A Perceptually Optimized Wavelet Embedded Zero Tree Image Coder
In this paper, we propose a Perceptually Optimized Embedded ZeroTree Image Coder (POEZIC) that introduces a perceptual weighting to wavelet transform coefficients prior to control SPIHT encoding algorithm in order to reach a targeted bit rate with a perceptual quality improvement with respect to the coding quality obtained using the SPIHT algorithm only. 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 POEZIC quality assessment. Our POEZIC 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) luminance masking and Contrast masking, 2) the contrast sensitivity function CSF to achieve the perceptual decomposition weighting, 3) the Wavelet Error Sensitivity WES used to reduce the perceptual quantization errors. 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] N. Jayant, J. Johnston, and R. Safranek, "Signal compression based on
models of human perception," Proc. IEEE, vol. 81, no. 10, pp. 1385-
1422, Oct. 1993.
[2] J. M. Shapiro, "Embedded image coding using zerotrees of wavelet
coefficients", IEEE Trans. Signal Processing, vol. 41, pp. 3445-3462,
1993.
[3] 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.
[4] A.Cohen, I.Daubechies, and J.C.Feauveau, "Biorthogonal bases of
compactly supported wavelets,"Commun. Pure Appl. Math., vol. 45, pp.
485-560, 1992.
[5] 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.
[6] J. Ross and H. D. Speed, "Contrast adaptation and contrast masking in
human vision," in Proc. Roy. Soc. Lond. B, 1991, pp. 61-69.
[7] G. E. Legge and J. M. Foley, "Contrast masking in human vision," J.
Opt. Soc. Amer. A, vol. 70, no. 12, pp. 1458-1471, 1980.
[8] D. G. Pelli, "Effects of Visual Noise," Ph.D. dissertation, Cambridge
Univ., Cambridge, U.K., 1981.
[9] R. J. Safranek, "A comparison of the coding efficiency of perceptual
models," Proc. SPIE, vol. 2411, pp. 83-91, 1995.
[10] 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.
[11] T. Pappas, T. Michel, and R. Hinds, "Supra-threshold perceptual image
coding," in IEEE Int. Conf. Image Processing, 1996, pp. 237-240.
[12] A. B. Watson, "DCT quantization matrices visually optimized for
individual images," in Proc. Hum. Vis., Visual Process., Digital Display
IV, 1993, pp. 202-216.
[13] J. M. Foley and G. M. Boynton, "A new model of human luminance
pattern vision mechanisms: analysis of the effects of pattern orientation,
spatial phase and temporal frequency," in Comput. Vis. Based
Neurobiol., vol. 2054, 1994, pp. 32-42.
[14] 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.
[15] 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,
January 2000.
[16] I. Honsch and L. J. Karam, "Adaptive image coding with perceptual
distortion control," IEEE Trans. Image Process., vol. 11, no. 3, pp. 213-
222, Mar. 2002.
[17] Marcus J. Nadenau, Julien Reichel, and Murat Kunt,`` Wavelet-based
Color Image Compression: Exploiting the Contrast Sensitivity
Function`` 2000.
[18] A. B.Watson, "Probability summation over time," Vis. Res., vol. 19, pp.
515-522, 1979.
[19] 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.
[20] P. C. Teo and D. J. Heeger, "Perceptual image distortion," in Proc. IEEE
Int. Conf. Image Processing, 1994, pp. 982-986.
[21] 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.
[22] 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
[23] 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.
[24] 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.
[25] Andrew P. Bradley "A Wavelet Visible Difference Predictor" Member
IEEE Transactions on Image Processing Vol. 8. No. 5. May 1999.
[1] N. Jayant, J. Johnston, and R. Safranek, "Signal compression based on
models of human perception," Proc. IEEE, vol. 81, no. 10, pp. 1385-
1422, Oct. 1993.
[2] J. M. Shapiro, "Embedded image coding using zerotrees of wavelet
coefficients", IEEE Trans. Signal Processing, vol. 41, pp. 3445-3462,
1993.
[3] 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.
[4] A.Cohen, I.Daubechies, and J.C.Feauveau, "Biorthogonal bases of
compactly supported wavelets,"Commun. Pure Appl. Math., vol. 45, pp.
485-560, 1992.
[5] 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.
[6] J. Ross and H. D. Speed, "Contrast adaptation and contrast masking in
human vision," in Proc. Roy. Soc. Lond. B, 1991, pp. 61-69.
[7] G. E. Legge and J. M. Foley, "Contrast masking in human vision," J.
Opt. Soc. Amer. A, vol. 70, no. 12, pp. 1458-1471, 1980.
[8] D. G. Pelli, "Effects of Visual Noise," Ph.D. dissertation, Cambridge
Univ., Cambridge, U.K., 1981.
[9] R. J. Safranek, "A comparison of the coding efficiency of perceptual
models," Proc. SPIE, vol. 2411, pp. 83-91, 1995.
[10] 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.
[11] T. Pappas, T. Michel, and R. Hinds, "Supra-threshold perceptual image
coding," in IEEE Int. Conf. Image Processing, 1996, pp. 237-240.
[12] A. B. Watson, "DCT quantization matrices visually optimized for
individual images," in Proc. Hum. Vis., Visual Process., Digital Display
IV, 1993, pp. 202-216.
[13] J. M. Foley and G. M. Boynton, "A new model of human luminance
pattern vision mechanisms: analysis of the effects of pattern orientation,
spatial phase and temporal frequency," in Comput. Vis. Based
Neurobiol., vol. 2054, 1994, pp. 32-42.
[14] 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.
[15] 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,
January 2000.
[16] I. Honsch and L. J. Karam, "Adaptive image coding with perceptual
distortion control," IEEE Trans. Image Process., vol. 11, no. 3, pp. 213-
222, Mar. 2002.
[17] Marcus J. Nadenau, Julien Reichel, and Murat Kunt,`` Wavelet-based
Color Image Compression: Exploiting the Contrast Sensitivity
Function`` 2000.
[18] A. B.Watson, "Probability summation over time," Vis. Res., vol. 19, pp.
515-522, 1979.
[19] 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.
[20] P. C. Teo and D. J. Heeger, "Perceptual image distortion," in Proc. IEEE
Int. Conf. Image Processing, 1994, pp. 982-986.
[21] 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.
[22] 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
[23] 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.
[24] 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.
[25] Andrew P. Bradley "A Wavelet Visible Difference Predictor" Member
IEEE Transactions on Image Processing Vol. 8. No. 5. May 1999.
@article{"International Journal of Information, Control and Computer Sciences:49910", author = "A. Bajit and M. Nahid and A. Tamtaoui and E. H. Bouyakhf", title = "A Perceptually Optimized Wavelet Embedded Zero Tree Image Coder", abstract = "In this paper, we propose a Perceptually Optimized Embedded ZeroTree Image Coder (POEZIC) that introduces a perceptual weighting to wavelet transform coefficients prior to control SPIHT encoding algorithm in order to reach a targeted bit rate with a perceptual quality improvement with respect to the coding quality obtained using the SPIHT algorithm only. 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 POEZIC quality assessment. Our POEZIC 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) luminance masking and Contrast masking, 2) the contrast sensitivity function CSF to achieve the perceptual decomposition weighting, 3) the Wavelet Error Sensitivity WES used to reduce the perceptual quantization errors. 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, 9/7 Wavelets Error Sensitivity WES, CSF implementation approaches, JND Just Noticeable Difference, Luminance masking, Contrast masking, standard SPIHT, Objective Quality Measure, Probability Score PS.", volume = "1", number = "6", pages = "1531-6", }
