The week's pick
Random Articles
Reseach Article
A New Quality Metric based on FFT Transform
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 40 - Number 2 |
| Year of Publication: 2012 |
| Authors: Mohamed Ben Amor, Nouri Masmoudi Fahmi Kammoun |
10.5120/4932-7165
|
Mohamed Ben Amor, Nouri Masmoudi Fahmi Kammoun . A New Quality Metric based on FFT Transform. International Journal of Computer Applications. 40, 2 ( February 2012), 41-46. DOI=10.5120/4932-7165
Abstract
The study of the human visual system (HVS) is very interesting to quantify the quality of an image or to predict perceived information. The contrast sensitivity function (CSF) is one of the main ways to incorporate the HVS properties in an imaging system. It characterizes its sensitivity to spatial and temporal frequencies. In this paper we are interested in establishing a metric with full reference to the image and video. We realize in our algorithm, the FFT transformation to apply the CSF function. Our method is applicable to any size of image and video sequence by increasing its size at powers of two. This increase is achieved by adding "mirror image". The experimental results show that our method keeps better the different frequency components. She is more efficient than the method of "zero padding" and returns results very close to those of the DFT transformation.
References
- Z.Wang , A.C.Bovik, H.R. Seikh, and E.P.Simoncelli, « Image quality assessment : from visibility to structural similarity», In IEEE Transactions on Image Processing, volume 13, 2004.
- D.M. Chandler and S.S. Hemami, « VSNR: A wavelet-based visual signal-to-noise ratio for natural images», in IEEE Transactions on Image Processing, 16(9): pages 2284–2298, 2007, doi :10.1109/TIP.2007.901820. 126, 145.
- J. Mannos and D. Sakrison, « The effects of a visual fidelity criterion on the encoding of images », In IEEE Transactions on Information Theory, volume IT-20, p.525-536, 1974.
- C. F. Hall and F. Hall, « A Nonlinear Model for the Spatial Characteristics of the Human Visual System », IEEE Transaction Systems, Man and Cybernetics, vol. SMC-7, No. 3, p. 161-170, 1977.
- J. O. Limb, « Distortion Criteria of the Human Viewer » , IEEE Transaction on Systems, Man and Cybernetics, vol. SMC-9, No. 12, p. 778-793, 1979.
- S. Daly, « the Visible Differences Predictor: an Algorithm for the Assessment of Image Fidelity », in Digital Images and Human Vision, edited by A. B. Watson, MIT press; p. 197-206. 1993.
- S. A. Karunasekera and N. G. Kingsbury, «A Distortion Measure for Coding Artifacts in Images: Implementation Aspects, Internal Report», Signal Processing Group, Department of Engineering, University of Cambridge, 1995.
- P. Le callet, «Critères objectifs avec référence de qualité visuelle des images couleurs», PhD thesis, Polytechnic University of Nantes, 2001. 117, 140.
- N.B. Nill, «A Visual Model Weighted Cosine Transform for Image Compression and Quality Assessment», IEEE Transactions on communications, Vol. COM-33, No. 6, pp. 551-556, 1985.
- K. Ngan, K. Rao, and H. Singh; Cosine transform coding incorporating human visual system model; Presented at SPIE fiber' 86, 1986.
- ITU-T Rec. H.264 / ISO/IEC 11496-10, «Advanced Video Coding», Final Committee Draft, Document JVTF100, December 2002.
- K. Veeraswamy, S. Srinivaskumar, B.N. Chatterji, «Designing Quantization Table for Hadamard Transform based on Human Visual System for Image Compression», ICGST-GVIP Journal, Volume 7, Issue 3, November 2007.
- M. Ben Amor, A. Samet, F. Kammoun, N. Masmoudi, «exploitation des caractéristiques du système visuel humain dans les métriques de qualité», Cinquième workshop AMINA 2010, pp 123-130.2010
- V. Rosselli, M.C. Larabi, C. Fernandez-Maloigne, «Métrique de différence couleur basée sur le seuil de perception» on COROSA COmpression et REprésentation des Signaux Audiovisuels, Montpellier, 8-9 novembre 2007.
- A. Stoica, M.C. Larabi, C. Fernandez Maloigne, «Amélioration de la qualité visuelle d’images couleur dans le cadre du standard de compression JPEG2000», traitement du signal 2004_volume 21_numéro spécial L'image numérique couleur (2004)
- A. V. Oppenheim and R. W. Schafer, Discrete-Time Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1998.
- M. Chen and G. Bailey « Image Quality Assessment Using Data Hiding for Performance Evaluation of Visual Communication Networks», fourth international working conference performance modelling and evaluation of heterogeneous networks HET-NETs, p. 1-6 September 2006.
- Z. Wang and E.P. Simoncelli, «Reduced-reference image quality assessment using a wavelet-domain natural image statistic model», Human Vision and Electronic Imaging X, 5666, Jan. 2005.
- Y. Horita, SH. Arata and T. Murai « No-reference image quality assessment for jpeg/jpeg2000 coding », XII European Signal processing conference EUSIPCO Vienne Austria, p. 1301-1304 September 2004.
- M.G. Choi, J.H. Jung and J.W. Jean « No-Reference Image Quality Assessment using Blur and Noise », proceedings of world academy of science, engineering and technology volume 38 february 2009 issn:2070-3740.
Index Terms
Keywords