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INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS, 2009. ICT '09. TAREK OUNI WALID AYEDI MOHAMED ABID NATIONAL ENGINEERING SCHOOL OF SFAX New Low Complexity.

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Presentation on theme: "INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS, 2009. ICT '09. TAREK OUNI WALID AYEDI MOHAMED ABID NATIONAL ENGINEERING SCHOOL OF SFAX New Low Complexity."— Presentation transcript:

1 INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS, ICT '09. TAREK OUNI WALID AYEDI MOHAMED ABID NATIONAL ENGINEERING SCHOOL OF SFAX New Low Complexity DCT Based Video Compression Method

2 Outline Introduction DCT based coding method Proposed method Experiments Conclusion

3 Introduction Video signal has high temporal redundancies due to the high correlation between successive frames. Current video compression technics are not suitable for exploiting this redundancy. This paper presents a new video compression approach exploiting the temporal redundancy in the video frames.  improve compression efficiency  with minimum processing complexity

4 Introduction This paper consists on a 3D to 2D transformation of the video frames that allows  exploring the temporal redundancy.  avoiding the computational MC step. The transformation turns the spatial-temporal correlation into high spatial correlation.  e.g, transforms each group of pictures to one picture Decorrelation of the resulting pictures by the DCT  energy compaction  high video compression ratio

5 Introduction The proposed method is efficient especially in high bit rate and with slow motion video. The proposed method is suitable for  video surveillance applications  embedded video compression systems

6 Problem Motion estimation process is computationally intensive.  stored video applications.  off-line on powerful computers. Not appropriate to be implemented as a real-time for  video surveillance camera  fully digital video camera

7 Solution Improve compression efficiency  Temporal redundancies are more relevant than spatial one.  Exploiting more redundancies in the temporal domain can achieve more efficient compression. Minimize processing complexity  3D transform produces video compression ratio  close to the motion estimation based.  less complex processing.  exploit temporal redundancy.

8 Outline Introduction DCT based coding method Proposed method Experiments Conclusion

9 DCT based coding method Compression  energy compaction

10 DCT based coding method Undesirable effects 1. graininess 2. blurring 3. blocking artifacts

11 3D-DCT coding method The 2D-DCT has the potential of easy extension into the third dimension.  e.g, 3D-DCT 3D-DCT includes the time as third dimension into the transformation and energy compaction process.

12 3D-DCT coding method In 3-D transform coding based on the DCT, the video is first divided into blocks of M N K pixels.  M : horizontal dimension  N : vertical dimension  K : temporal dimension Treat video as a succession of 3D blocks or video cubes.

13 3D-DCT coding method 3-D transform coding method  Advantage : 1. do not require the computationally intensive process of motion estimation.  Disadvantage : 1. requires K frame memories both at the encoder and decoder to buffer the frames.

14 3D-DCT coding method 3-D based coder v.s Motion compensated coder  3-D based coder :  high compression ratio  lower complexity The proposed method puts in priority the exploitation of temporal redundancy.  temporal is more important than spatial.

15 Outline Introduction DCT based coding method Proposed method Experiments Conclusion

16 Proposed method Basic idea is to represent video data with high correlated form.  projecting temporal redundancy of each group of pictures into spatial domain.  Combining them with spatial redundancy in one representation with high spatial correlation.  The obtained representation will be compressed as still image with JPEG coder.

17 Proposed method The proposed method step :  input the video cube.  decompose into temporal frames.  gather into one big frame.  coding the obtained big frame.

18 Proposed method A. Hypothesis many experiences had proved that the variation is much less in the temporal dimension than the spatial one. pixels, in 3D video signal, are more correlated in temporal domain than in spatial one. Expression :

19 Proposed method spatial temporal

20 Proposed method B. Accordion based representation temporal and spatial decomposition of video cube

21 Proposed method

22 C. Accordion analytic representation input the GOP frames and output the resulting frame IACC inverse process :

23 Proposed method D. Coding ACC-JPEG decompose the "IACC" frame into 8x8 blocks. for each 8x8 block : Discrete cosine Transformation (DCT). Quantification of the obtained coefficients. Course in Zigzag of the quantized coefficients. Entropic Coding of the coefficients (RLE, Huffman).

24 Outline Introduction DCT based coding method Proposed method Experiments Conclusion

25 Experiments A. Parameters of the representation

26 Experiments B. Compression performance

27 Experiments C. ACC-JPEG artifacts

28 Experiments Compare to MPEG-4

29 Outline Introduction DCT based coding method Proposed method Experiments Conclusion

30 Feature analysis  Symmetry  Simplicity  Objectivity  Flexibility Exploits temporal redundancy with the minimum of processing complexity is suitable in video embedded systems or video surveillance. Worst compression performance with ”non-uniform and fast motion” sequence.


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