Compression of the image

Adolf Knoll

National Library of the Czech Republic

General schemes for application of compression

The schemes adapt to the character of the represented objects:

Bitonal image (1-bit, black-and-white) Colour photorealistic image Mixed document (two above-mentioned

components)

Trends

Bitonal from CCITT Gr. Fax 3 and 4 to JBIG variants

Photorealistic Lossless compression: PNG, TIFF/LZW Lossy: from JPEG DCT to wavelet

Mixed document Both applied (Mixed Raster Content –

usually vertically)

How is it built into formats?

Trying to have it in ISO TIFF (even JPEG, LZW, or PNG) – but it is not enough due to lack of tools for conversion and display.

That is why the other more suitable formats are used: JPEG, PNG

That is why there is a lot of development in the area of mixed formats – they do not aim to become ISO

Relevant directions

Bitonal image JBIG2 (ISO) – no support (exc. Xerox), but

many similar activities Photorealistic image

wavelet JPEG2000 and many other non-ISO initiatives (WI, LWF, IW44, SID, Imagepower IW, …)

Mixed content DjVu, LDF, Imagepower MRC

Aims

Image Archiving standardized

archival format (TIFF, JPEG, PNG, …)

Image Delivery More efficient

modern format (JB2, MrSID, DjVu, LDF, …)

Which relationship will be between both of them?It will be defined by the goal of the project.

Around compression

Pre-processing of the image Compression Encoding in a format De-coding from the format De-compression Display – print-out

Pre-processing of the bitonal image - I

Efficient schemes are built on possibilities to apply vocabularies of pixel chunks/groups: E.g. a text is an image that can be interpreted as

several dozens of images of letters, while the repeated occurrence of each letter can be represented by its coordinates (x,y) and reference to a dictionary in which there is only one representation of similar letters (digitized only once as a bitmap)

This method is called PATTERN MATCHING, but…

Pre-processing of the bitonal image - II

However, scanned texts have a lot of information noise in individual pixel chunks representing, for instance, letters in text

Therefore, it is convenient to reduce differences between identically indentifiable chunks smoothing pixel flipping noise removal

Smoothing and pixel flipping

Problems in pattern matching

Česká republika

Low quality original and/or scan + inappropriate processing

Soft pattern matching

Better work with dictionaries; replacement only there, where the threshold value of the pixel chunk is satisfied

If not, the whole small bitmap is stored Tuning of these mechanisms is a key

to successful application of the lossy compression of a bitonal image.

How to know…

Libraries have documents of various qualities- also very bad

These documents are more difficult to process than good samples presented by software producers

Tests… tests… tests… on typical materials

Bitonal compression

Lossless (LZW, PNG, …, CCITT Fax Group 3 a 4, JB2, JBIG, JBIG2, Algo Vision/Luratech (1-bit LDF component)

Lossy modern schemes: AT&T (Lizardtech) (JB2) – soft pattern

matching ImagePower Inc. JBIG2 (JB2) – only pattern

matching Summus Inc. (Lightning Strike), ...

GIF would beslightly worsethan PNG

Květy české – 19th century Czech journal

Impact of the quality of digitized originals on performance of compression schemes

JB2

Most efficient compression schemes JB2 from the DjVu format (AT&T).

It enables compression: lossless lossy aggressive – while preserving high

quality

JB2 as a component part of the DjVu format

More files can be merged and saved into one (as PDF) – they have the common dictionary so that together their size will be smaller than the sum of all individual files

More files can be virtually joined (they are called one after another from the server)

More advantages: display, references, OCR, … (DjVu plug-in)

Expensive or free software for Linux or Solaris

Samples and résumé

Monitor and test new approaches for image processing

They can be very suitable for document delivery services Image servers Scanned content CLICK!!!

Which formats to use for bitonal image?

If you have no special tools: GIF

If you wish smaller files, use PNG Both are recommended for WWW However, TIFF/CCITT Fax Gr. 4 is

better Use DjVu, if you wish very small files

Problems

Good image editing software does not support TIFF with Gr. 4 encoding

Display possible within normal Windows tools

GIF and PNG support also higher brightness resolution (8-bit / 24-bit) – take care not to save bi-level image in higher image depth

DjVu – necessary to solve authoring software problem

Lossy compression – bitonal image

Compression of colour images

Lossless LZW

GIF (8-bit only) TIFF (5.0)

PNG Wavelet

JPEG2000 (JP2) …

Lossy DCT (JPEG) Fractals Wavelet

IW44 LWF, WI JPEG2000 (JP2) MrSID, …

Classical (LZW, RLE, DCT) versus wavelet approaches.

True colour image

DCT

wavelet

Testing compression efficiency

Sample Reference Full-colour (JPEG, wavelet) 1-bit (establish tresholds – Paint Shop

Pro, LuraWave) MRC (same sample – DjVu Solo)

Compression efficiency – bitonal image

Compression efficiency

True colour

How to apply compression?

It depends on the character of objects in the image: Photorealistic image (JPEG, wavelet) Text and simple blac-and-white graphics (Fax

Group 4, JB2, …) Colour graphics (problem to compress with losses

– better lossless PNG or GIF – application area of vector graphics - SVG)

Mixed content (composed solutions: DjVu, LDF, …)

The most efficient solution

To segment images into two or more groups of objects:

1. Objects good for bitonal conversion

2. Objects good for true colour representation

Tto compress each group separately and then merge into one format.

Horizontal segmentation/zoning

Horizontally- Text- Grafics- Photographs

Imagepower Inc.

Vertical segmentation/zoning

Vertically Foreground Background

Lizardtech Inc. (AT&T)Luratech GmBH

DjVu, LDF

Comparison of DjVu and LDF

DjVu

6 layers

Foreground: JB2 IW44

Background: 4 layers IW44

LDF

3 layers

Foreground: LDF 1-bit Comp. LFW

Background: 1 layer LWF, JP2

Bitonal versus composed image

Grey level

Other DjVu properties

More images in one: as TIFF, PDF, LDF, …, with use of the

common dictionary of pixel chunks Virtually: pages remaion on server and

only that page that is called is delivered

Multiresolution image

MrSID In one file several (up to 8) images in

various resolutions Sample Efficient with an image server

SAMPLES

Samples of various compression solutions