual System, Image Quality Assessment, Wavelet Transform (WT)

References

[1] Erez Cohen, Yitzhak Yitzhaky: No-reference assessment of blur and noise impacts on image. Signal, Image and Video Processing , no. 2 (2009), p.286.

DOI: 10.1007/s11760-009-0117-4

[2] Krzysztof Okarma, Piotr Lech: A Statistical Reduced-Reference Approach to Digital Image Quality Assessment. Computer Vision and Graphics, vol. 5337 (2009), p.43.

DOI: 10.1007/978-3-642-02345-3_5

[3] Liang Zhai, Xinming Tang: Fuzzy comprehensive evaluation method and its application in subjective quality assessment for compressed remote sensing images. Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, vol. 1(2007).

DOI: 10.1109/fskd.2007.321

[4] Suresh S, Venkatesh Babu R, Kim H. J: No-reference image quality assessment using modified extreme learning machine classifier. Applied Soft Computing Journal, vol. 9, no. 2(2009), p.541.

DOI: 10.1016/j.asoc.2008.07.005

[5] Choi Min Goo, Jung Jung Hoon, Jeon Jae Wook: No-reference image quality assessment using blur and noise. Proceedings of World Academy of Science, Engineering and Technology, vol. 38(2009), p.163.

[6] Brandão Tomás, Queluz Maria Paula: No-reference image quality assessment based on DCT domain statistics. Signal Processing, vol. 88, no. 4(2008), p.822.

DOI: 10.1016/j.sigpro.2007.09.017

[7] Larson Eric C, Chandler Damon M: Most apparent distortion: A dual strategy for full-reference image quality assessment. Proceedings of SPIE - The International Society for Optical Engineering, vol. 7242(2009), p.286.

DOI: 10.1117/12.810071

[8] Bianco S, Ciocca G, Marini F: Image quality assessment by preprocessing and full reference model combination. Proceedings of SPIE - The International Society for Optical Engineering, vol. 7242(2009), p.328.

DOI: 10.1117/12.806693

[9] Li Qiang, Wang Zhou: Reduced-reference image quality assessment using divisive normalization-based image representation. IEEE Journal on Selected Topics in Signal Processing, vol. 3, no. 2(2009), p.202.

DOI: 10.1109/jstsp.2009.2014497

[10] Engelke Ulrich, Kusuma Maulana, Zepernick Hans-Jürgen, Caldera Manora: Reduced-reference metric design for objective perceptual quality assessment in wireless imaging. Signal Processing: Image Communication, vol. 24, no. 7(2009), p.525.

DOI: 10.1016/j.image.2009.06.005

[11] Krzysztof Okarma, Piotr Lech: A Statistical Reduced-Reference Approach to Digital Image Quality Assessment. Computer Vision and Graphics, vol. 5337(2009), p.43.

DOI: 10.1007/978-3-642-02345-3_5

[12] Carnec Mathieu, Le Callet Patrick, Barba Dominique: Objective quality assessment of color images based on a generic perceptual reduced reference. Signal Processing: Image Communication, vol. 23, no. 4(2008), p.239.

DOI: 10.1016/j.image.2008.02.003