Postprocessing of images by filtering the unmasked coding noise

This paper presents a methodology for the restoration of the visual quality of still images affected by coding noise. This quality restoration is achieved only by considering the additive coding noise and is therefore limited to an adaptive postprocessing filtering. It is based on a model of the human visual system that considers the relationship between visual stimuli and their visibility. This phenomenon known as masking is used as a criterion for the locally adaptive filtering design. An image transformation that yields visual stimuli tuned to the frequency and orientation according to the perceptual model is proposed. It allows a local measure of the masking of each perceptual stimulus considering the contrast between signal and estimated noise. This measure is obtained by analytic filtering. Processing schemes are presented with applications to the discrete cosine transform (DCT) and subband coded images. One proposed solution considers the characteristics of DCT coding noise for the estimation of the noise. Another solution is based on a "blind" neural estimation of the noise characteristics. Experimental results of the proposed approaches show significant improvements of the visual quality, which validates our perceptual model and filtering.

[1]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[2]  M. Ibrahim Sezan,et al.  Survey of recent developments in digital image restoration. , 1990 .

[3]  Benoît Macq,et al.  Human visual quality criterion , 1990, Other Conferences.

[4]  Wan-Chi Siu,et al.  A practical postprocessing technique for real-time block-based coding system , 1998, IEEE Trans. Circuits Syst. Video Technol..

[5]  Uday B. Desai,et al.  Image restoration using a multilayer perceptron with a multilevel sigmoidal function , 1993, IEEE Trans. Signal Process..

[6]  K. R. Rao,et al.  Orthogonal Transforms for Digital Signal Processing , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  Joan L. Mitchell,et al.  JPEG: Still Image Data Compression Standard , 1992 .

[8]  H. W. Park,et al.  Blocking effect reduction of JPEG images by signal adaptive filtering , 1998, IEEE Trans. Image Process..

[9]  Jelena Kovacevic,et al.  Nonseparable multidimensional perfect reconstruction filter banks and wavelet bases for Rn , 1992, IEEE Trans. Inf. Theory.

[10]  Mark J. T. Smith,et al.  A filter bank for the directional decomposition of images: theory and design , 1992, IEEE Trans. Signal Process..

[11]  Jean-Pierre Antoine,et al.  Image analysis with two-dimensional continuous wavelet transform , 1993, Signal Process..

[12]  Daniel A. Pollen,et al.  Visual cortical neurons as localized spatial frequency filters , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[13]  Luc Vandendorpe,et al.  Optimum quality and progressive resolution of video signals , 1990 .

[14]  Nikolas P. Galatsanos,et al.  Projection-based spatially adaptive reconstruction of block-transform compressed images , 1995, IEEE Trans. Image Process..

[15]  Huifang Sun,et al.  Concealment of damaged block transform coded images using projections onto convex sets , 1995, IEEE Trans. Image Process..

[16]  D H Hubel,et al.  Brain mechanisms of vision. , 1979, Scientific American.

[17]  Nikolas P. Galatsanos,et al.  Regularized reconstruction to reduce blocking artifacts of block discrete cosine transform compressed images , 1993, IEEE Trans. Circuits Syst. Video Technol..

[18]  J. Daugman.  Two-dimensional spectral analysis of cortical receptive field profiles , 1980, Vision Research.

[19]  Jae Lim,et al.  Reduction Of Blocking Effects In Image Coding , 1984 .

[20]  Bhaskar Ramamurthi,et al.  Nonlinear space-variant postprocessing of block coded images , 1986, IEEE Trans. Acoust. Speech Signal Process..

[21]  Gary Mastin,et al.  Adaptive filters for digital image noise smoothing: An evaluation , 1985, Comput. Vis. Graph. Image Process..

[22]  Yehoshua Y. Zeevi,et al.  Reorganization and diversification of signals in vision , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[23]  Benoit M. Macq,et al.  Image visual quality restoration by cancellation of the unmasked noise , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[24]  Benoit M. Macq,et al.  Image quality criterion based on the cancellation of the masked noise , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[25]  Robert L. Stevenson,et al.  Improved image decompression for reduced transform coding artifacts , 1994, Electronic Imaging.

[26]  J. M. Foley,et al.  Contrast masking in human vision. , 1980, Journal of the Optical Society of America.

[27]  Nikolas P. Galatsanos,et al.  Iterative projection algorithms for removing the blocking artifacts of block-DCT compressed images , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[28]  Aggelos K. Katsaggelos,et al.  Image and video compression algorithms based on recovery techniques using mean field annealing , 1995, Proc. IEEE.

[29]  Dirk Roose,et al.  Wavelet-based image denoising using a Markov random field a priori model , 1997, IEEE Trans. Image Process..

[30]  Michael T. Orchard,et al.  A deblocking algorithm for JPEG compressed images using overcomplete wavelet representations , 1997, IEEE Trans. Circuits Syst. Video Technol..

[31]  H. Wilson,et al.  Spatial frequency tuning of orientation selective units estimated by oblique masking , 1983, Vision Research.

[32]  A. Murat Tekalp,et al.  New approaches for space-invariant image restoration , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[33]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[34]  Jae S. Lim,et al.  Reduction of blocking effect in image coding , 1983, ICASSP.

[35]  Bruno O. Shubert,et al.  Random variables and stochastic processes , 1979 .

[36]  Avideh Zakhor,et al.  Iterative procedures for reduction of blocking effects in transform image coding , 1991, Electronic Imaging.

[37]  Robert L. Stevenson,et al.  Reduction of coding artifacts in transform image coding , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[38]  Michael Elad,et al.  Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images , 1997, IEEE Trans. Image Process..

[39]  Avideh Zakhor.  Iterative procedures for reduction of blocking effects in transform image coding , 1992, IEEE Trans. Circuits Syst. Video Technol..

[40]  W. Kwok,et al.  Multi-directional interpolation for spatial error concealment , 1993 .

[41]  A B Watson,et al.  Efficiency of a model human image code. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[42]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .