1 / 29

Highly Parallel Line-Based Image Coding for Many Cores

IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 1, JANUARY 2012. Xiulian Peng , Jizheng Xu , Senior Member, IEEE, You Zhou, Member, IEEE , Feng Wu, Senior Member, IEEE. Highly Parallel Line-Based Image Coding for Many Cores. Guan-Yu liu. Introduction Proposed Method

lieu
Download Presentation

Highly Parallel Line-Based Image Coding for Many Cores

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 1, JANUARY 2012 • XiulianPeng, JizhengXu, Senior Member, IEEE, • You Zhou, Member, IEEE, • FengWu, Senior Member, IEEE Highly Parallel Line-Based Image Coding for Many Cores • Guan-Yu liu

  2. Introduction • Proposed Method • Hierarchical Coding • Adaptive Line Prediction • Variable-Length Transform • Parallel Entropy Coding • Extension to Lossless Coding Outline #1

  3. Parallelism Analysis • Experimental Result • Encode Parallelism • Decode Parallelism • Coding Performance • Conclusion Outline #2

  4. Pipeline approaches [4] Slice partitioning approaches MB-reordering approaches Introduction #1

  5. line-by-line image coding (LBLC) • Divided into small fixed-length segments • Different units • Three different line prediction methods • Parallel context-adaptive binary arithmetic coding Introduction #2

  6. The current block needs the reconstructed pixels of its upper, left, upper-left, and upper-right blocks for prediction. Proposed Method #1

  7. Proposed Method #2 Equal M N lines N

  8. Hierarchical Coding • prediction method unit (PMU) • prediction parameter unit (PPU) • transform unit (TU) Proposed Method.A #1

  9. Hierarchical Coding Proposed Method.A #2 Then how to make a decision?

  10. Block-based • RDO • There are three implicit prediction methods • two local prediction methods • predefined filters (PDF) • direction-aided local training (DA-LT) • one nonlocal prediction • line–template matching (LTM) Proposed Method.B #1

  11. Training window Proposed Method.B #2

  12. predefined filters (PDF) Proposed Method.B #3

  13. Proposed Method.B #4

  14. Minimize the prediction MSE for all pixels inside W • 123 Proposed Method.B #5

  15. direction-aided local training (DA-LT) Proposed Method.B #6

  16. Train a Wiener filter • 123 • This problem can simply be solved by the least-square approach. Proposed Method.B #7

  17. line template matching Proposed Method.B #8

  18. Variable-Length Transform • After the prediction, 1-D transforms are optionally performed on the predicted residues to further decorrelate them. Proposed Method.C #1

  19. Parallel Entropy Coding • Context-based arithmetic coding method is used, which is similar to the CABAC. Proposed Method.D #1

  20. Extension to Lossless Coding Proposed Method.E #1

  21. Extension to Lossless Coding Proposed Method.E #2

  22. The segments on the same line are assigned to different cores. All kth segments are assigned to the same core and coded sequentially. Proposed Method

  23. Encode Experimental Result.A #1

  24. Encode Experimental Result.A #2

  25. Decode Experimental Result.B #1

  26. Coding Performance Experimental Result.C #1

  27. Coding Performance Experimental Result.C #2

  28. Experimental Result

  29. It reduces the dependence among different cores and the independent coding of each segment within a line makes it easy to achieve a high parallelism. • The computational complexity is high at the decoder due to the derivation of prediction parameters. Conclusion

More Related