Skip to main content

Adaptive Resolution Upconversion for Compressed Video Using Pixel Classification

Abstract

A novel adaptive resolution upconversion algorithm that is robust to compression artifacts is proposed. This method is based on classification of local image patterns using both structure information and activity measure to explicitly distinguish pixels into content or coding artifacts. The structure information is represented by adaptive dynamic-range coding and the activity measure is the combination of local entropy and dynamic range. For each pattern class, the weighting coefficients of upscaling are optimized by a least-mean-square (LMS) training technique, which trains on the combination of the original images and the compressed downsampled versions of the original images. Experimental results show that our proposed upconversion approach outperforms other classification-based upconversion and artifact reduction techniques in concatenation.

References

  1. 1.

    Kondo T, Node Y, Fujiwara T, Okumura Y: Picture conversion apparatus, picture conversion method, learning apparatus and learning method. US patent: no. 6,323,905, November 2001

  2. 2.

    Atkins CB, Bouman CA, Allebach JP: Optimal image scaling using pixel classification. Proceedings of IEEE International Conference on Image Processing (ICIP '01), October 2001, Thessaloniki, Greece 3: 864–867.

    Google Scholar 

  3. 3.

    Li X, Orchard MT: New edge-directed interpolation. IEEE Transactions on Image Processing 2001,10(10):1521–1527. 10.1109/83.951537

    Article  Google Scholar 

  4. 4.

    Tegenbosch JAP, Hofman PM, Bosma MK: Improving non-linear up-scaling by adapting to the local edge orientation. Visual Communications and Image Processing, January 2004, San Jose, Calif, USA, Proceedings of SPIE 5308: 1181–1190.

    Google Scholar 

  5. 5.

    Plaziac N: Image interpolation using neural networks. IEEE Transactions on Image Processing 1999,8(11):1647-1651. 10.1109/83.799893

    Article  Google Scholar 

  6. 6.

    Keys RG: Cubic convolution interpolation for digital image processing. IEEE Transactions on Acoustics, Speech, and Signal Processing 1981,29(6):1153-1160. 10.1109/TASSP.1981.1163711

    MathSciNet  Article  Google Scholar 

  7. 7.

    Greenspan H, Anderson CH, Akber S: Image enhancement by nonlinear extrapolation in frequency space. IEEE Transactions on Image Processing 2000,9(6):1035-1048. 10.1109/83.846246

    MathSciNet  Article  Google Scholar 

  8. 8.

    Shao L, Kirenko I: Content adaptive coding artifact reduction for decompressed video and Images. Proceedings of International Conference on Consumer Electronics (ICCE '07), January 2007, Las Vegas, Nev, USA 1–2.

    Google Scholar 

  9. 9.

    Shao L: Unified compression artifacts removal based on adaptive learning on activity measure. to appear in Digital Signal Processing

  10. 10.

    Kirenko I, Muijs R, Shao L: Coding artifact reduction using non-reference block grid visibility measure. Proceedings of IEEE International Conference on Multimedia and Expo (ICME '06), July 2006, Toronto, Ontario, Canada 469–472.

    Google Scholar 

  11. 11.

    Yuen M, Wu HR: Reconstruction artifacts in digital video compression. Digital Video Compression: Algorithms and Technologies, February 1995, San Jose, Calif, USA, Proceedings of SPIE 2419: 455–465.

    Google Scholar 

  12. 12.

    Freeman WT, Pasztor EC: Markov networks for super-resolution. Proceedings of the 34th Annual Conference on Information Sciences and Systems (CISS '00), March 2000, Princeton, NJ, USA

    Google Scholar 

  13. 13.

    Baker S, Kanade T: Limits on super-resolution and how to break them. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '00), June 2000, Hilton Head Island, SC, USA 2: 372–379.

    Google Scholar 

  14. 14.

    Kondo T, Fujimori Y, Ghosal S, Carrig JJ: Method and apparatus for adaptive filter tap selection according to a class. US patent: no. 6,192,161 B1, February 2001

  15. 15.

    Zhao M, Kneepkens REJ, Hofman PM, de Haan G: Content adaptive image de-blocking. Proceedings of IEEE International Symposium on Consumer Electronics (ISCE '04), September 2004, Reading, Mass, USA 299–304.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ling Shao.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Shao, L. Adaptive Resolution Upconversion for Compressed Video Using Pixel Classification. EURASIP J. Adv. Signal Process. 2007, 071432 (2007). https://doi.org/10.1155/2007/71432

Download citation

Keywords

  • Entropy
  • Structure Information
  • Original Image
  • Activity Measure
  • Reduction Technique