Análise de Métricas de Similaridade Estrutural
Abstract
The approach which evaluates the image quality by checking structural similarity is based on the assumption that the HVS is highly adapted for extracting structural information of a scene and hence structural similarity metrics can provide a good approximation of the image quality perceived. The purpose of this article is to demonstrate the use of three metrics (SSIM, MS-SSIM and CW-SSIM) using this approach.References
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Wang, Z. and Bovik, A. C. (2002) “A universal image quality index,” IEEE Signal Processing Letters, vol. 9, pp. 81–84.
Wang, Z., Bovik, A. C. and Lu, L. (2002) “Why is image quality assessment so difficult,” in Proc. IEEE Conf. Acoustics, Speech, and Signal Processing, vol. 4, pp. 3313–3316.
Wang, Z., Sheikh, H. R. and Bovik, A. C. (2003) “Objective video quality assessment,” in The Handbook of Video Databases: Design and Applications (B. Furht and O. Marques, eds.), pp. 1041–1078, CRC Press, Sept. 2003.
Wang, Z., Bovik, A. C., Sheikh, H. R. and Simoncelli, E. P. (2004) “Image quality assessment: From error measurement to structural similarity,” IEEE Trans. Image Processing, vol. 13.
Wang, Z. and Bovik, A. C. (2004) “Video quality assessment based on structural distortion measurement,” Signal Processing: Image Communication, special issue on objective video quality metrics, vol. 19.
Wang, Z., Bovik, A. C. and Simoncelli, E. P. (2003) “Multi-scale Structural Similarity for Image Quality Assessment,” 37th IEEE Asilomar Conference on Signals, Systems and Computers.
Wang, Z. and Simoncelli, E. P. (2005) “Translation insensitive image similarity in complex wavelet domain,” in Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing, Philadelphia, PA.
Teo, P. C. and Heeger, D. J. (1994) “Perceptual image distortion,” in Proc. SPIE, vol. 2179, pp. 127–141.
Wang, Z. (2001) “Rate scalable Foveated image and video communications,” Ph.D. dissertation, Dept. Elect. Comput. Eng., Univ. Texas at Austin, Austin, TX.
Wang, Z. and Bovik, A. C. (2002) “A universal image quality index,” IEEE Signal Processing Letters, vol. 9, pp. 81–84.
Wang, Z., Bovik, A. C. and Lu, L. (2002) “Why is image quality assessment so difficult,” in Proc. IEEE Conf. Acoustics, Speech, and Signal Processing, vol. 4, pp. 3313–3316.
Wang, Z., Sheikh, H. R. and Bovik, A. C. (2003) “Objective video quality assessment,” in The Handbook of Video Databases: Design and Applications (B. Furht and O. Marques, eds.), pp. 1041–1078, CRC Press, Sept. 2003.
Wang, Z., Bovik, A. C., Sheikh, H. R. and Simoncelli, E. P. (2004) “Image quality assessment: From error measurement to structural similarity,” IEEE Trans. Image Processing, vol. 13.
Wang, Z. and Bovik, A. C. (2004) “Video quality assessment based on structural distortion measurement,” Signal Processing: Image Communication, special issue on objective video quality metrics, vol. 19.
Wang, Z., Bovik, A. C. and Simoncelli, E. P. (2003) “Multi-scale Structural Similarity for Image Quality Assessment,” 37th IEEE Asilomar Conference on Signals, Systems and Computers.
Wang, Z. and Simoncelli, E. P. (2005) “Translation insensitive image similarity in complex wavelet domain,” in Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing, Philadelphia, PA.
Published
2013-07-23
How to Cite
ARAUJO, Jean N. R.; MONTEIRO, Claudio de Castro.
Análise de Métricas de Similaridade Estrutural. In: NATIONAL COMPUTING MEETING OF FEDERAL INSTITUTES (ENCOMPIF), 1. , 2013, Maceió.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2013
.
p. 2102-2105.
ISSN 2763-8766.
