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JPEG Artifact Removal Using Error Distributions of Linear Coefficient Estimates

  • Mika Inki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5112)

Abstract

In this paper we present a method for JPEG artifact removal. The method works by estimating the distribution of a DCT coefficient given the values of the other coefficients, and then computing the expected value of this distribution in the quantization interval. We use information from an area exceeding the original block boundaries. Our method requires only information about image covariance, from which we estimate the effects of the transformations and quantization used in JPEG, under certain assumptions about the distributions. We will show that our method significantly improves the mean square error in our testing. Additionally, our method is shown to visibly reduce blocking artifacts in the images.

Keywords

Image Covariance Image Restoration JPEG Compression Laplacian Distribution JPEG Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Wallace, G.K.: The JPEG still picture compression standard. Comm. ACM 34, 30–44 (1991)CrossRefGoogle Scholar
  2. 2.
    Skodras, A., Christopoulos, C., Ebrahimi, T.: The JPEG 2000 still image compression standard. IEEE Signal Processing Magazine 18(5), 36–58 (2001)CrossRefGoogle Scholar
  3. 3.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall, Englewood Cliffs (2002)Google Scholar
  4. 4.
    Lai, J.Z.C., Liaw, Y.-C., Lo, W.: Artifact reduction of JPEG coded images using mean-removed classified vector quantization. Signal Processing 82, 1375–1388 (2002)zbMATHCrossRefGoogle Scholar
  5. 5.
    Nosratinia, A.: Denoising of JPEG images by re-application of JPEG. Journal of VLSI Signal Processing 27, 69–79 (2001)zbMATHCrossRefGoogle Scholar
  6. 6.
    Yang, Y., Galatsanos, N.P., Katsaggelos, A.K.: Regularized reconstruction to reduce blocking artifacts of block discrete cosine transform compressed images. IEEE Trans. on Circuits and Systems for Video Technology 3(6), 421–432 (1993)CrossRefGoogle Scholar
  7. 7.
    Shen, M.-Y., Kuo, C.-C.J.: Review of postprocessing techniques for compression artifact removal. Journal of Visual Communication and Image Representation 9(1), 2–14 (1998)CrossRefGoogle Scholar
  8. 8.
    Liu, S., Bovik, A.C.: Efficient DCT-domain blind measurement and reduction of blocking artifacts. IEEE Trans. on Circuits and Systems for Video Tech. 12(12), 1139–1149 (2002)CrossRefGoogle Scholar
  9. 9.
    Alter, F., Durand, S., Froment, J.: Adapted total variation for artifact free decompression of JPEG images. Journal of Mathematical Imaging and Vision 23, 199–211 (2005)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Mika Inki
    • 1
  1. 1.Helsinki Institute for Information TechnologyUniversity of Helsinki

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