SWVFS: a saliency weighted visual feature similarity metric for image quality assessment

Abstract

In this paper, a saliency weighted visual feature similarity (SWVFS) metric is proposed for full reference image quality assessment (IQA). Instead of traditional spatial pooling strategies, a visual saliency-based approach is employed for better compliance with properties of the human visual system, where the saliency allocation is closely related to the activity of posterior parietal cortex and the pluvial nuclei of the thalamus. Assuming that the saliency map actually represents the contribution of locally computed visual distortions to the overall image quality, the gradient similarity and the textural congruency are merged into the final image quality indicator. The gradient and texture comparison play complementary roles in characterizing the local image distortion. Extensive experiments conducted on seven publicly available image databases show that the performance of SWVFS is competitive with the state-of-the-art IQA algorithms.

Keywords

image quality assessment / gradient / texture / visual saliency

References

[1] Wang Z, Bovik A C. Modern image quality assessment. Synthesis Lectures on Image, Video, and Multimedia Processing, 2006, 2(1): 1−156

[2] Farnand S, Gaykema F. Special section guest editorial: image quality assessment. Journal of Electronic Imaging, 2010, 19(1): 1−2

[3] Lin W, Jay Kuo C C. Perceptual visual quality metrics: a survey. Journal of Visual Communication and Image Representation, 2011, 22(4): 297−312

[4] Damera-Venkata N, Kite T D, Geisler W S, Evans B L, Bovik A C. Image quality assessment based on a degradation model. IEEE Transactions on Image Processing, 2000, 9(4): 636−650

[5] Chandler D M, Hemami S S. VSNR: A wavelet-based visual signal-tonoise ratio for natural images. IEEE Transactions on Image Processing, 2007, 16(9): 2284−2298

[6] Sheikh H R, Bovik A C, 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

[7] Sheikh H R, Bovik A C. Image information and visual quality. IEEE Transactions on Image Processing, 2006, 15(2): 430−444

[8] Liu A, Lin W, Narwaria M. Image quality assessment based on gradient similarity. IEEE Transactions on Image Processing, 2012, 21(4): 1500−1512

[9] Zhang L, Zhang L, Mou X, Zhang D. FSIM: a feature similarity index for image quality assessment. IEEE Transactions on Image Processing, 2011, 20(8): 2378−2386

[10] Wang Z, Bovik A C, Sheikh H R, Simoncelli E P. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 2004, 13(4): 600−612

[11] Wang Z, Bovik A C, Sheikh H R, Simoncelli E P. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 2004, 13(4): 600−612

[12] Li C, Bovik A C. Content-partitioned structural similarity index for image quality assessment. Signal Processing: Image Communication, 2010, 25(7): 517−526

[13] Wang Z, Li Q. Information content weighting for perceptual image quality assessment. IEEE Transactions on Image Processing, 2011, 20(5): 1185−1198

[14] Cui L, Allen A R. An image quality metric based on corner, edge and symmetry maps. In: Proceedings of the 2008 British Machine Vision Conference. 2008, 1−10

[15] Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254−1259

[16] Liu H, Heynderickx I. Visual attention in objective image quality assessment: based on eye-tracking data. IEEE Transactions on Circuits and Systems for Video Technology, 2011, 21(7): 971−982

[17] You J, Perkis A, Hannuksela M M, Gabbouj M. Perceptual quality assessment based on visual attention analysis. In: Proceedings of the 17th ACM International Conference on Multimedia. 2009, 561−564

[18] Tong Y, Konik H, Cheikh F A, Trémeau A. Full reference image quality assessment based on saliency map analysis. Journal of Imaging Science and Technology, 2010, 54(3): 1−14

[19] Gu K, Zhai G, Yang X, Chen L, Zhang W. Nonlinear additive model based saliency map weighting strategy for image quality assessment. In: Proceedings of the IEEE 14th International Workshop on Multimedia Signal Processing. 2012, 313−318

[20] Roberts L G. Machine perception of three-dimensional solids. Technical Report, DTIC Document, 1963

[21] Manjunath B S, Ma W Y. Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18(8): 837−842

[22] Chandler D, Hemami S. A57 database, 2007

[23] Ninassi A, Le Callet P, Autrusseau F. Subjective quality assessment-IVC database

[24] Ponomarenko N, Lukin V, Zelensky A, Egiazarian K, Carli M, Battisti F. TID2008-A database for evaluation of full-reference visual quality assessment metrics. Advances of Modern Radioelectronics, 2009, 10(4): 30−45

[25] Horita Y, Shibata K, Kawayoke Y, Sazzad Z P. Mict image quality evaluation database, 2011

[26] Sheikh H R, Wang Z, Bovik A C, Cormack L. Image and video quality assessment research at live. 2003

[27] Larson E, Chandler D. Categorical image quality (CSIQ) database. 2010

[28] Engelke U, Kusuma T, Zepernick H. Wireless imaging quality (WIQ) database. 2010

[29] ITU-R Recommendation BT.500-13. Technical report, International Telecommunication Union, Geneva, Switzerland, 2002

[30] Subjective video quality assessment methods for multimedia applications. Technical Report, ITU-T recommendation P.910, 1999

[31] Tourancheau S, Le Callet P, Barba D. Image and video quality assessment using lCD: comparisons with CRT conditions. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2008, 91(6): 1383−1391

[32] Subjective assessment of standard definition digital television (SDTV) systems. Technical Report, ITU-R recommendation BT.1129-2, 1998

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag Berlin Heidelberg