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A postprocessing technique for block effect elimination using a perceptual distortion measure | IEEE Conference Publication | IEEE Xplore

A postprocessing technique for block effect elimination using a perceptual distortion measure


Abstract:

One of the drawbacks of the discrete cosine transform (DCT) is visible block boundaries due to coarse quantization of the coefficients. In this paper, an algorithm for th...Show More

Abstract:

One of the drawbacks of the discrete cosine transform (DCT) is visible block boundaries due to coarse quantization of the coefficients. In this paper, an algorithm for the reduction of blocking artifacts is presented. The proposed method allows to produce higher quality reconstructed images by adaptively filtering the video signal according to the noise visibility. A visual model is therefore defined for predicting the block edge visibility across each picture of the coded sequence. This model that accounts for the perceptual characteristics of the block distortion is described. Experimental results are presented for low bit-rate coded sequences. They show that the postfiltering operation yields significant results with enhanced visual quality.
Date of Conference: 21-24 April 1997
Date Added to IEEE Xplore: 06 August 2002
Print ISBN:0-8186-7919-0
Print ISSN: 1520-6149
Conference Location: Munich, Germany
Citations are not available for this document.

Cites in Papers - |

Cites in Papers - IEEE (3)

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1.
S. Chen, Z. He, B.L. Luk, "A generic postprocessing technique for image compression", IEEE Transactions on Circuits and Systems for Video Technology, vol.11, no.4, pp.546-553, 2001.
2.
B. Cahill, C. Heneghan, "Locally adaptive deblocking filter for low bit rate video", Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101), vol.2, pp.664-667 vol.2, 2000.
3.
Z. He, S. Chen, B. Luk, R. Istepanian, "Postprocessing for image coding applications using neural network visual model", Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378), pp.557-566, 1998.

Cites in Papers - Other Publishers (1)

1.
S Chen, Z He, P.M Grant, "Artificial neural network visual model for image quality enhancement", Neurocomputing, vol.30, no.1-4, pp.339, 2000.

References

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