A Perceptually Optimized Foveation Based Wavelet Embedded Zero Tree Image Coding
In this paper, we propose a Perceptually Optimized Foveation based Embedded ZeroTree Image Coder (POEFIC) that introduces a perceptual weighting to wavelet coefficients prior to control SPIHT encoding algorithm in order to reach a targeted bit rate with a perceptual quality improvement with respect to a given bit rate a fixation point which determines the region of interest ROI. The paper also, introduces a new objective quality metric based on a Psychovisual model that integrates the properties of the HVS that plays an important role in our POEFIC quality assessment. Our POEFIC coder is based on a vision model that incorporates various masking effects of human visual system HVS perception. Thus, our coder weights the wavelet coefficients based on that model and attempts to increase the perceptual quality for a given bit rate and observation distance. The perceptual weights for all wavelet subbands are computed based on 1) foveation masking to remove or reduce considerable high frequencies from peripheral regions 2) luminance and Contrast masking, 3) the contrast sensitivity function CSF to achieve the perceptual decomposition weighting. The new perceptually optimized codec has the same complexity as the original SPIHT techniques. However, the experiments results show that our coder demonstrates very good performance in terms of quality measurement.
[1] P. Kortum and W. S. Geisler, "Implementation of a foveated image
coding system for image bandwidth reduction," in Proc. SPIE: Human
Vision and Electronic Imaging, vol. 2657, 1996, pp. 350-360.
[2] N. Tsumura, C. Endo, H. Haneishi, and Y. Miyake, "Image compression
and decomposition based on gazing area," in Proc. SPIE, vol. 2657,
1996, pp. 361-367.
[3] T. Kuyel, W. Geisler, and J. Ghosh, "Retinally reconstructed images:
digital images having a resolution match with the human eyes," IEEE
Trans. Syst, Man, Cybern. A, vol. 29, pp. 235-243, Mar. 1999.
[4] W. S. Geisler and J. S. Perry, "A real-time foveated multiresolution
system for low-bandwidth video communication," Proc. SPIE, vol.
3299, 1998.
[5] S. Lee, M. S. Pattichis, and A. C. Bovik, "Foveated video quality
assessment," IEEE Trans. Multimedia, vol. 3, 2001.
[6] "Foveated video compression with optimal rate control," IEEE Trans.
Image Processing, vol. 10, pp. 977-992, July 2001.
[7] P. J. Burt and E. H. Adelson, "The Laplacian algorithm as a compact
image code," IEEE Trans. Commun., vol. C-31, pp. 532-540, Apr. 1983.
[8] E.-C. Chang and C. Yap, "A wavelet approach to foveating images,"
inProc. 13th ACM Symp. Computational Geometry, 1997, pp. 397-399.
[9] E.-C. Chang, "Foveation techniques and scheduling issues in
thinwirevisualization," Ph.D. dissertation, New York Univ., 1998.
[10] E.-C. Chang, S. Mallat, and C. Yap. (1999)Wavelet Foveation. (Online).
Available: http://www.cs.nyu.edu/visual/
[11] S. Daly, W. Zeng, J. Li, S. Lei, Visual masking in wavelet compression
for JPEG 2000, in: Proceedings of IS&T/SPIE Conference on Image and
Video Communications and Processing, San Jose, CA, Vol. 3974, 2000
[12] Zhou Wang, Alan Conrad Bovik,``Embedded Foveation Image
Coding,`` IEEE Transactions on image processing, Vol. 10, no. 10,
october 2001.
[13] S. G. Mallat, "Multifrequency channel decomposition of images and
wavelet models," IEEE Trans. Acoust., Speech, Signal Processing, vol.
37, pp. 2091-2110, 1989.
[14] M. Antonini, M. Barlaud, P. Mathieu, and I. Daubechies, "Image coding
using the wavelet transform," IEEE Trans. Image Processing, vol. 1,
pp.205-220, 1992.
[15] J. Ross and H. D. Speed, "Contrast adaptation and contrast masking in
human vision," in Proc. Roy. Soc. Lond. B, 1991, pp. 61-69.
[16] Watson and J. A. Solomon, "A model of visual contrast gain control and
pattern masking," J. Opt. Soc. Amer., vol. 14, pp. 2397-2391, 1997.
[17] J. M. Foley, "Human luminance pattern-vision mechanisms: masking
experiments require a new model," J. Compar. Neurol., vol. 11, no. 6,
pp. 1710-1719, 1994.
[18] Marcus J. Nadenau, Julien Reichel, and Murat Kunt,`` Wavelet-based
Color Image Compression: Exploiting Contrast Sensitivity
Function``2000.
[19] A.Cohen, I.Daubechies, and J.C.Feauveau, "Biorthogonal bases of
compactly supported wavelets,"Commun. Pure Appl. Math., vol. 45, pp.
485-560, 1992.
[20] A.B Watson, G.Y.Yang, J.A.Solomon, and J.Villasonor,``Visisbility Of
Wavelet Quantization Noise,`` IEEE Trans. Image Processing, vol.6 no,
8, pp. 1164-1175 1997.
[21] Michael Unser, and Thierry Blu, ``Mathematical Propertiesof JPEG2000
Wavelet Filters, `` IEEE Transactions on Image Processing, Vol. 12,
NO. 9, September 2003
[22] J. M. Shapiro, "Embedded image coding using zerotrees of wavelet
coefficients", IEEE Trans. Signal Processing, vol. 41, pp. 3445-3462,
1993.
[23] A. Said and W. A. Pearlman, "A new, fast and efficient image codec
based on set partitioning in hierarchical trees", IEEE Trans. Circuits and
Systems for video Technology, vol. 6, pp. 243-250, June 1996.
[24] S. J. P. Westen, R. L. Lagendijk and J. Biemond, "Perceptual Image
Quality based on a Multiple Channel HVS Model," Proceedings of
ICASSP, pp. 2351-2354, 1995
[25] C. Zetzsche and G. Hauske, "Multiple Channel Model Prediction of
Subjective Image Quality," SPIE, Human Vision, Visual Processing, and
Display, 1077, pp. 209-215, 1989.
[26] S. Daly, "The visible differences predictor: An algorithm for the
assessment of image fidelity," in Digital Images and Human Vision (A.
B. Watson, ed.), pp. 179-205, Cambridge, MA: MIT Press, 1993W.-K.
Chen, Linear Networks and Systems (Book style). Belmont, CA:
Wadsworth, 1993, pp. 123-135.
[27] Andrew P. Bradley "A Wavelet Visible Difference Predictor" Member
IEEE Transactions on Image Processing Vol. 8. No. 5. May 1999.
[28] A. B.Watson, "Probability summation over time," Vis. Res., vol. 19, pp.
515-522, 1979.
[29] J. G. Robson and N. Graham, "Probability summation and regional
variation in contrast sensitivity across the visual field," Vis. Res., vol. 21,
pp. 409-418, 1981.
[30] W. S. Geisler, "Visual detection following retinal damage: predictions of
an inhomogeneous retino-cortical model," Proc. SPIE, vol. 2674, pp.
119-130, 1996.
[1] P. Kortum and W. S. Geisler, "Implementation of a foveated image
coding system for image bandwidth reduction," in Proc. SPIE: Human
Vision and Electronic Imaging, vol. 2657, 1996, pp. 350-360.
[2] N. Tsumura, C. Endo, H. Haneishi, and Y. Miyake, "Image compression
and decomposition based on gazing area," in Proc. SPIE, vol. 2657,
1996, pp. 361-367.
[3] T. Kuyel, W. Geisler, and J. Ghosh, "Retinally reconstructed images:
digital images having a resolution match with the human eyes," IEEE
Trans. Syst, Man, Cybern. A, vol. 29, pp. 235-243, Mar. 1999.
[4] W. S. Geisler and J. S. Perry, "A real-time foveated multiresolution
system for low-bandwidth video communication," Proc. SPIE, vol.
3299, 1998.
[5] S. Lee, M. S. Pattichis, and A. C. Bovik, "Foveated video quality
assessment," IEEE Trans. Multimedia, vol. 3, 2001.
[6] "Foveated video compression with optimal rate control," IEEE Trans.
Image Processing, vol. 10, pp. 977-992, July 2001.
[7] P. J. Burt and E. H. Adelson, "The Laplacian algorithm as a compact
image code," IEEE Trans. Commun., vol. C-31, pp. 532-540, Apr. 1983.
[8] E.-C. Chang and C. Yap, "A wavelet approach to foveating images,"
inProc. 13th ACM Symp. Computational Geometry, 1997, pp. 397-399.
[9] E.-C. Chang, "Foveation techniques and scheduling issues in
thinwirevisualization," Ph.D. dissertation, New York Univ., 1998.
[10] E.-C. Chang, S. Mallat, and C. Yap. (1999)Wavelet Foveation. (Online).
Available: http://www.cs.nyu.edu/visual/
[11] S. Daly, W. Zeng, J. Li, S. Lei, Visual masking in wavelet compression
for JPEG 2000, in: Proceedings of IS&T/SPIE Conference on Image and
Video Communications and Processing, San Jose, CA, Vol. 3974, 2000
[12] Zhou Wang, Alan Conrad Bovik,``Embedded Foveation Image
Coding,`` IEEE Transactions on image processing, Vol. 10, no. 10,
october 2001.
[13] S. G. Mallat, "Multifrequency channel decomposition of images and
wavelet models," IEEE Trans. Acoust., Speech, Signal Processing, vol.
37, pp. 2091-2110, 1989.
[14] M. Antonini, M. Barlaud, P. Mathieu, and I. Daubechies, "Image coding
using the wavelet transform," IEEE Trans. Image Processing, vol. 1,
pp.205-220, 1992.
[15] J. Ross and H. D. Speed, "Contrast adaptation and contrast masking in
human vision," in Proc. Roy. Soc. Lond. B, 1991, pp. 61-69.
[16] Watson and J. A. Solomon, "A model of visual contrast gain control and
pattern masking," J. Opt. Soc. Amer., vol. 14, pp. 2397-2391, 1997.
[17] J. M. Foley, "Human luminance pattern-vision mechanisms: masking
experiments require a new model," J. Compar. Neurol., vol. 11, no. 6,
pp. 1710-1719, 1994.
[18] Marcus J. Nadenau, Julien Reichel, and Murat Kunt,`` Wavelet-based
Color Image Compression: Exploiting Contrast Sensitivity
Function``2000.
[19] A.Cohen, I.Daubechies, and J.C.Feauveau, "Biorthogonal bases of
compactly supported wavelets,"Commun. Pure Appl. Math., vol. 45, pp.
485-560, 1992.
[20] A.B Watson, G.Y.Yang, J.A.Solomon, and J.Villasonor,``Visisbility Of
Wavelet Quantization Noise,`` IEEE Trans. Image Processing, vol.6 no,
8, pp. 1164-1175 1997.
[21] Michael Unser, and Thierry Blu, ``Mathematical Propertiesof JPEG2000
Wavelet Filters, `` IEEE Transactions on Image Processing, Vol. 12,
NO. 9, September 2003
[22] J. M. Shapiro, "Embedded image coding using zerotrees of wavelet
coefficients", IEEE Trans. Signal Processing, vol. 41, pp. 3445-3462,
1993.
[23] A. Said and W. A. Pearlman, "A new, fast and efficient image codec
based on set partitioning in hierarchical trees", IEEE Trans. Circuits and
Systems for video Technology, vol. 6, pp. 243-250, June 1996.
[24] S. J. P. Westen, R. L. Lagendijk and J. Biemond, "Perceptual Image
Quality based on a Multiple Channel HVS Model," Proceedings of
ICASSP, pp. 2351-2354, 1995
[25] C. Zetzsche and G. Hauske, "Multiple Channel Model Prediction of
Subjective Image Quality," SPIE, Human Vision, Visual Processing, and
Display, 1077, pp. 209-215, 1989.
[26] S. Daly, "The visible differences predictor: An algorithm for the
assessment of image fidelity," in Digital Images and Human Vision (A.
B. Watson, ed.), pp. 179-205, Cambridge, MA: MIT Press, 1993W.-K.
Chen, Linear Networks and Systems (Book style). Belmont, CA:
Wadsworth, 1993, pp. 123-135.
[27] Andrew P. Bradley "A Wavelet Visible Difference Predictor" Member
IEEE Transactions on Image Processing Vol. 8. No. 5. May 1999.
[28] A. B.Watson, "Probability summation over time," Vis. Res., vol. 19, pp.
515-522, 1979.
[29] J. G. Robson and N. Graham, "Probability summation and regional
variation in contrast sensitivity across the visual field," Vis. Res., vol. 21,
pp. 409-418, 1981.
[30] W. S. Geisler, "Visual detection following retinal damage: predictions of
an inhomogeneous retino-cortical model," Proc. SPIE, vol. 2674, pp.
119-130, 1996.
@article{"International Journal of Information, Control and Computer Sciences:54007", author = "A. Bajit and M. Nahid and A. Tamtaoui and E. H. Bouyakhf", title = "A Perceptually Optimized Foveation Based Wavelet Embedded Zero Tree Image Coding", abstract = "In this paper, we propose a Perceptually Optimized Foveation based Embedded ZeroTree Image Coder (POEFIC) that introduces a perceptual weighting to wavelet coefficients prior to control SPIHT encoding algorithm in order to reach a targeted bit rate with a perceptual quality improvement with respect to a given bit rate a fixation point which determines the region of interest ROI. The paper also, introduces a new objective quality metric based on a Psychovisual model that integrates the properties of the HVS that plays an important role in our POEFIC quality assessment. Our POEFIC coder is based on a vision model that incorporates various masking effects of human visual system HVS perception. Thus, our coder weights the wavelet coefficients based on that model and attempts to increase the perceptual quality for a given bit rate and observation distance. The perceptual weights for all wavelet subbands are computed based on 1) foveation masking to remove or reduce considerable high frequencies from peripheral regions 2) luminance and Contrast masking, 3) the contrast sensitivity function CSF to achieve the perceptual decomposition weighting. The new perceptually optimized codec has the same complexity as the original SPIHT techniques. However, the experiments results show that our coder demonstrates very good performance in terms of quality measurement.
", keywords = "DWT, linear-phase 9/7 filter, Foveation Filtering, CSF implementation approaches, 9/7 Wavelet JND Thresholds and Wavelet Error Sensitivity WES, Luminance and Contrast masking, standard SPIHT, Objective Quality Measure, Probability Score PS.", volume = "1", number = "8", pages = "2407-6", }
