Noise Restoration Of Compressed Image Data

James R. Sullivan

doi:10.1117/12.952230

1 May 1986 Noise Restoration Of Compressed Image Data

James R. Sullivan

Proceedings Volume 0594, Image Coding; (1986) https://doi.org/10.1117/12.952230
Event: 1985 International Technical Symposium/Europe, 1985, Cannes, France

Abstract

Image noise restoration and predictive image coding are combined by implementing a maximum-a-posteriori (MAP) estimator on the differential signal in a differential pulse code modulation (DPCM) image compression scheme. For a Laplacian differential-signal probability density function (pdf) and a Gaussian noise pdf, the MAP estimator is an adaptive coring operator which is linear in the uncored region with a bias toward zero and a null operator in the cored region. The bias and the width of the coring region are functions of the noise and differential-signal variance, which are estimated from local image statistics over variable-length line segments. Independent segments are isolated by using a generalized-likelihood-ratio-test (GLRT) for Laplacian signals to determine whether or not adjacent segments have statistically equivalent differential variances. Because the MAP operator is an additive bias, it can be inserted in the transmitter of a DPCM encoder without error build-up or overhead information, and since it lowers the variance of the signal to be quantized by reducing the noise it can simplify the encoder by decreasing the number of levels that are required.

Citation Download Citation

James R. Sullivan "Noise Restoration Of Compressed Image Data", Proc. SPIE 0594, Image Coding, (1 May 1986); https://doi.org/10.1117/12.952230

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KEYWORDS

Interference (communication)

Image segmentation

Statistical analysis

Image compression

Error analysis

Computer programming

Signal processing

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