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JPEG2000. The next generation still image coding system. Touradj Ebrahimi*, Charilaos Christopoulos* *. *Ecole Polytechnique Federale d e Lausanne, Switzerland * * MediaLab, Ericsson Research, Stockholm, Sweden. Standards Organizations. International Organization for Standardization (ISO)

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The next generation still image coding system

Touradj Ebrahimi*, Charilaos Christopoulos**

*Ecole Polytechnique Federale de Lausanne, Switzerland

**MediaLab, Ericsson Research, Stockholm, Sweden

Standards organizations

Standards Organizations

  • International Organization for Standardization (ISO)

    • 75 Member Nations

    • 150+ Technical Committees

    • 600+ Subcommittees

    • 1500+ Working Groups

  • International Electrotechnical Commission (IEC)

    • 41 Member Nations

    • 80+ Technical Committees

    • 100+ Subcommittees

    • 700+ Working Groups


Iso iec terminology

ISO / IEC Terminology

  • ISO: International Standardization Organization

  • IEC: International Electrotechnical Committee

  • ISO/IEC JTC1: Joint Technical Committee

  • SC29: Information Technologies

    • WG1: still images, JPEG and JBIG

      • Joint Photographic Experts Group and Joint Bi-level Image Group

    • WG11: video, MPEG

      • Motion Picture Experts Group

    • WG12: multimedia, MHEG

      • Multimedia Hypermedia Experts Group

Jpeg summary

JPEG: Summary

  • JPEG(Joint Photographic Experts Group)

  • “Digital Compression and Coding of Continuous-tone Still Images”

  • Joint ISO and ITU-T

  • Published in 4 Parts:

    • ISO/IEC 10918-1 | ITU-T T.81 : Requirements and guidelines

    • ISO/IEC 10918-2 | ITU-T T.83 : Compliance testing

    • ISO/IEC 10918-3 | ITU-T T.84: Extensions

    • ISO/IEC 10918-4 | ITU-T T.86: Registration of JPEG Parameters, Profiles, Tags, Color Spaces, APPn Markers Compression Types, and Registration Authorities (REGAUT)

Jpeg summary cont

JPEG: Summary (cont.)

  • JPEG derived industry standards

  • JFIF (JPEG File Interchange Format, <xxxxxx.jpg>)

  • JTIP (JPEGTiled, Pyramid Format)

  • TIFF (Tagged Image File Format)

  • SPIFF (Still Picture Interchange File Format, JPEG Part 3)

  • FlashPix

    • Developed by Hewlett-Packard, Kodak, Microsoft, Live Picture (1996)

    • Transferred to Digital Imaging Group (DIG), an industry consortium


JPEG 2000: Image Coding System



Why another still image compression standard?

In order to address areas that the current standards fail to produce the best quality or performance, as for example:

  • Low bit-rate compression: for example below 0.25 bpp

  • Lossless and lossy compression: No current standard exists that can provide superior lossy and lossless compression in a single codestream.

  • Computer generated imagery: JPEG was optimized for natural imagery and does not perform well on computer generated imagery.


Why another still image compression standard? (cont’d)

  • Transmission in noisy environments: The current JPEG standard has provision for restart intervals, but image quality suffers dramatically when bit errors are encountered.

  • Compound documents: Currently, JPEG is seldom used in the compression of compound documents because of its poor performance when applied to bi-level (text) imagery.

  • Random codestream access and processing

Why another still image compression standard cont d

Why another still image compression standard?(cont’d)

  • Open Architecture: Desirable to allow open architecture to optimise the system for different image types and applications.

  • Progressive transmission by pixel accuracy and resolution

Jpeg2000 markets and applications

JPEG2000Markets and Applications

  • Internet

  • Mobile

  • Printing

  • Scanning

  • Digital Photography

  • Remote Sensing

  • Facsimile

  • Medical

  • Digital Libraries

  • E-Commerce

The relation jpeg jpeg2000

The relation JPEG  JPEG2000

  • JPEG2000 is intended to complement and not to replace the current JPEG standards


JPEG2000 Development

  • Timeline

    • Feb 96 (Geneva) started with original proposal

    • Nov 96 (Palo Alto) test method agreed

    • Mar 97 (Dijon) call for proposals

    • Jul 97 (Sapporo) requirements analysis started

    • Nov 97 (Sydney) algorithm competition & selection

    • VM 1 (Mar 98), VM 2 (Aug 98), split to VM 3A and 3B Nov 98. Converged to VM4 and WD in Mar 99

    • Promotion to CD, FCD, FDIS as well as creation of different parts

  • Current status: VM 8, FDIS


JPEG2000 contributors

  • 21 countries / 80-100 meeting attendees

    • EUROPE

      • Ericsson, Nokia, Philips, Canon, Motorola, IMEC, EPFL, NTNU, Technical University of Denmark, VUB, Technical University of Berlin

    • USA/Canada

      • Kodak, HP, Rockwell, Motorola, TI, Ricoh, Sharp, Adobe, Sarnoff, SAIC, Teralogic, Univ. of Arizona, Univ. of Southern California, Univ. of Maryland, UBC, RPI

    • ASIA/Australia

      • Samsung, Sony, Mitsubishi, CISRA, Univ. New South Wales, Oki, Panasonic, ...

  • 3-4 meetings per year


First steps of algorithm development

  • November 1997 (Sydney)

    • about 100 participants

    • 24 candidate algorithms

    • All of them intensively tested

      • objective tests (quality metrics) ran on 22 test images at lossless and 6 different lossy bit rates (2, 1, 0.5, 0.25, 0.125, 0.0625 bpp)

      • subjective tests by 40 evaluators at the 3 lowest bit rates

    • selection WTCQ

    • VM established in March 98



JPEG2000 work plan

  • Part I: A set of tools covering a good proportion of application requirements (20-80 rules)

  • Other parts are also defined and planned for a further date

  • Possible Amendment will be added to Part I

  • Schedule for part I:

    Elevation to FDIS: 08/00

    Elevation to IS: 12/00


JPEG2000 work plan

  • Part II: Extension tools to cover specific applications

  • Part III: Motion JPEG2000

  • Part IV: Conformance

  • Part V: Reference software

  • Part VI: Compound images file format

  • Part VII: Technical Report

  • Part VIII: ?


Status of existing implementations

Software status

  • C implementation (SAIC / Univ. of Arizona / HP)

    • JPEG2000 Verification Model used for the development of the standard

  • JavaTM implementation (EPFL, Ericsson, Canon)

    • Reference implementation of JPEG2000 in part V and publicly available

  • C implementation (ImagePower / UBC)

    • Reference implementation of JPEG2000 in part V

Jpeg2000 features in part i

JPEG2000 Features in Part I

  • High compression efficiency

  • Lossless colour transformations

  • Lossy and lossless coding in one algorithm

  • Embedded lossy to lossless coding

  • Progressive by resolution, quality, position, …

  • Static and dynamic Region-of-Interest coding/decoding

  • Error resilience

  • Perceptual quality coding

  • Multiple component image coding

  • Tiling

  • Palletized image coding

  • Light file format (optional)

Some examples

Some examples



JPEG baseline


Jpeg at 0 125 bpp

JPEG at 0.125 bpp

Jpeg2000 at 0 125 bpp

JPEG2000 at 0.125 bpp

Jpeg at 0 25 bpp

JPEG at 0.25 bpp

Jpeg2000 at 0 25 bpp

JPEG2000 at 0.25 bpp

Jpeg at 0 5 bpp

JPEG at 0.5 bpp

Jpeg2000 at 0 5 bpp

JPEG2000 at 0.5 bpp

Jpeg compound image 1 0 bpp

JPEG compound image 1.0 bpp

Jpeg2000 compound image 1 0 bpp

JPEG2000 compound image 1.0 bpp

Major differences between jpeg and jpeg2000

Major Differences between JPEG and JPEG2000

  • New functionalities

    • ROI

    • Better error resiliency

    • More flexible progressive coding

    • ...

  • Lossy to lossless in one system

  • Better compression at low bit-rates

  • Better at compound images and graphics (palletized)

Jpeg2000 and other standards

JPEG2000 and other standards

Some lossless compression results

Some lossless compression results

Comparison of various algorithms from a functionality point of view

Comparison of various algorithms from a functionality point of view

More in depth comparisons between jpeg2000 versus other standards

More in depth comparisons between JPEG2000 versus other standards

  • « JPEG 2000 still image coding versus other standards », D. Santa-Cruz, T. Ebrahimi, J. Askelöf, M. Larsson and Ch. Christopoulos, in Proc. of SPIE, Vol. 4115

  • « A study of JPEG 2000 still image coding versus other standards », D. Santa-Cruz, T. Ebrahimi, in Proc. of the X European Signal Processing Conference (EUSIPCO), Tampere, Finland, September 5-8, 2000

  • « An analytical study of JPEG 2000 functionalities »,D. Santa-Cruz, T. Ebrahimi, in Proc. of the IEEE International Conference on Image Processing (ICIP), Vancouver, Canada, September 10-13, 2000



Algorithm description


Jpeg2000 basic encoding scheme

JPEG2000: Basic encoding scheme










Why block coding

Why block coding?

  • exploit local variations in the statistics of the image from block to block

  • provide support for applications requiring random access to the image

  • reduce memory consumption in hardware implementations of the compression or decompression engine

  • Allow for parallel implementation

Ebcot layered bitstream formation

EBCOT: layered bitstream formation

  • Each bitstream is organized as a succession of layers

  • Each layer contains additional contributions from each block (some contributions might be empty)

  • Block truncation points associated with each layer are optimal in the rate distortion sense

  • Rate distortion optimization can be performed but it does not need to be standardized

Ebcot layered formation

EBCOT layered formation

Wavelet transform

Wavelet Transform

Dyadic decomposition

  • Two filters supported

    • W9x7 (Floating point)

      for lossy coding

    • W5x3 (Integer) for

      lossless coding

  • Only dyadic decomposition supported




  • Explicit

    • Define a specific quantization step for each subband

    • Smaller quantization steps for lower resolution subbands

  • Implicit

    • Quantization steps derived from LL subband quantization steps

    • Smaller quantization steps for lower resolution subbands

  • Reversible

    • No quantization but pure bit plane coding of transform coefficients

  • Possibility of visual weighting

    • Fixed visual weighting

    • Visual progressive coding (VIP)

Lazy coding mode


  • Not all bitplanes need to be encoded by arithmetic coding

  • Some bits are saved as raw bits

  • This increases speed without sacrificing performance

Lazy mode image gold

Lazy mode: Image “Gold”

No lazy mode 0 0625 bpp

No lazy mode: 0.0625 bpp

Lazy mode 0 0625 bpp

Lazy mode: 0.0625 bpp

No lazy mode 0 25 bpp

No lazy mode: 0.25 bpp

Lazy mode 0 25 bpp

lazy mode: 0.25 bpp

Multi component imagery

Multi-component imagery

  • up to 256 components

  • arbitrary dimensions/bitdepths for each component

  • reversible & non-reversible component color transforms

Reversible color transformation making lossless color coding possible

Reversible color transformation:making lossless color coding possible

All components must have identical subsampling parameters and same depth before transformation

Multiresolution decomposition



Multiresolution decomposition

Multiresolution decomposition1






Multiresolution decomposition

Multiresolution decomposition2









Multiresolution decomposition

Multiresolution decomposition3

Multiresolution decomposition











Jpeg2000 scalability


JPEG2000: Scalability

  • Different modes are realized depending on the way information is written into the codestream

Scalability progressive by resolution

Scalability - Progressive By Resolution

Scalability progressive by resolution1

Scalability - Progressive By Resolution

Scalability progressive by resolution2

Scalability - Progressive By Resolution

Scalability progressive by resolution3

Scalability - Progressive By Resolution

Scalability progressive by accuracy

Scalability - Progressive By Accuracy

Scalability progressive by accuracy1

Scalability - Progressive By Accuracy

Scalability progressive by accuracy2

Scalability - Progressive By Accuracy

Example progressive by resolution

Example:Progressive by resolution

  • Image:Woman

  • Resolution levels:5

  • Decoded sizes: 1/16





Example progressive by quality

Example:Progressive by quality

  • Image:Woman

  • Bitrates:0.125 bpp

    0.25 bpp

    0.5 bpp

    1.0 bpp

    2.0 bpp


0.125 bpp


0.25 bpp


0.5 bpp


1.0 bpp


2.0 bpp

Region of interest coding

Region Of Interest coding

  • Allows certain parts of an image to be coded or decoded in better quality

  • Static:

    • The ROI is decided and coded once for all en the encoder side

  • Dynamic:

    • The ROI can be decided and decoded on the fly from a same bitstream

  • C

    Roi some visual results

    ROI: Some visual results

    69:1 overall compression ratio

    No ROI

    Rectangular ROI

    Regions of interest

    Regions Of Interest

    Regions of interest1

    Regions Of Interest

    Regions of interest2

    Regions Of Interest

    Roi coding mask computation

    ROI coding: mask computation

    Region of interest coding1

    Region Of Interest coding


      • Calculate wavelet transform of whole image/time

      • calculate ROI mask == set of coefficients that are needed for up to lossless ROI coding

      • Encoding is progressive by accuracy and resolution

  • NOTE: ROI mask need NOT be transmitted to decoder (location and shape of ROI needs however)

  • Creation of roi mask

    Creation of ROI mask

    • The ROI masks are acquired by looking at the inverse transform

    • For each pixel (X) that is in the ROI, the low and high frequency coefficients (L:s and H:s) that are needed to reconstruct the pixel, are included in the ROI mask

    Inverse transform of the 5-3 filter

    Roi scaling based method

    ROI Scaling based method

    Roi maxshift method

    ROI MaxShift method


    Example: ROI coding

    • Image:Woman

    • ROI:rectangular

    • Scaling value:6

    • Progressive type: SNR

    • Bitrate: 4bpp


    0.125 bpp


    0.25 bpp


    0.5 bpp


    1.0 bpp


    2.0 bpp


    4.0 bpp


    Example: ROI coding

    • Image:Woman

    • ROI:rectangular

    • Scaling value:MAXSHIFT

    • Progressive type: SNR

    • Bitrate: 4bpp

    1 0 bpp

    1.0 bpp

    3 0 bpp

    3.0 bpp


    4.0 bpp

    Roi maxshift mode what is the gain

    ROI Maxshift mode: what is the gain?

    • Support for arbitrary shaped ROI’s with minimal complexity

    • No need to send shape information

    • No need for shape encoder and decoder

    • No need for ROI mask at decoder side

    • Decoder as simple as non-ROI capable decoder

    • Can decide in which subband the ROI will begin

      • therefore it can give similar results to the general scaling method

    Roi coding what do we pay lossless image coding with roi

    ROI coding: what do we pay?Lossless image coding with ROI

    Roi coding what do we pay lossless image coding with roi1

    ROI coding: what do we pay?Lossless image coding with ROI

    Block transform coding

    Block transform coding

    • Tiling

      • Allow random access to portions of an image

    • Single-Sample Overlap Discrete Wavelet Transform (SSODWT)

      • Exploit overlapping in order to reduce blockiness

      • In part II


    Tiling 128x128 0 25 bpp

    Tiling (128x128, 0.25 bpp)

    Ssodwt 128x128 0 25 bpp

    SSODWT (128x128, 0.25 bpp)

    Error resilience capabilities

    Error resilience capabilities

    • Most still image coders use Entropy Coding

    • Variable Length Coding is known to be prone to channel or transmission errors

      • Loss of synchronization






    Bit errors (Noise)

    Burst errors (Fading)

    Error resilience

    Error resilience

    • Error resilience is achieved at two levels:

      • Entropy coding level

        • Code-blocks

        • Termination of arithmetic coding

        • Reset of context

        • Selective arithmetic coding bypass

      • Packet level

        • Short packet format

        • Resynchronization markers

    Visual frequency weighting

    Visual Frequency Weighting

    • Allows system designers to take advantage of visual perception

    • Utilize knowledge of the visual system’s varying sensitivity to spatial frequencies as measured in the contrast sensitivity function (CSF)

    • CSF is determined by the visual frequency of the transform coefficients; One CSF weight per subband

    • Design of CSF weights is an encoder issue; depends on viewing condition of decoded image

    Visual frequency weighting cont

    Visual Frequency Weighting (cont.)

    • Fixed Visual Weighting(FVW) & Progressive Visual Coding (PVC)

    • FVW: CSF are chosen according to the final viewing condition

    • PVC: Visual weights are changed during the embedded process


    Line based transforms

    • Most acquisition devices are serial in nature

    • Most common scanning patterns work on a line-by-line basis

    • Traditional wavelet transforms require whole image to be buffered and filtered

    • Filtering along a line, requires one line

    • Filtering along a column requires whole image

      That is too complex!

    Line based transforms

    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 in full frame wavelet transform

    File format

    File Format

    • File Format extension .jp2

    • Possible to include XML data

    • Possible to include vendor specific information

    • Possible to include IPR information

    • Possible to add URL to file format

      • Can be used by an application to acquire more information about the associated vendor specific extensions

    Jpeg2000 part i

    JPEG2000 Part I

    Core Coding System

    • Schedule

      • March 2000, FCD

      • September 1, FDIS

      • December 2000, IS

    • Only editorial changes allowed

    • File extension, .jp2


    Jpeg2000 part ii

    JPEG2000 Part II


    • Schedule

      • March 2000, WD

      • September 2000, CD

      • December 2000, FCD

      • April 2001, FDIS

      • July 2001, IS

    • File extension .jpx

    Jpeg2000 part iii

    JPEG2000 Part III


    • Schedule

      • March 2000, WD

      • December 2000, CD

      • March 2001, FCD

      • July 2001, FDIS

      • November 2001, IS

    • Based on JPEG2000 Part I

    • No inter-frame dependencies

    Jpeg2000 part v

    JPEG2000 Part V

    Reference Software

    • Schedule

      • March 2000, ED

      • July 2000, CD

      • December 2000, FCD

      • April 2001, FDIS

      • July 2001, IS

    • Software

      • JavaTM implementation (EPFL, Canon, Ericsson)

      • C implementation (UBC / ImagePower)

    Jpeg2000 part iv

    JPEG2000 Part IV

    Compliance Tests

    • Schedule

      • July 2000, WD

      • December 2000, CD

      • March 2001, FCD

      • July 2001, FDIS

      • November 2001, IS

    Jpeg2000 part v1

    JPEG2000 Part V

    Reference software

    • Schedule

      • July 2000, CD

      • December 2000, FCD

      • March 2001, FDIS

      • July 2001, IS

    Jpeg2000 part vi

    JPEG2000 Part VI

    Compound Image File Format

    • Schedule

      • August 2000, CD

      • December 2000, FCD

      • March 2001, FDIS

      • July 2001, IS

    Jpeg2000 part vii

    JPEG2000 Part VII

    Technical report

    • Schedule

      • December 2000, PDTR

      • March 2001, DTR

      • July 2001, TR



    • Advanced Still Image Coding System

    • More complex than JPEG but it offers many interesting functionalities

    • No IPR associated to Part I of the standard (free licensing)

    • Intended to become the key standard for still image coding in the next millennium

    More information

    More information

    • JJ2000


    • JPEG Web site:




    • SPEAR


    Contact us for any further information

    Contact us for any further information

    • Touradj Ebrahimi

      • [email protected]

    • Charilaos Christopoulos

      • [email protected]




    * In alphabetical order

    • Mr. Joel Askelöf, Ericsson

    • Mr. Nicolas Aspert, EPFL

    • Dr. Eiji Atsumi, Mitsubishi, Japan

    • Mr. Martin Boliek, Ricoh

    • Dr. A. Chien, Eastman Kodak Company, USA

    • Dr. Troy Chinen, Fuji

    • Mr. Raphael Grosbois, EPFL

    • Prof. Faouzi Kossentini, UBC

    • Mr. Mathias Larsson, Ericsson

    • Dr. Daniel Lee, HP Labs

    • Dr. Eric Majani, CRF

    • Prof. Michael Marcellin, Univ. of Arizona

    • Prof. Andrew Perkis, NTNU

    • Dr. Majid Rabbani, Eastman Kodak Company

    • Mr. Diego Santa Cruz, EPFL

    • Prof. Athanasios Skodras, Univ. Of Padras

    • Dr. David Taubman, HP Labs & Univ. New South Wales

    • and many others ...

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