25
ERICSSON RESEARCH Media Lab UNIVERSITY OF PATRAS Electronics Laboratory FLC (NTNU) VLC Error resilience capabilities Error resilience capabilities ( cont’d cont’d) R=0.5 bit/pixel, ber=0.001

FLC (NTNU) VLCdsp/JPEG2000/JPEG2000_126to150.… · ICIP’99, Oct. 24-28, Kobe, Japan Charilaos Christopoulos ERICSSON RESEARCH Media Lab UNIVERSITY OF PATRAS Electronics Laboratory

  • Upload
    others

  • View
    0

  • Download
    0

Embed Size (px)

Citation preview

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    FLC (NTNU) VLC

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

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    • Re-synch marker at packet boundaries• Ability to locate errors in a packet

    Magic String(3 bytes)

    “JP2”

    Header Length(2 bytes: msb first)

    HL

    Global Header

    ( HL bytes)

    Packet

    Head BodyRes

    ync

    (2 b

    ytes

    ) Packet

    Head BodyRes

    ync

    (2 b

    ytes

    )

    optional

    Error resilience capabilities Error resilience capabilities ((cont’dcont’d))

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    • Re-synch marker atblock boundaries

    • Locate errors in ablock

    Segment Marker

    Segment Marker

    Segment Marker

    Segment Marker

    Bitplane

    Bitplane

    Bitplane

    Bitplane

    error

    Code block

    Error resilience capabilities Error resilience capabilities ((cont’dcont’d))

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    Reversible color transformation:Reversible color transformation:making making lossless colourlossless colour coding possible coding possible

    GBVr

    GRUr

    BGRYr

    −=−=

    ++=

    4

    *2

    GVrB

    GUrR

    VrUrYrG

    +=+=

    +−= )

    4(

    All components must have identical subsamplingparameters and same depth

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    Visual Frequency WeightingVisual 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 finalviewing condition

    • Implementation: Q steps in subband I aremodified based on the CSF

    • PVC: Visual weights are changes during theembedded process

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    Visual frequency weighting: FVWVisual 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 includingdifferent number of bitplanes from the embeddedbitstream of each code-block

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    Visual frequency weighting: PVCVisual frequency weighting: PVC

    • Visual weights have to be changed duringthe 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 bitstreambased on the visual weights

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    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) = { w0

    (m) , w1(m) , . . . , wn

    (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 embeddingorder of code-block sub-bitplanes on the fly

    Visual frequency weighting: PVCVisual frequency weighting: PVC

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    ExampleExample0.25 bpp, TCQ(RMSE:8.81), Visual TCQ (13.53)

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    Line based transformsLine based transforms

    • A way for low memory implementation of thewavelet transform

    • Same wavelet coefficients as full framewavelet transform

    • Same encoding results as the standard VM

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    Line based transforms Line based transforms ((cont’dcont’d))

    VH

    VL

    HH

    LH

    HL

    LL

    Input image data

    High pass Horizontal

    High pass Horizontal

    Low pass Horizontal

    Low pass Horizontal

    Line buffering for vertical decomposition

    Low pass Vertical

    High pass Vertical

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    Line Buffer

    Line Buffer

    Line Buffer

    0

    1

    2

    3

    4

    Input Image

    Encode

    Encode

    Encode

    Encode

    Encode

    Line Buffer

    Filtering Elements

    Line Buffer

    Line based transforms Line based transforms ((cont’dcont’d))

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    Compressed image manipulationCompressed image manipulation

    • Allows for– rotations of 90, 180, 270 degrees– vertical flipping (horizontal axis symmetry)– horizontal flipping (vertical axis symmetry)– all possible combinations of abovein the wavelet domain

    by rearranging the quantized subbandcoefficients (no modification of the coeffs.)

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    Compressed imageCompressed imagemanipulation: advantagesmanipulation: advantages

    • More efficient in terms of– memory– complexity requirements

    • No additional distortion is introduceddue to inverse / forward transformation

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    Bit stream

    entropydecoding

    geometricmanipulation

    entropydecoding

    Transcoder

    Bit stream

    JPEG2000 Encoder

    JPEG2000 Encoder

    Update of OCB h, OCBv and T bits

    originalimage

    Reconstructed image Bit stream

    entropydecoding

    geometricmanipulation

    entropydecoding

    Transcoder

    Bit stream

    JPEG2000 Encoder

    JPEG2000 Encoder

    Update of OCB h, OCBv and T bits

    originalimage

    Reconstructed image

    OCBh=0, OCBv=0, T=0

    Compressed image manipulation Compressed image manipulation ((cont’dcont’d))

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    Transcoding: vertical flipping on subbands

    Compressed image manipulation Compressed image manipulation ((cont’dcont’d))

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    Transcoding: 90 degrees rotation on subbands

    Compressed image manipulation Compressed image manipulation ((cont’dcont’d))

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    • The only information needed at thedecoder are the filtering conventionorders (OCBh/OCHv) for horizontal andvertical direction

    • The decoder must know if the linesand/or columns have been flipped

    Compressed image manipulation Compressed image manipulation ((cont’dcont’d))

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    PostprocessingPostprocessing

    • Typical artifacts in Wavelet coding areringing effects

    • Postprocesing can reduce these artifacts

    • Postprocesing filter based on robust M-estimator

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    PostprocessingPostprocessing ((cont’dcont’d.).)Examples of filtering windowsExamples of filtering windows

    ∑=

    −=N

    ijix

    xxxj 1

    )(minargˆ ρ

    ))(1log()(

    }1,min{)(

    21,||)(

    ||)|(|2

    ||)(

    221

    22

    2

    2

    γ

    γγ

    ργρ

    γρ

    γγγγγ

    ρ

    xxLorenzian

    xxLTruncated

    xxL

    xx

    xxxHuber

    +=

    =

    ≤≤=

    >−+≤

    =

    P(x) characterizes the behavior of the estimator,or the smoothing capability

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    ConclusionsConclusions

    • 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

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    More informationMore information• JPEG

    – http://www.jpeg.org

    • EUROSTILL– http://ltswww.epfl.ch/~eurostill

    • SPEAR– http://spear.jpeg.org/

    • JJ2000– JavaTM JPEG2000 development– http://jj2000.epfl.ch

  • ICIP’99, Oct. 24-28, Kobe, JapanCharilaos Christopoulos

    ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    AcknowledgementsAcknowledgements• 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* In alphabetical order

  • ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    Thank you for your attention!Thank you for your attention!

  • ERICSSON RESEARCH

    Media Lab

    UNIVERSITY OF PATRAS

    Electronics Laboratory

    C.A.C.A.ChristopoulosChristopouloscharilaos.christopoulos@era.ericsson.se

    [email protected]