The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
To be valuable, this representation has to report accurately the 
quality of the image and the performance that one can expect 
from a particular application whatever the image is. However, 
most degradation effects are sensitive to the image. It is easy to 
understand that a spatial smoothing will have a greater influence 
on a city image than on a uniform area (Fig. 7). 
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Figure 5: Quality for different values of white noise on three dif 
ferent images. 
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Figure 6: Quality for different rates for JPEG 2000 on three dif 
ferent images. 
When considering spectral smoothing, the same conclusion holds. 
Moffett3 and harvardl images have more high frequency compo 
nents than mojfett4. The spectral content of harvardl is com 
pletely different from moffettS and mojfett4. Thus, the quality 
figures differ (Fig. 8). However, as we can expect the influence 
of the smoothing on applications to be different also, this is not 
surprising. 
It is important to notice that the representation is quite robust for 
the three different images moffett3, mojfett4 et harvardl for the 
white noise and JPEG 2000 degradations (Figs. 5 and 6). 
3 QUANTITATIVE EVALUATION 
3.1 Distance between degradations 
The ideal situation to reach, the Grail of the quality criteria would 
be to infer precisely the degradation impact on application know- 
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Figure 7: Quality for different values of spatial smoothing on 
three different images. 
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Figure 8: Quality for different values of spectral smoothing on 
three different images.