Multiscale_Halftoning - IEEE TRANSACTIONS ON IMAGE...

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IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 6, NO. 3, MARCH 1997 483 240 240 portion of the luminance (Y) component of the SVD- filtered frame no. 75 (first field), with a 12. (Magnified by a factor of two). This figure is almost indistinguishable from the original. For comparison, it also shows the normalized (from zero to 255) error between the original frame and the output of the SVD-based filter and the normalized error between the original frame and the output of the 3 3 median filter. As shown in Fig. 6, the median filter extracts both image information and noise, thus causing image blurring. Hence, such a filter is not suitable for our application, which requires near lossless reproduction. In contrast, the SVD filter preserves edge details and overall picture fidelity. IV. CONCLUSIONS We presented a novel noise estimation and filtering algorithm for still images and video sequences based on the theories of SVD and data compression. Our experiments show that the technique can effectively filter noisy images with no prior knowledge of either the image or the noise characteristics. This results in increased compressibility when the filtered data is subsequently processed by image and video compression schemes, such as JPEG and MPEG. For still images, comparisons with other filtering schemes, such as But- terworth filtering and wavelet-based filtering show that SVD-based filtering is better in preserving edge details. For video sequences, experiments have shown a 16% improvement in the compression ratio achieved by nearly lossless MPEG or, equivalently, a visual quality improvement of 1 dB at the same rate. This scheme can be used in conjunction with traditional motion-compensated temporal filtering techniques to further improve the overall performance of a high-quality video processing system [11]. ACKNOWLEDGMENT The authors thank V. Bhaskaran and C. Herley for discussions, comments, and suggestions. REFERENCES [1] A. Rosenfeld and A. C. Kak, Digital Picture Processing, 2nd ed. New York: Academic, 1982. [2] M. K. Ozkan, A. T. Erdem, M. I. Sezan, and A. M. Tekalp, “Efficient multiframe Wiener restoration of blurred and noisy image sequences,” IEEE Trans. Image Processing, vol. 1, pp. 453–476, Oct. 1992. [3] H. C. Andrews and C. L. Patterson, “Singular value decompositions and digital image processing,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-24, pp. 26–53, Feb. 1976. [4] H.-C. Lee et al., “Digital image noise suppression method using SVD block transform,” U.S. Patent 5 010 504, Apr. 1991. [5] B. K. Natarajan, “Filtering random noise from deterministic signals via data compression,” IEEE Trans. Signal Processing, vol. 43, pp. 2595–2605, Nov. 1995. [6] D. L. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Inform. Theory, vol. 41, pp. 613–627, May 1995. [7] G. H. Golub and C. F. Van Loan, Matrix Computations. Baltimore, MD: John Hopkins Univ. Press, 1983.
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Multiscale_Halftoning - IEEE TRANSACTIONS ON IMAGE...

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