9c1c2480-9f02-46d3-af92-f58e2809ab25 12 15 12 decimal

&[DATE] &[TIME] - &[FILENAME]

8 band RGB rainbow colors 25500255128025525500255002006475752550012864012800093 mat :

. . . RGB color map . . ..

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x 032649612816019222425691 mat :

Vector 'x' is the linear interpolator over each of the 'colors', i.e: over the 8 band RGB basis rainbow. Next, we will create the complete 'rainbow' colormap from interpolation. There are lot of "colormap" for specific rendering of 3D plots [typical MCD].

R colors 1 col : G colors 2 col : B colors 3 col : 31 line

j 1255 range band j 1 + 1 el j : band j 1 + 2 el x R j linterp Floor : band j 1 + 3 el x G j linterp Floor : band j 1 + 4 el x B j linterp Floor : band 12 el 255 : 51 line for band 21 line

The respective RGB traces R band 1 col band 2 col augment : G band 1 col band 3 col augment : B band 1 col band 4 col augment : 31 sys

band 2 col sum 256 / 97

band 3 col sum 256 / 114

band 4 col sum 256 / 72

.....
'n' width band band transpose : n 12 : 21 sys

data ListCollect collect row/wise 'n' data ct 1 : i 1 n range rainbow ct el data 1 row : ct ct 1 + : 21 line for rainbow nest the collection v rainbow 1 el : j 2 rainbow rows range v v rainbow j el stack : for v 81 line :

r band 2 row : g band 3 row : b band 4 row : 31 line

Red r ListCollect : Green g ListCollect : Blue b ListCollect : 31 line

rainbow Red Green Blue 13 mat :

gray Red Green + Blue + 3 / :

Optical illusion: horizontal= vertical = 256 pixels

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Rainbow RGB bands ...

B R G 3 1 sys B R G 3 1 sys

The relative perception of the brightness of R,G,B of average person has been normalised NTSC => 0.299*Red+0.587*Green+0.114*Blue = 1. At the image capture, the gray level is pro- portioned to this NTSC ratio to produce the 3 RGB components. The NTSC conversion is done on the *.raw image, quantisation follows. Even if the sky looks blue, there is little blue in nature, not much red, nature is mostly green to human eye.

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red

Red 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

green

Green 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

blue

Blue 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

gray

97256 / 0.379

114256 / 0.445

72256 / 0.281