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Experimental study on scan order and motion compensation in lossless video coding

Telematics/Network Engineering. Experimental study on scan order and motion compensation in lossless video coding. Scan order and motion compensation in lossless coding. Team. School of Telematics and Network Engineering Carinthia Tech Institute, Austria

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Experimental study on scan order and motion compensation in lossless video coding

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  1. Telematics/Network Engineering Experimental study on scan order and motion compensation in lossless video coding

  2. Scan order and motion compensation in lossless coding Team School of Telematics and Network Engineering Carinthia Tech Institute, Austria • Team of students: Stefan A. Kramatsch Agnes Gruber, Alexander Krapesch, Stefan Matschitsch, Thomas Mayerdorfer, Stefan Miedl, Stefan Moser, Martin Tschinder, Stefan Zorn-Pauli • Project leader Dr. Andreas Uhl • Head of School Dr. Herbert Stögner

  3. Presentation Outline Structure • Motivation • Basics • Realization • Results • Conclusion

  4. Motivation Project goals • Semester Project in Compression Techniques 2 • Alternative way to view videos • Make data compression more concrete • Experience usage of programming languages in picture processing

  5. Basics(1) Lossless video coding • Mainly used in medical applications – required by legal regulation • JPEG, JPEG-LS, lossless JPEG 2000 on per-frame basis • Temporal redundancy ignored  no motion compensation  limited compression performance

  6. Basics(2) Classical view of video data

  7. Basics(3) Classical view of video data • Temporally ordered still images • Frames are similar  basis for motion compensated hybrid coding  basis for application of 3D video techniques • Possible to form a 3D block of video data

  8. Vertical View Horizontal View Normal View Basics(4) Different views on the video block

  9. Basics(5) Different views on the video block Normal view Horizontal view Vertical view Frame 40 Frame 112 Frame 112

  10. Basics(6) Scan order

  11. Basics(7) Streams – stream compression • File seen as a stream of gray values • Written to a .txt file • File compressors used: - Arithmetical coder - Runlength Encoding (RLE) - Huffman Coding

  12. Basics(8) Motion compensation – Block matching • Scene divided into non-overlapping “block“ regions • Compare blocks (current <-> reference frame)  motion vector for each block • “Best“ match based on mean square error  Stored as prediction • Current frame – prediction = residual frame • to be compressed • Common for lossy compression

  13. Basics(9) Motion compensation – Block matching • Usage in lossless coding • Normally temporal based  now spatially Reference Frame 1 Residual Frame 40 Vertical View Horizontal View Frame 112 non BM and BM Frame 112 non BM and BM

  14. Realization(1) Implementation • Input: all frames of a video (in .pgm format) • Build the 3D video block • Cut normally, vertically and horizontally • With or without blockmatching • Frame based or stream based computing • Implemented in c++

  15. Realization(2) Implementation of block matching • Matlab application • Based on one reference frame • all remaining: residual frames • Searchwindow 32x32 Pixels • Blocksize 16x16 Pixels • Similar Block search based on Root Mean Square

  16. Realization(3) Lossless frame compression • JPEG 2000 Lossless mode • Java Implementation: JJ2000 (http://jj2000.epfl.ch) • Standard options except: • Lossless Mode (“ –lossless on “) • Cancel console output (“ –verbose off “)

  17. Realization(4) Testvideos (Spatial x Temporal resolution) • Akiyo (176 x 144 x 300) – low movement • Carphone (176 x 144 x 383) – high movement • Claire (176 x 144 x 494) – low movement • Football (720 x 486 x 60) – high movement • Foreman (176 x 144 x 49) – high movement • Grandma (176 x 144 x 871) – low movement • Mobile (720 x 576 x 40) – high movement • Mother and Daughter (176 x 144 x 962) – low movement • Salesman (176 x 144 x 449) – low movement

  18. Results Compression Ratio Low movementHigh MovementStream

  19. Conclusion(1) Without Blockmatching • Improved frame based compression by alternative views • Exploitation of spatial instead of temporal redundancies through alternative scan order • Little computational demand compared to BM • Increased memory demand and coding delay • Stream compression has little effect

  20. Conclusion(2) With Blockmatching • The increase of compression ratio does not justify the usage of BM algorithms in case of alternative views • Superior results for 1D based compression algorithms

  21. Telematics/Network Engineering Thank you for your attention!

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