WO1997031337A1 - Procede de conception de palettes et de table de couleurs - Google Patents

Procede de conception de palettes et de table de couleurs Download PDF

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Publication number
WO1997031337A1
WO1997031337A1 PCT/US1997/002662 US9702662W WO9731337A1 WO 1997031337 A1 WO1997031337 A1 WO 1997031337A1 US 9702662 W US9702662 W US 9702662W WO 9731337 A1 WO9731337 A1 WO 9731337A1
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color
region
image
colors
list
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PCT/US1997/002662
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English (en)
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Karl L. Denninghoff
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Denninghoff Karl L
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Priority to AU20532/97A priority Critical patent/AU2053297A/en
Publication of WO1997031337A1 publication Critical patent/WO1997031337A1/fr

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    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
    • G09G5/00—Control arrangements or circuits for visual indicators common to cathode-ray tube indicators and other visual indicators
    • G09G5/02—Control arrangements or circuits for visual indicators common to cathode-ray tube indicators and other visual indicators characterised by the way in which colour is displayed
    • G09G5/06—Control arrangements or circuits for visual indicators common to cathode-ray tube indicators and other visual indicators characterised by the way in which colour is displayed using colour palettes, e.g. look-up tables
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/001—Texturing; Colouring; Generation of texture or colour
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
    • G09G5/00—Control arrangements or circuits for visual indicators common to cathode-ray tube indicators and other visual indicators
    • G09G5/02—Control arrangements or circuits for visual indicators common to cathode-ray tube indicators and other visual indicators characterised by the way in which colour is displayed

Definitions

  • TITLE METHOD FOR COLOR PALETTE DESIGN AND LOOK-UP
  • This invention relates to methods for producing color images on displays and, more particularly, to such methods which use quantization of images.
  • quantization of images suffers in quality and poor performance.
  • the problem is sufficiently acute that high quality in 256 or fewer color images has often been obtained routinely by touching up the images using artists after some tool has performed an initial quantization.
  • the algorithmic solutions have produced poor quality quantized images regardless of speed.
  • contouring where the human eye perceives transition lines between quantized colors that do not appear in the original image
  • the literature for contouring solutions involves attempts to repair the deficiency by adjusting the color palette selection, which causes poor color fidelity; or it uses randomness heavily in the dithering or error diffusion transformation, which further dulls lustrous surfaces in the image.
  • contouring problem has not been solved better in the past is that existing dithering and error diffusion schemes have not been able to eliminate it without a too severe loss of resolution, i.e., the quantized images become relatively fuzzy or grainy. This usually occurs by using too much randomness in the transformation. Randomness breaks up artifact contour lines but it also breaks up features that actually appear in the original image. As a result, at least a partial remedy has in the past been sought in the palette selection step.
  • Solving contouring in the palette selection step involves allocating more colors to areas where contouring is most apparent, such as slowly transitioning smooth areas of an image. However, this is exactly opposite to what should be done based on human color perception characteristics. There are highly non-linear characteristics of the human eye in detecting differences in color. It increases considerably faster than the difference squared. Thus, smoothly transitioning areas of an image requires fewer colors to give the lowest overall error where the error takes into account these characteristics of human color perception.
  • a quantization method is needed that allows the selection of color palettes solely on the basis of human color perception and which eliminates contours, maintains a sharp image, and preserves lustre.
  • a color bitmap for computer display typically uses far more than 256 distinct colors, but the most commonly used PC display modes are restricted to only 256 distinct simultaneously displayed colors. Thus, images are necessarily distorted when displayed on these color restricted machines. Image processing for display on such machines attempts to minimize or even eliminate the human perceivable distortion.
  • the image that the user sees is called a quantized version of the original image.
  • Usually between 200 and 236 of the 256 colors can be selected arbitrarily from about 16 million colors; the selection of precisely which of those 16 million colors are used to display a particular image has pronounced effects on the amount of distortion seen in the displayed image.
  • a set of colors selected to minimize distortion for a particular image is called an optimal palet te for that image. Problems with existing optimal palette selection include slow speed and poor quality of the final image due to optimal palette selection on the basis of criteria that are neither matched well to human color perception capabilities nor to the mechanism used to map the image to a quantized version using the optimal palette.
  • the image must be transformed into a quantized version using that palette.
  • the best quantized image to the human eye is usually not the image obtained by replacing each original pixel color with the nearest color from the palette. Indeed, two pixels having the same color in the original image will often have different colors in a well quantized version of the image.
  • Dithering and error diffusion are two broad classes of schemes for performing such transformation. All such schemes only partially correct for the unavoidable color distortion in a pixel by correcting for that distortion in nearby pixels. Such schemes can only be partially effective since pixels are sufficiently large that the human eye does not always perceive the average of the nearby colors and may perceive loss of resolution or a relative fuzziness in the image.
  • Lustre has not been maintained in the existing dithering and error diffusion transformations because both are relatively clumsy in correcting for color error.
  • a smooth surface has a texture that needs to be maintained. Simple attempts to correct for color error can destroy that texture and this is especially true of lustrous surfaces. To the human eye, it is often better to maintain the texture with perhaps a slight loss of color fidelity. Dithering simply averages colors on a local basis over pre-determined blocks of pixels without regard to texture and thereby dulls lustrous surfaces.
  • Error diffusion schemes do not take into account the fact that it is very important to correct for a color error only near the error itself, i.e., existing error diffusion schemes propagate error an unlimited distance from the pixel whose quantization created the error. This results in gratuitous matting of lustrous surfaces and actually reduces the human perceived color fidelity. When randomness is used too heavily to break un contour lines, it also mattes lustrous surfaces.
  • a color look-up table is a solution to the more general problem of finding the nearest point in a target set of points to each of many points in a multidimensional space. For colors, three dimensions are usually used. Computing nearest points, in general, can often be greatly accelerated by using a color look-up table which reduces each nearest point computation to simply indexing into an array.
  • a color look-up table is typically an array where the indexes into the array represent a grid of points in the space and the entries in the array are the nearest points to the corresponding grid point from the target set. These are called Voronoi labeled grids after the elegant Voronoi techniques for finding nearest points.
  • An advantage of "on-the-fly" image quantization is that only high color resolution images need be kept with an application.
  • the high resolution images can be quantized whenever necessary for display on the low end machines. This allows the application to serve both the high end market and low end market with the same set of images. It therefore also reduces storage space for the application and production costs for the application creators. Note that it is much better to sell applications that work in several color modes. Modern machines usually have modes that allow high color resolution and other modes, usually with higher numbers of pixels, that have limited palettes. Applications normally must work in all such modes.
  • the method of color quantization of images has incorporated therein a novel method for high speed color palette selection, a novel method for creating at high speed color look-up tables, and a novel method of high speed error diffusion.
  • the method includes provisions for the intergration of a method of stretching. More specifically, the method of image quantization includes the following steps: a) creating an optimal palette for an image using full three-dimensional deviation and capable of assigning fewer colors to smooth transitional regions of the images; b) constructing a color look-up table to efficiently map arbritrary colors to the nearest colors of the optimal palette using a sweep-list method; and, c) mapping an image to a color quantized image using an over-compensated and adaptively damped error diffusion method.
  • Fig. 1 is a control flow diagram of the method of color quantization with closely integrated stretching.
  • Fig. 2 is a control flow diagram of the method of color quantization with loosely integrated stretching.
  • Fig. 3 is a control flow diagram of the method of color quantization with stretching performed prior to the step of quantization.
  • Fig. 4 is a control flow diagram of a method for optimal palette selection.
  • Fig. 5 is a control flow diagram of a method for creating a color look-up table.
  • Fig. 6 is a control flow diagram of the sweep list method referred to in Fig. 5.
  • Fig. 7 is a control flow diagram of a method for over- compensating and adaptively damping color error diffusion to quantize an image.
  • Fig. 8 is a diagram of the error proprogation pattern of the error diffusion method disclosed herein.
  • Figs. 1-8 Shown in the accompanying Figs. 1-8, there is shown a general method of color quantization. Incorporated in the general method are several novel methods for high speed color palette selection, efficiently creating a color look-up table, and high speed error diffusion.
  • Figs. 1-3 show control flow diagrams of the overall method of image color quantization.
  • a decompressed image is used. It should be understood that any image may be used in the method.
  • an optional stretching step is also provided. Further, palettes that are not optimal may be used.
  • the method of image color quantization includes the following steps: a) creating an optimal palette for an image using full three-dimensional deviation and capable of assigning fewer colors to smooth transitional regions of the images steps 3, 9 and 16; b) constructing a color look-up table to efficiently map arbritrary colors to colors of said optimal palette using a sweep-list method steps 4, 10 and 17; and, c) efficiently mapping an image to a color quantized image using the color look-up table and an over-compensated and adaptively damped error diffusion method steps 5, 12 and 18.
  • the high speed optimal palette selection is matched both to human color perception capabilities and to the error diffusion method disclosed herein. Further, contouring is not corrected in the palette selection step. In fact, the optimal palettes selected by this method would increase contouring artifacts if used with existing dithering or error diffusion schemes.
  • the quality of the optimal palettes selected is accomplished by making color palette selection solely on the basis of fundamental characteristics of human color perception. So in particular, this palette selection method does not use more colors for smoothly transitioning areas of an image to control contouring artifacts. This method actually does the opposite and allocates fewer colors to gently transitioning flat areas of the image than any pre ⁇ existing technique. However, this exacerbates and passes the problem of eliminating contour artifacts to the error diffusion method.
  • the preferred embodiment of the optimal palette selection method uses the widely used YUV color representation scheme which is related to the standard RGB scheme by a linear transformation.
  • a region R of a histogram of YUV colors is a three dimensional portion of the histogram defined by three ranges, one for each dimension. Thus the boundaries of a region are perpendicular to the axes of the YUV space. The entire YUV space is the largest possible region.
  • a region of the histogram has an associated value that is called the deviation of that region, which is determined by one of a family of formulas, the simplest of which is a three- dimentional standard statistical deviation of the color occurrences appearing in a region of the histogram.
  • the deviation is a function of the sum and the sum of squares of colors in the region for each dimension as well > as the count of pixels in the region.
  • a pl anar cut is a region of a histogram such that for one of the dimensions Y, U, or V, all colors in the planar cut region have the same value.
  • a planar cut consists of all grid points of a region with an index value of j for the dimension X
  • the value j is the index of that planar cut for the dimension X in that region.
  • the following steps, as shown in Figure 4, are performed for selecting an optimal color palette of n colors: a) building a YUV histogram of colors appearing in the image to create an initial region step 20; b) selecting a region of the histogram having the maximum deviation of all the regions step 21; c) calculating for each of the primary colors Y, U, and V, the sum and the sum of the squares of each dimension of all the pixel color values for each planar cut of the region and storing them in an array for fast retrieval using the index of the planar cut, and simultaneously generating for each of the primary colors Y, U, and V, the minimum and maximum color values and the count of pixels in the planar cut and then storing them in the array step 22; d) constructing an empty high child region and an empty low child region for one of the
  • step (e) the deviation value is determined by the following equ
  • p is the number of pixels in the region R and if p is 0 or 1 then dev (R) is defined to be 0;
  • W y , W u , and W v are weights (to correct for perceptual non-uniformity) of each of the dimensions of the Y, U, V space respectively and in the preferred embodiment are 5, 3, and 2 respectively;
  • r is preferrably greater than or equal to 2 and not necessarily an integer;
  • ⁇ i f U i , and V are the Y, U, V values of the pixel i; and Y, U, and V are the average Y, U, V values, respectively, of the p pixels in the region R.
  • the particular deviation function preferred for efficiency and used for on-the-fly purposes is given as follows:
  • the correction factor function for a pixel count of p currently preferred in this formula is ln(l+4p) where In is the natural logarithm.
  • In is the natural logarithm.
  • the importance of variance increases more than the importance of large numbers of pixels in a region.
  • an optimal color palette is selected.
  • a color look-up table of 32K entries is constructed which is produced in under 150 milliseconds on a pentium 90 which is grossly faster than any previous method.
  • this table is used by the quantization algorithms described, the usefulness of this technique for constructing a table is determined solely by its speed. The speed is accomplished at the highest level by a divide and conquer technique to cut the problem down recursively and then by a very fast method, hereinafter called the sweep-l ist method for finding nearest points.
  • the sweep list method finds the nearest color to a point in the three dimensional color space, then it finds the nearest color for an adjacent point. This continues in a straight line until the boundary of the grid is reached. This is called a sweep.
  • a list of possible nearest colors is searched for each grid point.
  • Each color in the list has a partial distance pre- calculated for it in the two dimensions whose difference is the same for all grid points in the sweep.
  • the same list is searched for each point in the sweep but during the sweep the list gets smaller by using position information and the partial distance information to eliminate colors from the list that cannot be closest to any point yet to be processed in the sweep.
  • the search stops for a point when the fill or three- dimensional distance to the nearest point so far found is less than the two-dimentional partial distance to the next color in the list. This works because the list is sorted on the basis of this partial distance before the first search in a sweep.
  • the nearest color to a grid point is a color that minimizes the formula: W r C3 ) where W y , W u , W v are weights for the dimensions of Y, U, and V, respectively, and have preferred values 5, 3, and 2 respectively, and where (cl, c2, c3) , and (gl, g2 , g3) are the color and grid point YUV vectors, respectively.
  • W r C3 W r C3
  • W y , W u , W v are weights for the dimensions of Y, U, and V, respectively, and have preferred values 5, 3, and 2 respectively
  • (cl, c2, c3) , and (gl, g2 , g3) are the color and grid point YUV vectors, respectively.
  • the partial distance between a grid point and a color is determined by the above formula with only two dimensions - Y and U, Y and V, or U and V.
  • Figs. 5 and 6 The steps of the color table construction are given in two separate sequences shown in Figs. 5 and 6.
  • the general sequence is shown in Fig. 5, while the second sequence, known as the sweep-list method, is shown in Fig. 6.
  • the divide and conquer or region partitioning technique is given only for the outermost dimension to improve clarity; but the same techniqu should be used for all but the innermost dimension.
  • partitioning three dimensional regions with planar cuts is .described but the analogous and simpler splitting of planar cuts with line cuts is not described.
  • a line cut partitions a planar cut just as a planar cut partitions a three dimensional region and similar speed benefits are possible. Line cuts should be used for the same reasons as planar cuts and in substantially the same way.
  • splitting can be used for all of the dimensions in constructing such a look-up table, but a 32K cube of grid points is small enough that using the region splitting technique is of limited usefulness for the innermost dimension, especially when the innermost dimension is labeled by the highly efficient sweep-list method.
  • the innermost dimension is called the sweep-list dimension
  • all other dimensions are called partition dimensions since partitioning or dividing regions is performed using planar cuts perpindicular to those dimensions.
  • the split parameter for a region is a measure of the usefulness to the speed of the overall labeling process of the partitioning that created the region. Smaller values for the split parameter are better than larger values and values less than 1 are not possible.
  • the method for creating a color look-up table includes the following steps: a) selecting a region of the grid to be labeled with Dl being the partition dimension with the greatest range in the region, D3 being the sweep-list dimension, and L being the list of target points for the region step 39; b) marking the region for partitioning and selecting a grid value (gl) of the Dl dimension by estimating the median of Dl values in the optimal palette colors of L, if the split parameter is less than 1.5 and there are at least 3 indexes of Dl in the region steps 40 and 41; c) selecting a grid value (gl) of the Dl dimension for a planar cut of the region that has not yet been labeled, if gl is yet unselected step 33; d) calculating the partial
  • a sweep-list method for labeling an increasing index line of grid points including the following steps: a) assigning a color to minLabel which is the initial element of list L, if there is no guess, otherwise assigning the guess to minLabel step 64; b) computing the distance between g and minLabel, let minDist be the distance between g and minLabel and let nextLabel be the first optimal palette color in the list L step 64; c) label grid point g with minLabel if the minDist is less than the partial distance to the nextLabel steps 65 and 66; d) assign to nextNextLabel the optimal palette color following nextLabel in the list L step 67; e) if the distance between g and nextLabel is less than the minDist, then go to step g step 68; f) remove nextLabel from the list L, if the D3 component of minLabel is greater than or equal to the D3 component of nextLabel and g3 is greater than or equal to the D3 component of next
  • each successive sweep for labeling a line of grid points is performed on a line adjacent to the line previously labeled.
  • the sort step (f) above is performed using insertion sort starting with the sorted order used for the previous sweep; otherwise it is performed using the quicksort algorithm. While quicksort is more efficient on randomly ordered lists, insertion sort is extremely fast and much better than quicksort for only slightly out of order lists. Because there has been a change of only one unit in some dimension between the adjacent lines, the previous list is nearly in order for the next sweep.
  • step (k) above where it is tested whether a color is "closer to the continuous plane of the planar cut (from the opposite side) than any of the labels of the 4 nearest planar cut grid points", it should be understood that the continuous extended plane includes all points between the grid points as well as outside of the grid points in the plane of the planar cut. It should further be understood that a color for the region may have a component dimensional value outside of the range of the corresponding dimensional values of the grid points of the planar cut; this may occur for either or both of the two dimensions whose values are not fixed in the planar cut.
  • the partial distances for colors are kept in a sorted array and the lists are implemented as a separate array whose entries are indexes of the next element in this sorted list. A color is removed from the list simply by setting the index of the previous color in the list to the index of the next color in the list.
  • the color look-up table is a YUV table and the input colors are normally in a different representation such as RGB
  • the YUV color look-up table is converted to a RGB color look-up table.
  • a 32K entry RGB to YUV look-up table is used with 6-bits for each dimension in conjunction with the YUV optimal color look-up table to construct an RGB optimal color look-up table by simply indexing into the arrays.
  • the RGB color look-up table is used in the overcompensated error diffusion step described below.
  • step (f) above there is considerable improvement in speed to be obtained by evaluating the formula using look-up tables.
  • this is done with two bits for the random function portion of the index and 11 bits for the errorA(i)(X) input.
  • the table thus has 8K entries. Likewise, it is faster to combine the overcompensated error with the input value using another look-up table to obtain tar(X) .
  • the error diffusion method given here does eliminate contours. It also maintains lustre in the image and is fast. It does this by simultaneously over-compensating and adaptively damping the error as it is used and propagated.

Abstract

Cette invention concerne un procédé de quantification d'images qui consiste à créer une palette optimale (2) pour une image en utilisant un procédé d'écart plein à trois dimensions et en affectant moins de couleurs au lissage des zones de transition des images, à fabriquer une table de couleurs (4) de façon à faire correspondre efficacement des couleurs arbitraires aux couleurs de la palette optimale au moyen d'un procédé de balayage de liste, et à faire correspondre (5) une image donnée à une image de quantification des couleurs en utilisant un procédé de diffusion d'erreurs à surcompensation et à amortissement adaptatif.
PCT/US1997/002662 1996-02-23 1997-02-21 Procede de conception de palettes et de table de couleurs WO1997031337A1 (fr)

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AU20532/97A AU2053297A (en) 1996-02-23 1997-02-21 Method for color palette design and look-up

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US60/012,197 1996-02-23

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1999050820A1 (fr) * 1998-03-16 1999-10-07 Försvarets Forskningsanstalt Procede d'optimisation du choix des couleurs lors de la presentation d'une image

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US5394518A (en) * 1992-12-23 1995-02-28 Microsoft Corporation Luminance sensitive palette
US5418894A (en) * 1991-03-12 1995-05-23 Dainippon Screen Mfg. Co., Ltd. Coloring of designated image area and pinhole elimination by image scaling
US5490238A (en) * 1990-03-19 1996-02-06 Evans & Sutherland Computer Corporation Attribute blending system for composing computer-graphic images from objects
US5506946A (en) * 1991-10-01 1996-04-09 Electronics For Imaging, Inc. Selective color correction
US5509111A (en) * 1992-04-27 1996-04-16 International Business Machines Corporation Color image region management system and color image region management method and color image region retrieval method

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US5490238A (en) * 1990-03-19 1996-02-06 Evans & Sutherland Computer Corporation Attribute blending system for composing computer-graphic images from objects
US5418894A (en) * 1991-03-12 1995-05-23 Dainippon Screen Mfg. Co., Ltd. Coloring of designated image area and pinhole elimination by image scaling
US5506946A (en) * 1991-10-01 1996-04-09 Electronics For Imaging, Inc. Selective color correction
US5509111A (en) * 1992-04-27 1996-04-16 International Business Machines Corporation Color image region management system and color image region management method and color image region retrieval method
US5394518A (en) * 1992-12-23 1995-02-28 Microsoft Corporation Luminance sensitive palette
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