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 be slightly worse than 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!!! 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 SamplesSamples of various compression solutions