CN113269206A - Color-embedded visual exploration method and system - Google Patents

Color-embedded visual exploration method and system Download PDF

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CN113269206A
CN113269206A CN202110565988.6A CN202110565988A CN113269206A CN 113269206 A CN113269206 A CN 113269206A CN 202110565988 A CN202110565988 A CN 202110565988A CN 113269206 A CN113269206 A CN 113269206A
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color
embedding
data
colors
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CN113269206B (en
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曾琼
赵勇威
汪云海
钟凡
屠长河
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Shandong University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/56—Extraction of image or video features relating to colour
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/50—Extraction of image or video features by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
    • G06V10/507—Summing image-intensity values; Histogram projection analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image

Abstract

The invention belongs to the technical field of human-computer interaction, and provides a color-embedded visual exploration method and system. The method comprises the following steps: acquiring two-dimensional scalar field data; mapping the selected initial color table onto two-dimensional scalar field data; embedding one or more colors into a color embedding location in an initial color table of the two-dimensional scalar field data; and mapping the color table embedded with the colors to two-dimensional scalar field data, and outputting a color optimized visual image.

Description

Color-embedded visual exploration method and system
Technical Field
The invention belongs to the technical field of human-computer interaction, and particularly relates to a color-embedded visual exploration method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The exploration of scalar field data is an important task in the visualization of scientific data, for example, the change of ocean currents is very important to be connected with the salinity and the temperature of seawater. Color is an indispensable visual coding element for expressing scalar field data, and is most widely applied. Color visualization creates a visual image rich in visual information by creating a mapping relationship (i.e., color mapping) between a color table (colormap) and data. However, due to the finite number of colors in the color table, the scalar field data, and the diversity of the pattern features, it is difficult to help users dynamically explore rich content in the data with a statically colored table. For example, for data with a high dynamic range of values, if the data that the user wants to observe exists in a narrow range and the number of colors corresponding to the corresponding value range is small, the corresponding feature may be visually indistinguishable. Therefore, in order to obtain a color chart suitable for the data exploration task, the user needs to adjust the color position in the conventional color chart, which is time-consuming and labor-consuming.
In order to improve the efficiency of color table design, researchers have proposed a series of automatic color table optimization methods. Tominski and the like automatically adjust the color positions in the color chart based on the idea of histogram equalization, so that a data range containing more numerical values can be represented by more colors; zeng et al adjust the color distribution based on the boundary model so that the hidden boundary information in the data is highlighted. Although the above algorithm can provide more visual information for a specific value region (such as a high-density value region, a high boundary region, etc.), since the number of colors in the original color table is limited, the colors for encoding other value regions are inevitably reduced, the global balance is difficult to achieve, and the data visual search is not facilitated. Although some methods provide interaction means such as region-of-interest (ROI), the method is relatively long in calculation time and inefficient in large-scale data exploration due to the adoption of a relatively complicated optimization process.
Disclosure of Invention
In order to solve the technical problems in the background art, the present invention provides a color-embedded visual exploration method and system, which introduces one or more new colors (i.e. embedded colors) into a specific position region in an original color table of two-dimensional scalar field data to enrich data information in a visualized image.
In order to achieve the purpose, the invention adopts the following technical scheme:
a first aspect of the present invention provides a method for visual exploration of color embedding.
A method of color embedded visual exploration, comprising:
acquiring two-dimensional scalar field data;
mapping the selected initial color table onto two-dimensional scalar field data;
embedding one or more colors into a color embedding location in an initial color table of the two-dimensional scalar field data;
and mapping the color table embedded with the colors to two-dimensional scalar field data, and outputting a color optimized visual image.
Further, the color embedding positions are selected as follows:
Figure BDA0003080698870000021
wherein, IijExpressing the contradiction between the data characteristics and the color distribution between adjacent control points i and j in the color table; w is aijData characteristics between control points i and j are shown; dijIndicating color discernability between adjacent control points i and j.
Further, the color embedding position is based on: the m numerical value regions with the maximum inconsistency between the data and the color distribution are used as color embedding intervals, and the respective midpoints of the m numerical value regions are used as color embedding positions.
Further, the selection of the embedded colors is:
Figure BDA0003080698870000031
wherein, i and j are assumed to be control points corresponding to the color embedding positions,
Figure BDA0003080698870000032
as a color discriminability constraint term，
Figure BDA0003080698870000033
Is a color name constraint term; α is a balance parameter between the two, and its main role is to balance the contradiction between the color discriminability constraint and the color name constraint.
A second aspect of the present invention provides a color-embedded visual exploration system.
A color-embedded visual exploration system, comprising:
an input module configured to: acquiring imported two-dimensional scalar field data;
a mapping module configured to: mapping the selected initial color table onto two-dimensional scalar field data;
an embedding module configured to: embedding one or more colors into a color embedding location in an initial color table of the two-dimensional scalar field data;
an output module configured to: and mapping the color table embedded with the colors to two-dimensional scalar field data, and outputting a color optimized visual image.
Further, a data feature module is included that is configured to: and displaying the distribution of the data histogram between the adjacent control points.
Further, a color discernability module is included that is configured to: and presenting the color discriminability change curves of the initial color table and the optimized color table corresponding to the middle of each bins segment.
Further, a history module is included, which is configured to: and displaying the color table generated in the user exploration process and storing the color table selected and stored by the user.
A third aspect of the invention provides a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the color-embedded visual exploration method as described above in relation to the first aspect.
A fourth aspect of the invention provides a computer apparatus.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the color-embedded visual exploration method according to the first aspect when executing said program.
Compared with the prior art, the invention has the beneficial effects that:
(1) the system can help a user to explore multi-scale global data information in real time, and supports visual exploration tasks such as single-region numerical reading and multi-region numerical comparison by combining an overview and detail technology.
(2) A color embedding position estimation method is provided, which helps a user to quickly find a position with low color discriminability corresponding to a characteristic region in data.
(3) A color embedding optimization method is provided, which helps a user to embed a color with higher harmony compared with the surrounding area in an area with lower data distinguishability so as to improve the overall distinguishability of data.
(4) A region of interest (ROI) interactive exploration and optimization method is proposed that will optimize only for the region based on the user specified ROI region.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of the present embodiment;
FIG. 2 is a diagram showing an interface distribution according to the present embodiment;
FIG. 2-A is a color table panel where a user may select a color table;
FIG. 2-B is a visual input panel for a user to import data and map through an initial color table;
FIG. 2-C is a visual output panel for presenting a color optimized visual image;
FIG. 2-D is a data feature panel, primarily for displaying data histogram distribution between adjacent control points;
FIG. 2-E is a color discriminability panel, which is mainly used to present the color discriminability variation curve corresponding to the initial color table and the optimized color table in the middle of each bins segment;
FIG. 2-F is a history panel, primarily used to record a color table after optimization;
FIG. 3 is an optimization result of mandlebromdata (mapped by gradolormap) under different color optimization balance parameters;
FIG. 4 is the optimization result of frequency ncydata (mapped with gradolormap) under different color embedding numbers m;
FIG. 5 shows the results of displaying the function of ROI with heatfluxdata (mapped with coolwarcolormap).
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
It is noted that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagrams may represent a module, a segment, or a portion of code, which may comprise one or more executable instructions for implementing the logical function specified in the respective embodiment. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Example one
As shown in fig. 1, the present embodiment provides a color-embedded visual exploration method, and the present embodiment is illustrated by applying the method to a server, it is understood that the method may also be applied to a terminal, and may also be applied to a system including a terminal and a server, and is implemented by interaction between the terminal and the server. The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a network server, cloud communication, middleware service, a domain name service, a security service CDN, a big data and artificial intelligence platform, and the like. The terminal may be, but is not limited to, a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and the like. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the application is not limited herein. In this embodiment, the method includes the steps of:
s101: acquiring two-dimensional scalar field data;
s102: mapping the selected initial color table onto two-dimensional scalar field data;
s103: embedding one or more colors into a color embedding location in an initial color table of the two-dimensional scalar field data;
specifically, this step is mainly to improve the overall discrimination of the two-dimensional field data by embedding a harmonious relatively high color into an appropriate position found in the initial color table.
Problem definition: given scalar field data D comprising l data samples and an initial color table C comprising n three-dimensional colors of CIELAB space, these colors may be represented by w (w)<n) control points are generated by linear interpolation. Specifically, the control point includes a position p ═ { p ═ p1,p2,…,pwAnd color c ═ p1,p2,…,pwTwo attributes, the set of colors between any two adjacent control points i and j is denoted cij＝{cij 1，cij 2，…，cij k}. By normalizing the data and the color to the same value range (such as 0-1), an association mapping between the data and the color can be established based on the index, so as to obtain a color-coded visual image. The aim of the invention is to find a reasonable color embedding position
Figure BDA0003080698870000072
And embedding colors
Figure BDA0003080698870000073
S1031: color embedding location
Figure BDA0003080698870000074
The specific implementation manner of the method is as follows:
Figure BDA0003080698870000075
the formula is mainly used for describing the contradiction between the data characteristics and the color distribution between the adjacent control points i and j in the color table, when the more the data characteristics are, the lower the corresponding color distinguishability of the areas is, the greater the number of the inconsistency measurement submeters is, namely, the stronger the inconsistency between the data characteristics and the color distribution is; otherwise, the smaller the value, the smaller the contradiction between the two.
Wherein wijThe data characteristics between the control points i and j are represented, and the specific calculation mode is as follows:
Figure BDA0003080698870000076
l is the sum of all data in the two-dimensional scalar field, lijIs the sum of the corresponding data samples between control points i and j.
Wherein d isijThe color distinguishability between adjacent control points i and j is represented by the following specific calculation mode:
Figure BDA0003080698870000081
Δ E is the Euclidean distance calculation matrix for CIELAB space, and k is the number of colors between control points i and j.
Finally, m numerical value regions with maximum inconsistency between data and color distribution are selected as color embedding regions, and respective midpoints of the m numerical value regions are used as color embedding positions
Figure BDA0003080698870000082
By default, m is set to 1. As shown in fig. 4, the optimization results of frequency data (mapped with gray colormap) at different color embedding numbers m are shown.
S1032: embedded colors
Figure BDA0003080698870000083
The specific implementation manner of the method is as follows:
Figure BDA0003080698870000084
whereinAssuming that i and j are control points corresponding to the color embedding positions,
Figure BDA0003080698870000085
as a constraint term for the discriminability of colors,
Figure BDA0003080698870000086
is a color name constraint term; α is a balance parameter between the two, and its main role is to balance the contradiction between the color discriminability constraint and the color name constraint.
The color discriminative constraint is defined as follows:
Figure BDA0003080698870000087
wherein the content of the first and second substances,
Figure BDA0003080698870000088
is the discriminability solved on the new color values.
The color name constraint is defined as follows:
Figure BDA0003080698870000089
wherein the content of the first and second substances,
Figure BDA0003080698870000091
and
Figure BDA0003080698870000092
respectively, the color name difference between the embedded color and the embedded interval control point.
The color name difference can be solved by the minimum cosine distance between the color name matrices:
Figure BDA0003080698870000093
in the above-mentioned formula,
Figure BDA0003080698870000094
is color ciThe corresponding name matrix.
As shown in fig. 3, the optimization results of the mandlebrot data (mapped by the gray colormap) under different color optimization balance parameters α are shown.
S104: and mapping the color table embedded with the colors to two-dimensional scalar field data, and outputting a color optimized visual image.
Example two
The present embodiment provides a color-embedded visual exploration system.
A color-embedded visual exploration system, comprising:
an input module configured to: acquiring imported two-dimensional scalar field data;
wherein, the input module: the user imports his own two-dimensional scalar field data on the interactive interface. The two-dimensional scalar field data refers to scalar data of a two-dimensional space, such as temperature distribution on an earth grid plane, a medical CT (computed tomography) picture and the like; as shown in region B in fig. 2.
A mapping module configured to: mapping the selected initial color table onto two-dimensional scalar field data;
wherein the mapping module: the module contains 70 built-in color charts, which cover the commonly used 50 perception linear color charts, 9 color charts with two dispersed ends and 11 multi-tone nonlinear color charts, including a gray scale color chart, a rainbow color chart and the like. The user may select an arbitrary color table as the initial color coding. As shown in region a in fig. 2.
The specific implementation of the module is as follows:
a color table containing n colors is denoted by C, { C ═ C1,C2,…,CnD ═ D }1,D2,…,D n0 < D < 1 denotes the values of the two-dimensional scalar field data normalized to between 0 and 1 in ascending order, the mapping being achieved by assigning C to the elements with the same subscript as D.
An embedding module configured to: embedding one or more colors into a color embedding location in an initial color table of the two-dimensional scalar field data;
the embedded module is mainly used for embedding a harmonious relatively high color into a proper position of the initial color table so as to improve the overall distinguishability of the two-dimensional field data.
Problem definition: given scalar field data D comprising l data samples and an initial color table C comprising n three-dimensional colors of CIELAB space, these colors may be represented by w (w)<n) control points are generated by linear interpolation. Specifically, the control point includes a position p ═ { p ═ p1,p2,…,pwAnd color c ═ p1,p2,…,pwTwo attributes, the set of colors between any two adjacent control points i and j is denoted cij＝{cij 1，cij 2，…，cij k}. By normalizing the data and the color to the same value range (such as 0-1), an association mapping between the data and the color can be established based on the index, so as to obtain a color-coded visual image. The aim of the invention is to find a reasonable color embedding position
Figure BDA0003080698870000102
And embedding colors
Figure BDA0003080698870000103
Step 1: color embedding location
Figure BDA0003080698870000104
The specific implementation manner of the method is as follows:
Figure BDA0003080698870000105
the formula is mainly used for describing the contradiction between the data characteristics and the color distribution between the adjacent control points i and j in the color table, when the more the data characteristics are, the lower the corresponding color distinguishability of the areas is, the greater the number of the inconsistency measurement submeters is, namely, the stronger the inconsistency between the data characteristics and the color distribution is; otherwise, the smaller the value, the smaller the contradiction between the two.
Wherein wijThe data characteristics between the control points i and j are represented, and the specific calculation mode is as follows:
Figure BDA0003080698870000111
l is the sum of all data in the two-dimensional scalar field, lijIs the sum of the corresponding data samples between control points i and j.
Wherein d isijThe color distinguishability between adjacent control points i and j is represented by the following specific calculation mode:
Figure BDA0003080698870000112
Δ E is the Euclidean distance calculation matrix for CIELAB space, and k is the number of colors between control points i and j.
Finally, m numerical value regions with maximum inconsistency between data and color distribution are selected as color embedding regions, and respective midpoints of the m numerical value regions are used as color embedding positions
Figure BDA0003080698870000113
By default, m is set to 1. As shown in fig. 4, the optimization results of frequency data (mapped with gray colormap) at different color embedding numbers m are shown.
Step 2: embedded colors
Figure BDA0003080698870000114
The specific implementation manner of the method is as follows:
Figure BDA0003080698870000115
wherein, i and j are assumed to be control points corresponding to the color embedding positions,
Figure BDA0003080698870000116
as a constraint term for the discriminability of colors,
Figure BDA0003080698870000117
is a color name constraint term; α is a balance parameter between the two, and its main role is to balance the contradiction between the color discriminability constraint and the color name constraint.
The color discriminative constraint is defined as follows:
Figure BDA0003080698870000118
wherein the content of the first and second substances,
Figure BDA0003080698870000119
is the discriminability solved on the new color values.
The color name constraint is defined as follows:
Figure BDA0003080698870000121
wherein the content of the first and second substances,
Figure BDA0003080698870000122
and
Figure BDA0003080698870000123
respectively, the color name difference between the embedded color and the embedded interval control point.
The color name difference can be solved by the minimum cosine distance between the color name matrices:
Figure BDA0003080698870000124
in the above-mentioned formula,
Figure BDA0003080698870000125
is color ciThe corresponding name matrix.
As shown in fig. 3, the optimization results of the mandlebrot data (mapped by the gray colormap) under different color optimization balance parameters α are shown.
An output module configured to: mapping the color table embedded with colors to two-dimensional scalar field data, and outputting a color optimized visual image;
and the output module is used for mapping the optimized color table to the two-dimensional scalar field. As shown in region C of fig. 2.
The data characteristic module is used for showing the distribution of a data histogram between every two adjacent control points;
and the color discriminability module is used for presenting a color discriminability change curve corresponding to the initial color table and the optimized color table in the middle of each bins segment.
And the history recording module is used for displaying the color table generated in the exploration process of the user, and once the user is satisfied with the optimized color table, the savecormap button can be clicked, and the color table can be stored in the history recording area. The module is convenient for the user to quickly trace back the searched color table and quickly store the satisfied result. As shown in region F of fig. 2.
EXAMPLE III
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the color-embedded visual exploration method as described in the first embodiment above.
Example four
This embodiment provides a computer device, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the color-embedded visual exploration method according to the first embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A method for visual exploration of color embedding, comprising:
acquiring two-dimensional scalar field data;
mapping the selected initial color table onto two-dimensional scalar field data;
embedding one or more colors into a color embedding location in an initial color table of the two-dimensional scalar field data;
and mapping the color table embedded with the colors to two-dimensional scalar field data, and outputting a color optimized visual image.
2. A visual exploration method of color embedding according to claim 1, characterized in that said selection of color embedding positions is:
Figure FDA0003080698860000011
wherein, IijExpressing the contradiction between the data characteristics and the color distribution between adjacent control points i and j in the color table; w is aijData characteristics between control points i and j are shown; dijIndicating color discernability between adjacent control points i and j.
3. The method of claim 2, wherein the color embedding position is determined according to: the m numerical value regions with the maximum inconsistency between the data and the color distribution are used as color embedding intervals, and the respective midpoints of the m numerical value regions are used as color embedding positions.
4. A visual exploration method of the color embedding according to claim 1, characterized in that the selection of the embedding colors is:
Figure FDA0003080698860000012
wherein, i and j are assumed to be control points corresponding to the color embedding positions,
Figure FDA0003080698860000013
as a constraint term for the discriminability of colors,
Figure FDA0003080698860000014
is a color name constraint term; α is a balance parameter between the two, and its main role is to balance the contradiction between the color discriminability constraint and the color name constraint.
5. A color-embedded visual exploration system, comprising:
an input module configured to: acquiring imported two-dimensional scalar field data;
a mapping module configured to: mapping the selected initial color table onto two-dimensional scalar field data;
an embedding module configured to: embedding one or more colors into a color embedding location in an initial color table of the two-dimensional scalar field data;
an output module configured to: and mapping the color table embedded with the colors to two-dimensional scalar field data, and outputting a color optimized visual image.
6. The color embedded visual exploration system according to claim 5, further comprising a data characteristics module configured to: and displaying the distribution of the data histogram between the adjacent control points.
7. The color embedded visual exploration system according to claim 5, further comprising a color discernability module configured to: and presenting the color discriminability change curves of the initial color table and the optimized color table corresponding to the middle of each bins segment.
8. The color-embedded visual exploration system according to claim 5, further comprising a history module configured to: and displaying the color table generated in the user exploration process and storing the color table selected and stored by the user.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the color-embedded visual exploration method according to any one of claims 1 to 4.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps in the color-embedded visual exploration method according to any one of claims 1 to 4 when executing said program.
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