Argyll Overview

From 22f703cab05b7cd368f4de9e03991b7664dc5022 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=B6rg=20Frings-F=C3=BCrst?= Date: Mon, 1 Sep 2014 13:56:46 +0200 Subject: Initial import of argyll version 1.5.1-8 --- doc/Source.html | 219 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 219 insertions(+) create mode 100644 doc/Source.html (limited to 'doc/Source.html') diff --git a/doc/Source.html b/doc/Source.html new file mode 100644 index 0000000..5ea423e --- /dev/null +++ b/doc/Source.html @@ -0,0 +1,219 @@ + + + + Argyll Overview + + + +

Source Code Documentation

+Some general comments about how different software works.
+
+TARGET
+
+ Nearly all current Color correction systems generate test charts +(or device characterization target charts) by laying out a regular +rectangular grid of test points in device space (Targen will do this if +you feed it a non-zero -m +option). On some consideration, this approach is far from optimal. Not +only is a regular grid inefficient in packing the multidimensional +device space but if the points are spaced evenly in device space, they +will probably not be optimal in determining the underlying device +characteristic, sampling too finely where the device behavior is +smooth, and too coarsely where the device behavior changes rapidly. +Some commercial color systems tackle +the latter problem by "pre-linearizing" the device, which amounts to +distorting +the regular device space grid points with a perceptual inverse per +device +channel lookup curve.
+
+The approach I have taken with Argyll, is a little different. Both a +device independent and device dependent approach are available. In the +device independent mode, test points are distributed so as to minimize +the distance from any point in the device space to the nearest test +chart value. When the device dependent approach is used, test points +are chosen so as to minimize the expected error between the resulting +profile and the underlying device response.
+
+For higher dimensional spaces, where the aim is to create a more +approximate device profile, I've used an "incremental far point" point +generator, that starting with a previous test point as a seed, use a +minimization algorithm to locate another point that is as far as +possible from the nearest existing test point in perceptual space, +while remaining in gamut at all tines. This means that ideally each +point "fills in" the gaps in the existing distribution.
+
+Another issue with laying test points out in regular grids, is that +this means that the device response is poorly sampled (since the grids +are usually coarse), and this can make it impossible to create detailed +device linearisation "shaper" curves from the resulting data ! Ideally, +in any colorspace (input or output), when viewed from any possible +angle, none of the test data points should appear to line up. The +Argyll target generator seems to achieve this goal.
+
+
+target/printtarg.c:
+
+printtarg simply generates a PostScript file containing the patches +laid out for an Xrite DTP51/DTP41/SpectroScan. It allows them to be +laid out on +a choice of paper sizes, with the appropriate contrasting color spacers +between +each patch for the strip reading instruments. Unlike other charts, +Argyll +charts are generated as required, rather that being fixed. Also unlike +most +other strip reading charts, the spacers may colored, so that the +density contrast +ratio is guaranteed, even when two patches are about 50% density.
+
+Another feature is the pseudo random patch layout. This has three +purposes. One is to try and average out any variation in the device +response in relationship to the location of the patch on the paper. +Color copiers and printing presses (for instance), are notorious in +having side to side density variations.
+
+Another purpose of the random patch layout, is that it gives the +reading program a good mechanism for detecting user error. It can guess +the expected values, compare them to the readings, and complain if it +seems that the strip is probably the wrong one.
+
+The final purpose of the random patch layout is to optimize the +contrast +between patches in a strip, to improve the robustness of the strip +reading. +Using this, small charts may be even be generated without any gaps +between +the test patches.
+
+RSPL
+
+A lot of core Argyll algorithms are bound up in the RSPL (Regular +Spline) class. The RSPL class is closely related to the CLUT table +structure used in the ICC profile format, and is a general purpose way +of mapping one multi-dimensional value to another, using interpolation +to reduce the mapping function to a manageable size.
+Regular splines are not directly related to other types of splines, and +do not generally suffer from the sort of "wiggles" and "overshoot" +characteristic of other spline systems.
+
+The RSPL class adds three basic methods to the underlying data +structure:
+
+1) Interpolation
+
+2) Creation from scattered data
+
+The regular spline implementation was inspired by the following +technical reports:
+
+D.J. Bone, "Adaptive Multi-Dimensional Interpolation Using Regularized +Linear Splines," Proc. SPIE Vol. 1902, p.243-253, Nonlinear Image +Processing IV, Edward R. Dougherty; Jaakko T. Astola; Harold G. +Longbotham;(Eds)(1993).
+
+D.J. Bone, "Adaptive Colour Printer Modeling using regularized linear +splines," Proc. SPIE Vol. 1909, p. 104-115, Device-Independent Color +Imaging and Imaging Systems Integration, Ricardo J. Motta; Hapet A. +Berberian; Eds.(1993)
+
+Don Bone and Duncan Stevenson, "Modelling of Colour Hard Copy Devices +Using Regularised Linear Splines," Proceedings of the APRS workshop on +Colour Imaging and Applications, Canberra (1994)
+
+see <http://www.cmis.csiro.au/Don.Bone/>
+
+Also of interest was:
+
+"Discrete Smooth Interpolation", Jean-Laurent Mallet, ACM Transactions +on Graphics, Volume 8, Number 2, April 1989, Pages 121-144.
+
+3) Inversion
+
+Simplex interpolation is normally done in Baricentric space, where +there +is one more baricentric coordinate than dimensions, and the sum of all +the +baricentric coordinates must be 1.
+To simplify things, we work in a "Simplex parameter" space, in which +there are only <dimension> parameters, and each directly +corresponds with a cartesian coordinate, but the parameter order +corresponds with the baricentric order.
+For example, given cartesian coordinates D0, D1, D2 these should be +sorted from smallest to largest, thereby choosing a particular simplex +within a cube, +and allowing a correspondence to the parameter coordinates, ie:
+
+D1 D0 D2     Smallest -> Largest cartesian sort
+P2 P1 P0     Corresponding Parameter coordinates +(note reverse order!)
+
+B0 = P0      Conversion to Baricentric +coordinates
+B1 = P1 - P0
+B2 = P2 - P1
+B3 = 1 - P2
+
+The vertex values directly correspond to Baricentric coordinates,
+giving the usual interpolation equation of:
+
+ VV0 * B0
++ VV1 * B1
++ VV2 * B2
++ VV3 * B3
+
+where VVn is the vertex value for each of the 4 vertices of the simplex.
+
+Reversing the Parameter -> Baricentric equations gives the
+following interpolation equation using Parameter coordinates:
+
+ VV0 - VV1 * P0
++ VV1 - VV2 * P1
++ VV2 - VV3 * P2
++ VV3
+
+It is this which is used in rev.c for solving the reverse interpolation +problem. Within a simplex, only linear algebra is needed to compute +inverses. To deal with the 4D->3D nature of a CMYK profile, the SVD +(Singular Value Decomposition) approach is used to solving a CMYK for a +given CIE value, the result being the equation of the line of +solutions. The lines from adjacent simplexes form a solution locus, +that can be resolved into a single point solution once a black +generation rule is applied.
+
+Outline of inversion processing:
+
+     Basic function requirements: exact, +auxil, locus, clip inversion.
+     Fwd cell - reverse cell list lookup
+     Basic layout di -> fdi + auxils + ink limit
+     Basic search strategy
+     Sub Simplex decomposition & properties
+     How each type of function finds solutions
+       Sub-simplex dimensionality & +dof + target dim & dof
+       Linear algebra choices.
+     How final solutions are chosen
+
+XICC
+
+    Profiling using concatenated shapers to augment thin +plate splines for per channel curves.
+
+IMDI
+
+This module generates C code routines which implement an integer +multi-channel transform. The input values are read, passed through per +channel lookup tables, a multi-dimensional interpolation table, and +then a per channel output lookup table, before being written.
+
+ MPP
+
+Outline model parameter optimization using slope accelerated error +metric, and initial parameter speedup for adjacent spectral bands etc.
+
+
+
+ + -- cgit v1.2.3