Error resilience capabilities (cont d R=0.5 bit/pixel, ber=0.001

Transcription

1 Error resilience capabilities (cont d R=0.5 bit/pixel, ber=0.001 FLC (NTNU) VLC cont d)

2 Error resilience capabilities (cont d) Re-synch marker at packet boundaries Ability to locate errors in a packet Magic String (3 bytes) JP2 Header Length (2 bytes: msb first) L H Global Header L bytes) ( H Resync (2 bytes) Packet Head Body Resync (2 bytes) Head Packet Body optional

3 Error resilience capabilities (cont d) Re-synch marker at block boundaries Locate errors in a block Code block Segment Marker Segment Marker Segment Marker Bitplane Bitplane Bitplane error Segment Marker Bitplane

4 Reversible color transformation: making lossless colour coding possible Yr Ur Vr R + 2* G + = 4 = R G = B G B G R B Ur + Vr = Yr ( ) 4 = Ur + G = Vr + G All components must have identical subsampling parameters and same depth

5 Visual Frequency Weighting Allows system designers to take advantage of visual perception Fixed Visual Weighting (FVW) & Progressive Visual Coding (PVC) FVW: CSF are chosen according to the final viewing condition Implementation: Q steps in subband I are modified based on the CSF PVC: Visual weights are changes during the embedded process

6 Visual frequency weighting: FVW modify transform coefficients by multiplying by the CSF weight (decoder has to know) modify Q step sizes (decoder needs not know) modify the embedded coding order the distortion weights fed to the R-D optimization are altered this controls the relative significance of including different number of bitplanes from the embedded bitstream of each code-block

7 Visual frequency weighting: PVC Visual weights have to be changed during the embedded process difficult to change the coefficient values of Q steps performance of entropy coder might degrade due to changing statistics of the binary representation Solution change on the fly the order in which code blocks sub biplanes should appear in the embedded bitstream based on the visual weights

8 Visual frequency weighting: PVC Weighting set 0: r (0), with w (0) = { w0 (0), w1 (0),..., wn (0) } ; Weighting set 1: r (1), with w (1) = { w0 (1), w1 (1),..., wn (1) } ;... Weighting set m: r (m), with w (m) = { w 0 (m), w 1 (m),..., w n (m) } Distortion metric is changed progressively based on the visual weights during bitstream formation Bitsream formation is driven by postprocessing R-D optimization Progressive visual weights control the embedding order of code-block sub-bitplanes on the fly

9 ICIP 99, Oct , Kobe, Japan Example 0.25 bpp, TCQ(RMSE:8.81), Visual TCQ (13.53)

10 Line based transforms A way for low memory implementation of the wavelet transform Same wavelet coefficients as full frame wavelet transform Same encoding results as the standard VM

11 Line based transforms (cont d) Low pass Vertical LL Low pass Horizontal V L Line buffering for vertical decomposition HL High pass Horizontal LH Low pass Horizontal V H HH High pass Horizontal High pass Vertical Input image data

12 Line based transforms (cont d) Input Image Filtering Elements 4 Line Buffer Encode 3 Line Buffer Encode Line Buffer 2 Encode Line Buffer 1 Encode Line Buffer 0 Encode

13 Compressed image manipulation Allows for rotations of 90, 180, 270 degrees vertical flipping (horizontal axis symmetry) horizontal flipping (vertical axis symmetry) all possible combinations of above in the wavelet domain by rearranging the quantized subband coefficients (no modification of the coeffs.)

14 Compressed image manipulation: advantages More efficient in terms of memory complexity requirements No additional distortion is introduced due to inverse / forward transformation

15 Compressed image manipulation (cont d) OCB h =0, OCB v =0, T=0 Bit stream JPEG2000 Encoder original image Transcoder entropy decoding geometric manipulation entropy decoding Update of OCB h, OCB v and T bits Reconstructed image JPEG2000 Encoder Bit stream

16 Compressed image manipulation (cont d) Transcoding: vertical flipping on subbands

17 Compressed image manipulation (cont d) Transcoding: 90 degrees rotation on subbands

18 Compressed image manipulation (cont d) The only information needed at the decoder are the filtering convention orders (OCBh/OCHv) for horizontal and vertical direction The decoder must know if the lines and/or columns have been flipped

19 Postprocessing Typical artifacts in Wavelet coding are ringing effects Postprocesing can reduce these artifacts Postprocesing filter based on robust M- estimator

20 Postprocessing (cont d cont d.) Examples of filtering windows xˆ = arg min N x j i= 1 ρ( x i x j ) Huber L γ Truncated L 2 ρ( x) = γ ρ( x) = x,1 γ ρ( x) = min{ γx 2 γ + 2γ ( x γ ) 2 x 2,1} 2 x γ x > γ Lorenzian ρ( x) = log( ( x γ ) 2 ) P(x) characterizes the behavior of the estimator, or the smoothing capability

21 Conclusions Advanced still image coding standard Better than current baseline JPEG Includes many interesting functionalities Intended to become the key standard for still image coding in the next millennium

22 JPEG More information EUROSTILL SPEAR JJ2000 JavaTM JPEG2000 development

23 Acknowledgements Mr. Joel Askelöf, Ericsson Dr. Eiji Atsumi, Mitsubishi, Japan Martin Boliek, Ricoh Dr. Christos Chrysafis, HP Labs Prof. Touradj Ebrahimi, EPFL Prof. Nariman Farvardin, Univ. Maryland Prof. Faouzi Kossentini, UBC Mathias Larsson, Ericson Dr. Daniel Lee, HP Labs Dr. Eric Majani, CRF Prof. Michael Marcellin, Univ. of Arizona Prof. Andrew Perkis, NTNU Dr. Majid Rabbani, Kodak Dr. David Taubman, HP Labs & Univ. New South Wales * * In alphabetical order

24 Thank you for your attention!

25 C.A.Christopoulos Christopoulos A.N.Skodras