PDFlib Text Extraction Toolkit (TET) Manual

7 Image Extraction
7.1 Image Extraction Basics
Image formats. TET extracts raster images from PDF pages and stores the extracted
images in one of the following formats:
> TIFF (.tif) images will be created in the majority of cases. Most TIFF images created by
TET can be used in the majority of TIFF viewers and consumers. However, some advanced
TIFF features are not supported by all image viewers. Note that the Windows
XP image viewer does not support the common Flate compression method in TIFF.
We regard Adobe Photoshop as benchmark for the validity of TIFF images.
> JPEG (.jpg) will be created for images which are already compressed with the JPEG algorithm
(DCTDecode filter) in PDF. However, in some cases DCT-compressed images
must be extracted as TIFF since not all aspects of PDF color handling can be expressed
in JPEG.
> JPEG 2000 (.jpx) will be created for images which are already compressed with the
JPEG 2000 algorithm (JPXDecode filter) in PDF.
Placed images and image objects. TET distinguishes between placed images and image
objects. A placed image corresponds to an image on a page. A placed image has geometric
properties: it is placed at a certain location and has a size (measured in points,
millimeters, or some other absolute unit). The image is usually visible on the page, but
in some cases it may be invisible because it is obscured by other objects on the page, is
placed outside the visible page area, is fully or partially clipped, etc. Placed images are
represented by the PlacedImage element in TETML.
An image object is a resource which represents the actual pixel data, colorspace and
number of components, number of bits per component, etc. Unlike placed images, image
objects don’t have any intrinsic geometry. However, they do have width and height
properties (measured in pixels). Each image object has a unique associated ID which can
be used to extract its pixel data. Image objects are represented by the Image element in
TETML.
A placed image always corresponds to exactly one image object, but there is no oneto-one
relationship. For example, consider an image for a company logo which is used
repeatedly on the header of each page in the document. Each logo on a page constitutes
a placed image, but all those placed images may be associated with the same image in
an optimized PDF. On the other hand, in a non-optimized PDF each placed logo could be
based on its own copy of the same image object. This would result in the same visual appearance,
but a more bloated PDF document.
Page-oriented and document-oriented image extraction. The distinction between
placed images and image objects gives rise to two fundamentally different approaches
to image extraction:
> Page-oriented image extraction: the application is interested in the exact page layout,
but doesn’t care about duplicated images. Extracting images in a page-oriented
way may result in the same data for more than one extracted image. However, the
application can avoid image duplication by checking for duplicate image IDs.
7.1 Image Extraction Basics 81