The following list of criteria was obtained from the following sources:
- Eadie, M. (2005). Preservation Handbook - Bitmap (raster) images. London: Arts
and Humanities Data Service.
(http://ahds.ac.uk/preservation/Bitmap-preservation-handbook_d6.pdf)
- Rauch, C., Rauber, A., Hofman, H., Bogaarts, J., Vedegem, R., Pavuza, F., et al.
(2005). A Framework for Documenting the Behaviour and Funcionality of Digital
Objects and Preservation Strategies. Glasgow: DELOS Network of Excellence.
(http://www.dpc.delos.info/outputs/index.php)
Groups criteria related to the appearance of the digital object.
Groups criteria related to the resolution of the the digital image
The width of the digital image measured in
pixels.
The height of the digital image measured in
pixels.
Groups criteria related to the color of the the digital image
A color model is an abstract mathematical model describing the
way colors can be represented as tuples of numbers, typically as three
or four values or color components (e.g. RGB, sRGB, HSL, HSV, YUV and
CMYK are color models) [wikipedia].
A computer graphics term describing the number of bits used to
represent the color of a single pixel in a bitmapped image or video
frame buffer. This concept is also known as bits per pixel (bpp),
particularly when specified along with the number of bits used. Higher
color depth gives a broader range of distinct colors [wikipedia].
Comprises criteria that are related to the intellectual content carried by the
digital object.
Comprises criteria related to the completeness of the content that
constitute the digital object.
How well pixels are respectfull to the original image. In cases
of multiple page images, the comparison is performed by page and an
overall similarity value is calculated by averaging the whole page set
results.
The number of pages that constitute the image.
Comprises information that describes the context that surrounds the object,
usually resolved by resorting to metadata.
Some image formats embed metadata. This criterion aims at measuring how
much of that metadata was preserved.
Groups together criteria that are related to the structure of the object.
is the application of data compression on digital images. In effect,
the objective is to reduce redundancy of the image data in order to be able to
store or transmit data in an efficient form. Image compression can be lossy or
lossless. Lossless compression is sometimes preferred for artificial images such
as technical drawings, icons or comics. This is because lossy compression
methods, especially when used at low bit rates, introduce compression artifacts.
Lossless compression methods may also be preferred for high value content, such
as medical imagery or image scans made for archival purposes. Lossy methods are
especially suitable for natural images such as photos in applications where
minor (sometimes imperceptible) loss of fidelity is acceptable to achieve a
substantial reduction in bit rate. [wikipedia]
Image compression can be lossy or lossless. Lossless
compression is sometimes preferred for artificial images such as
technical drawings, icons or comics. This is because lossy compression
methods, especially when used at low bit rates, introduce compression
artifacts. Lossless compression methods may also be preferred for high
value content, such as medical imagery or image scans made for archival
purposes. Lossy methods are especially suitable for natural images such
as photos in applications where minor (sometimes imperceptible) loss of
fidelity is acceptable to achieve a substantial reduction in bit rate.
Examples of lossless image compression methods are: Run-length encoding,
Entropy coding and Adaptive dictionary algorithms such as LZW. Examples
of methods for lossy compression are: Reducing the color space to the
most common colors in the image; Chroma subsampling; Transform coding
and Fractal compression. [wikipedia]
The level compression used in the object. A value of zero
should be used when lossless compression methods are in
place.