JPEG2000

ERICSSON RESEARCH Media Lab

1Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne

JPEG2000JPEG2000The next generation still The next generation still

image coding systemimage coding system

Touradj Ebrahimi*, Charilaos Christopoulos**

*Ecole Polytechnique Federale de Lausanne, Switzerland

**MediaLab, Ericsson Research, Stockholm, Sweden



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

T



ISO / IEC TerminologyISO / 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 (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 derived industry standards

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

• JTIP (JPEG Tiled, 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

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Why another still image compression Why another still image compression standard?standard?

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.

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



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

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



Internet Mobile Printing Scanning Digital

Photography Remote Sensing Facsimile Medical Digital Libraries E-Commerce

JPEG2000JPEG2000Markets and ApplicationsMarkets and Applications



The relation JPEG The relation JPEG JPEG2000 JPEG2000

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



JPEG2000 DevelopmentJPEG2000 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 contributorsJPEG2000 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 First steps of algorithm developmentdevelopment

• 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



JPEG2000 work planJPEG2000 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: 08/00Elevation to IS: 12/00



JPEG2000 work planJPEG2000 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: ?



SStatus of tatus of existing existing implementationsimplementations

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 JPEG2000 Features in Part II

• 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 examplesSome examples

JPEG2000 JPEG2000 versus versus

JPEG baselineJPEG baseline

T



JPEGJPEG at 0.125 bpp at 0.125 bpp



JPEG2000JPEG2000 at 0.125 bpp at 0.125 bpp



JPEGJPEG at 0.25 bpp at 0.25 bpp



JPEG2000JPEG2000 at 0.25 bpp at 0.25 bpp



JPEGJPEG at at 0.50.5 bpp bpp



JPEG2000JPEG2000 at at 0.50.5 bpp bpp



JPEG compound image 1.0 bpp



JPEG2000 compound image 1.0 bpp



Major Differences between Major Differences between JPEG and JPEG2000JPEG 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 JPEG2000 and other standards and other standards

24

26

28

30

32

34

36

38

40

42

44

46

0 0.5 1 1.5 2

bpp

PSNR (dB)

J2K R J2K NR JPEG VTC



Some lossless compression results

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

bike café cmpnd1 chart aerial2 target us average

com

pre

ssio

n r

ati

o

JPEG2000 JPEG-LS L-JPEG BZIP2



Comparison of various algorithms Comparison of various algorithms from a functionality point of viewfrom a functionality point of view

JPEG 2000 JPEG-LS JPEG MPEG-4 VTC

lossless compression performance +++ ++++ + -lossy compression performance +++++ + +++ ++++progressive bitstreams ++++ - + ++Region of Interest (ROI) coding +++ - - +arbitrary shaped objects - - - ++random access ++ - - -low complexity ++ +++++ +++++ +error resilience +++ + + +++non-iterative rate control +++ - - +genericity +++ +++ ++ ++



More in depth comparisons More in depth comparisons between between JPEG2000JPEG2000 versus other versus other

standardsstandards• « 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



JPEG2000JPEG2000

Algorithm descriptionAlgorithm description

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JPEG2000JPEG2000: : Basic encoding Basic encoding schemescheme

Wavelettransform

Codeblockpartition

Quantization Entropycoding

Rateallocation



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 EBCOT: layered bitstream formationformation

• 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 formationEBCOT layered formation

empty

emptyempty

empty

empty

empty

empty

empty

layer 5

layer 4

layer 3

layer 2

layer 1

block 1bit-stream

block 2bit-stream

block 3bit-stream

block 4bit-stream

block 5bit-stream

block 6bit-stream

block 7bit-stream



Wavelet TransformWavelet Transform

• Two filters supported – W9x7 (Floating point)

for lossy coding – W5x3 (Integer) for

lossless coding• Only dyadic

decomposition supported

Dyadic decomposition

T



QuantizationQuantization• 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” Gold: No lazy mode vs. lazy mode

0

5

10

15

20

25

30

35

40

45

50

0.0625 0.125 0.25 0.5 1.0 2.0

Bits per pixel [bpp]

PS

NR

[d

B]

No Lazy mode

Lazy mode



No lazy mode: 0.0625 bppNo lazy mode: 0.0625 bpp



Lazy mode: 0.0625 bppLazy mode: 0.0625 bpp



No lazy mode: 0.25 bppNo lazy mode: 0.25 bpp



lazy mode: 0.25 bpplazy mode: 0.25 bpp



Multi-component imageryMulti-component imagery

– up to 256 components– arbitrary dimensions/bit depths for

each component– reversible & non-reversible

component color transforms



Reversible color Reversible color transformation:transformation:

making lossless color coding making lossless color coding possiblepossible

GBVr

GRUr

BGRYr

4

*2

GVrB

GUrR

VrUrYrG

)

4(

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



Multiresolution decomposition

OriginalImage



LH1

HL1

HH1

LL1


LL1




LL2

LH1

HL1

HH1

LH2 HH2

HL2LL2




LH1 HH1

LH2 HH2

HL2

HL3

HH3LH3

LL3

HL1



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

codestream

JPEG2000: JPEG2000: ScalabilityScalability



Scalability - Progressive By Resolution












Scalability - Progressive By Accuracy









Example:Example:Progressive by resolutionProgressive by resolution

• Image: Woman• Resolution levels: 5• Decoded sizes: 1/16

1/81/41/21













Example:Example:Progressive by qualityProgressive by quality

• Image: Woman• Bitrates: 0.125 bpp

0.25 bpp0.5 bpp1.0 bpp2.0 bpp



0.125 bpp



0.25 bpp



0.5 bpp



1.0 bpp



2.0 bpp



Region Of Interest codingRegion 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 resultsROI: Some visual results

No ROI

69:1 overall compression ratio

Rectangular ROI



Regions Of Interest



Regions Of Interest



Regions Of Interest



ROI coding: mask ROI coding: mask computationcomputation



Region Of Interest codingRegion Of Interest coding• BASIC IDEA:

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 maskCreation 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

n-1 n n+1

Low High

n-1 n n+1

X:s

2n 2n+1

Inverse transform of the 5-3 filter



ROI Scaling based methodROI Scaling based method

Coefficient values

ROI Coefficients

Highest BG coeff value is found



ROI MaxShift methodROI MaxShift method

ROI Coefficients

Coefficient values

After shifting, all the ROI coefficients are larger than the largest BG coefficient



Example:Example: ROI coding 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:Example: ROI coding ROI coding

• Image: Woman• ROI: rectangular• Scaling value: MAXSHIFT• Progressive type: SNR• Bitrate: 4bpp



1.0 bpp



3.0 bpp



4.0 bpp



ROI Maxshift mode: what ROI Maxshift mode: what is the gain?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


ERICSSON RESEARCH Media Lab ROI coding: what do we ROI coding: what do we

pay?pay?Lossless image coding with Lossless image coding with

ROIROIGold: Rectangular ROI

0,99

0,995

1

1,005

1,01

1,015

1,02

No ROI

S=2

S=4

Maxshift


ERICSSON RESEARCH Media Lab ROI coding: what do we ROI coding: what do we

pay?pay?Lossless image coding with Lossless image coding with ROIROI Target - approx. 25% circular ROI - Relative sizes

0,94

0,96

0,98

1

1,02

1,04

1,06

1,08

1,1

No ROI

S=2

S=4

Max shift



Block transform codingBlock 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

T



TilingTiling (128x128, 0.25 bpp) (128x128, 0.25 bpp)



SSODWTSSODWT (128x128, 0.25 bpp) (128x128, 0.25 bpp)



Error resilience Error resilience capabilitiescapabilities• Most still image coders use Entropy Coding

• Variable Length Coding is known to be prone to channel or transmission errors– Loss of synchronization

CHeader

Residual

DCHANNEL

Bit errors (Noise)Burst errors (Fading)



• 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

Error resilienceError resilience



Visual Frequency Visual Frequency WeightingWeighting• 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 Visual Frequency Weighting (cont.)(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 transformsLine 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 transformsLine 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 FormatFile 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 IJPEG2000 Part I

Core Coding System

• Schedule– March 2000, FCD – September 1, FDIS– December 2000, IS

• Only editorial changes allowed

• File extension, .jp2

C



JPEG2000 Part IIJPEG2000 Part IIExtensions• Schedule

– March 2000, WD– September 2000, CD – December 2000, FCD– April 2001, FDIS– July 2001, IS

• File extension .jpx



JPEG2000 Part IIIJPEG2000 Part IIIMotion-JPEG2000• 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 VJPEG2000 Part VReference 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 IVJPEG2000 Part IV

Compliance Tests

• Schedule– July 2000, WD– December 2000, CD – March 2001, FCD– July 2001, FDIS– November 2001, IS



JPEG2000 Part VJPEG2000 Part V

Reference software

• Schedule– July 2000, CD– December 2000, FCD – March 2001, FDIS– July 2001, IS



JPEG2000 Part VIJPEG2000 Part VI

Compound Image File FormatCompound Image File Format

• Schedule– August 2000, CD– December 2000, FCD – March 2001, FDIS– July 2001, IS



JPEG2000 Part VIIJPEG2000 Part VII

Technical reportTechnical report

• Schedule– December 2000, PDTR – March 2001, DTR– July 2001, TR



ConclusionsConclusions

• 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 informationMore information• JJ2000

– http://jj2000.epfl.ch

• JPEG Web site:– http://www.jpeg.org

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

• SPEAR– http://spear.jpeg.org/



Contact usContact us for any further for any further informationinformation

• Touradj Ebrahimi– [email protected]

• Charilaos Christopoulos– [email protected]



AcknowledgementsAcknowledgements• 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 ...

**

* In alphabetical order* In alphabetical order

JPEG2000

Documents

image compression standard

current jpeg standard

jtip jpeg

japan jpeg

image quality

lossy compression

lossless compression

rate compression