; XXXIX-B3, 2012
lake.tif (400?).
al view.tif (400?).
aximum | Minimum
fference | difference
6 8
23 29
35 34
39 46
uirements needed to
ng contour increments
'S
Minimum
difference
aximum
fference
7
21
37
39
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| view using contour
ensity values.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
6. COLOUR IMAGES
When processing colour images, the same concepts above hold
true. However, instead of a single grey-scale intensity value,
colour digital images have pixels that are generally quantised
using three components (i.e. Red, Green and Blue). In general,
all image processing operations can be extended to process
colour images simply by applying them to each colour
component (Bovik, 2007).
Each of the three RGB components are processed, encoded and
reconstructed separately as if they were three different grey
scale images (Duperet, 2002). The results of the three
reconstructions are then merged or fused to recreate the original
colour image. In terms of image quality (R.M.S.) memory
requirements the results from the contouring approach, for the
same classes of images (i.e. faces, landscapes and aerial views),
were similar to those obtained from the grey-scale imagery
described above.
7. CONCLUSIONS
e Generating contour nodes from digital images involves
assigning coordinates values to pixels in the raster format,
and interpolating between the pixels to find the coordinates
of points in the path of a contour having the same grey-
scale intensity value. This enables the contour nodes to be
found to sub-pixel accuracy if required.
e The conversion of certain classes of digital images into
contour maps may be used to compress and reconstruct
images in pixel format that are more accurate and with
improved visual details than JPEG compressed versions of
the same image, while requiring similar memory space for
storage and speed of transmission over digital links.
o For the images investigated in this work, the contour
approach to image compression requires contour data to be
filtered and discriminated from the reconstruction process.
® Spline interpolation was used to reconstruct digital images
from the nodes of their contour representations. The
process involves determining the pixel intensity value
which would exist at the intersections of a regular grid
using the nodes of randomly spaced contours.
* Refinements to the proposed method are being undertaken
to increase the accuracy achievable for a variety of scenes
and dynamic ranges (including bi-tonal imagery).
* More research is required to assess the accuracy of the
compression process in the presence of added random
noise, a variety of image scenes with various levels of
details and/or video imagery.
* Further tests are required to determine whether a binary
coding of the contour data may have an impact on memory
requirements.
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