Análise de Métricas de Similaridade Estrutural
Resumo
A abordagem que avalia a qualidade da imagem por meio de verificação de similaridade estrutural é baseada na suposição de que o sistema visual humano (HVS) é altamente adaptado para extrair informação estrutural de uma cena e, portanto, métricas de similaridade estrutural podem fornecer uma boa aproximação da qualidade da imagem percebida. O objetivo deste artigo é demonstrar o uso de três métricas (SSIM, MS-SSIM e CW-SSIM) que utilizam desta abordagem.Referências
Girod, B. (1993) “What’s wrong with mean-squared error,” in Digital Images and Human Vision, A. B. Watson, Ed. Cambridge, MA: MIT Press, pp. 207–220.
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.
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.
Publicado
23/07/2013
Como Citar
ARAUJO, Jean N. R.; MONTEIRO, Claudio de Castro.
Análise de Métricas de Similaridade Estrutural. In: ENCONTRO NACIONAL DE COMPUTAÇÃO DOS INSTITUTOS FEDERAIS (ENCOMPIF), 1. , 2013, Maceió.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2013
.
p. 2102-2105.
ISSN 2763-8766.
