Image Quality Assessment Technique Using Gradient Magnitude Similarity with Phase Congruency  
  Authors : Rohit Kumar; Vishal Moyal

 

The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images in a perceptually consistent manner. Image QA algorithms generally interpret image quality as fidelity or similarity with a “reference” or “perfect” image in some perceptual space. In order to improve the assessment accuracy of white noise, Gauss blur, JPEG2000 compression and other distorted images, this paper puts forward an image quality assessment method based on phase congruency and gradient magnitude. The experimental results show that the image quality assessment method has a higher accuracy than traditional method and it can accurately reflect the image visual perception of the human eye. In this paper, we propose an image information measure that quantifies the information that is present in the reference image and how much of this reference information can be extracted from the distorted image.

 

Published In : IJCAT Journal Volume 1, Issue 11

Date of Publication : 31 December 2014

Pages : 582 - 586

Figures :03

Tables : 01

Publication Link :Image Quality Assessment Technique Using Gradient Magnitude Similarity with Phase Congruency

 

 

 

Rohit Kumar : Department of Electronics & Communication

Vishal Moyal : Department of Electronics & Communication

 

 

 

 

 

 

 

Image quality assessment (IQA)

Structural similarity index (SSIM)

Phase congruency (PC)/p>

Gradient magnitude (GM)

Low level feature

In this paper, we proposed a novel efficient and effective IQA index, based on a specific visual saliency model.This is designed based on the assumption that an image’s visual saliency map has a close relationship with its perceptual quality. Experimental results indicate that it could yield statistically better prediction performance than all the other competing methods evaluated. Thus,it can be the best candidate of IQA indices for real time applications.

 

 

 

 

 

 

 

 

 

[1] Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli,“Image quality assessment: from error visibility to structural similarity,” IEEE Trans. IP, vol. 13, pp. 600-612, 2004. [2] N. Damera-Venkata, T.D. Kite, W.S. Geisler, B.L. Evans, and A.C. Bovik, “Image quality assessment based on a degradation model”, IEEE Trans. Image Process., vol. 9, no. 4, pp. 636-650, Apr. 2000. [3] D.M. Chandler and S.S. Hemami, “VSNR: a wavelet-based visual signal-to-noise ratio for natural images”, IEEE Trans. Image Process., vol. 16, no. 9, pp. 2284-2298, Sep. 2007. [4] H.R. Sheikh and A.C. Bovik, “Image information and visual quality”, IEEE Trans. Image Process., vol. 15, no. 2, pp. 430-444, Feb. 2006. [5] Z. Wang, E.P. Simoncelli, and A.C. Bovik, “Multi-scale structural similarity for image quality assessment”, presented at the IEEE Asilomar Conf. Signals, Systems and Computers, Nov. 2003. [6] H.R. Sheikh, M.F. Sabir, and A.C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms”, IEEE Trans. Image Process., vol. 15, no. 11, pp. 3440-3451, Nov. 2006. [7] N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti, “TID2008 - A database for evaluation of full-reference visual quality assessment metrics”, Advances of Modern Radioelectronics, vol. 10, pp.30-45, 2009. [8] Z. Liu and R. Laganière, “Phase congruence measurement for image similarity assessment”, Pattern Recognit. Letters, vol. 28, no. 1, pp. 166- 172, Jan. 2007. [9] Z. Wang and E.P. Simoncelli, “Local phase coherence and the perception of blur”, in Adv. Neural Information Processing Systems., 2004, pp. 786-792. [10] R. Hassen, Z. Wang, and M. Salama, “No reference image sharpness assessment based on local phase coherence measurement”, in Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing, 2010, pp. 2434-2437 [11] P. Kovesi, “Image features from phase congruency”, Videre: J. Comp. Vis. Res., vol. 1, no.3, pp. 1-26, 1999. [12] D. Marr and E. Hildreth, “Theory of edge detection”, Proc. R. Soc. Lond. B, vol. 207, no. 1167, pp. 187-217, Feb.1980. [13] M.C. Morrone and D.C. Burr, “Feature detection in human vision: a phase-dependent energy model”, Proc. R. Soc.Lond. B, vol. 235, no. 1280, pp. 221- 245, Dec. 1988. [14] M.C. Morrone and R.A. Owens, “Feature detection from local energy”, Pattern Recognit.Letters, vol. 6, no. 5, pp.303-313, Dec. 1987.