  Capable of automated near real time superpixel detection and quality
assessment in an uncalibrated monitor typical red green blue (RGB) image,
depicted in either true or false colors, an original low level computer vision
(CV) lightweight computer program, called RGB Image Automatic Mapper (RGBIAM),
is designed and implemented. Constrained by the Calibration Validation (CalVal)
requirements of the Quality Assurance Framework for Earth Observation (QA4EO)
guidelines, RGBIAM requires as mandatory an uncalibrated RGB image pre
processing first stage, consisting of an automated statistical model based
color constancy algorithm. The RGBIAM hybrid inference pipeline comprises: (I)
a direct quantitative to nominal (QN) RGB variable transform, where RGB pixel
values are mapped onto a prior dictionary of color names, equivalent to a
static polyhedralization of the RGB cube. Prior color naming is the deductive
counterpart of inductive vector quantization (VQ), whose typical VQ error
function to minimize is a root mean square error (RMSE). In the output multi
level color map domain, superpixels are automatically detected in linear time
as connected sets of pixels featuring the same color label. (II) An inverse
nominal to quantitative (NQ) RGB variable transform, where a superpixelwise
constant RGB image approximation is generated in linear time to assess a VQ
error image. The hybrid direct and inverse RGBIAM QNQ transform is: (i) general
purpose, data and application independent. (ii) Automated, i.e., it requires no
user machine interaction. (iii) Near real time, with a computational complexity
increasing linearly with the image size. (iv) Implemented in tile streaming
mode, to cope with massive images. Collected outcome and process quality
indicators, including degree of automation, computational efficiency, VQ rate
and VQ error, are consistent with theoretical expectations.
