TW201200851A - Object reflection spectrum estimation method - Google Patents

Abstract

The invention provides an object reflection spectrum estimation method for use in digital image processing, comprising inputting data relating to light source frequency spectrums, color-matching, image colors to a data processing device for estimating a plurality of substrate reflection spectrums accordingly; inputting an image data to the processing device for allowing data of the image color to be retrieved; selecting a corresponding substrate reflection spectrum among a plurality of reflection spectrums according to a preset light source such that the reflection spectrum of the image can be estimated in view of the data of the image color. The invention employs light sources, human eyes and objects for the estimation of reflection spectrums and can be applied to image processing for computer animation or digital synthesizing to improve quality and efficiency.

Description

201200851 VI. Description of the Invention: [Technical Field] The present invention relates to an object reflection spectrum estimation method, and more particularly to an object reflection spectrum estimation method involving a light source, a human eye, and an object. [Prior Art] With the advancement of computer technology, digital technology has also developed rapidly, especially with image synthesis as a major trend in the future of the multimedia industry. Image compositing is the process of splicing two different images and superimposing them. Usually, the boundary grading technique or computational compensation is used to solve the boundary problem of image synthesis. This method of synthesis is feasible when the light source conditions of the two images are not much different. However, the images taken by the two groups in different light source environments will produce unreality at the boundary during the synthesis, because the object will The light source reflects or transmits to the color-sensitive cone-shaped cells of the human eye, and thus the color of the image captured by the same object under different light source conditions may be different. In order to solve the unreality of image synthesis, it is more commonly used by people familiar with the field of color image processing to use white balance technology or other color adjustment techniques. The common white balance processing method in the past includes Gray World Theory (GWT) and Maximal RGB Value (GAM). Other color adjustment techniques such as principal component analysis and estimation, multiple regression analysis Law and so on. In addition, synthetic images intended to allow viewers to agree and accept their realism must be directed to perspective and perspective, dimensional relationships, lenses, colors and contrasts, 111659 4 201200851 grain, edge and edge quality, 'however, such a modification process Careful correction of highlights and shadows, reflections, materials, and hand-drawn graphics and drawings is time consuming and cumbersome. Therefore, how to provide the method can be applied to image processing such as color temperature modulation (4) color material metering, #, " spot color temperature synthesis, etc.

Adding image processing: the image coloring technique based on the rule of thumb is further increased. [Summary of the Invention] In order to solve the various methods of estimating the spectrum of the prior art, it is easy and quick to change the image quality. Μ 录制 录制 , , , , 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 物体 II II II II II II II II II II II II II II II II II II II a color data; (2) causing the #material processing device to estimate a plurality of base reflection spectra according to the plurality of light source spectrum=material, the plurality of color matching materials, and the plurality of image color data; 3) wheeling a shirt image to the data processing device and capturing the color data of the image; and (4) causing the data to be loaded and placed according to the color data of the image and the plurality of base reflection spectra, and estimating The reflection spectrum of the object. In the embodiment, the step (4) further includes a step (4_1) for causing the data processing device to select a corresponding base reflection spectrum from the plurality of base reflection spectra according to a predetermined light source; and step (4-2) Having the data processing device estimate the reflection spectrum of the object according to the color data of the image and the base reflection corresponding to the predetermined light source. In the foregoing embodiment, the step (4_3) is further included to make the data processing device The reflection spectrum of the scene image estimates the color value of the image by the predetermined formula; and the step (4-4) causes the data processing device to convert the color value of the image into the image according to the color temperature data of the predetermined light source. The three-color color value. Compared with the prior art, the present invention first establishes a model of the base reflection spectrum for performing image processing, and according to the color data or other related data of the image, the base reflection spectrum model is used for reflection. The estimation of the spectrum, in addition to the application of the present invention to digital image post-production, can improve efficiency and quality. [Embodiment] The following is a specific implementation form Illustrating the present invention, those skilled in the art can easily understand that other advantages and functions of the present invention can be implemented or applied by other different embodiments. It is obvious that the human eye can perceive color, which is achieved by the combination of $source, object reflection, and eye. Therefore, = two parameters of known light source and eye can be used to derive the object. The reflected light is the image of the object. The tenth 1A diagram shows the basic/scheduled diagram of the method for estimating the reflection spectrum of the object of the present invention. The step S11 is located in the step S11, for example, a personal computer, a notebook computer, a digital computer, a smart phone. The data processing device inputs a plurality of light source spectra; a plurality of color matching materials and a plurality of image color data. In detail, 6 111659 201200851 a plurality of light source spectral data is an energy distribution of each wavelength of the light source, and the plurality of color matching materials are human eye cones. The degree of sensitization of the cells, and the plurality of image color data are all colors that the object can reflect. Then, the process proceeds to step S12. And the data processing device estimates the number of base reflection spectra of the different poles according to the plurality of source spectrum data, the plurality of color matching materials, and the plurality of image color data, and establishes a base reflection spectrum model of the object. Then, the process proceeds to step S13. In step S13, the data processing device inputs an image and captures the color data of the image. The color data of the image is the R, 〇, and B components of the image. Cc, Ce. Then proceed to step si4. In step S14, the data processing device estimates the reflection spectrum of the image according to the color data of the image and the plurality of base reflection spectra. In other words, the color data of the image is input to The reflection spectrum of the image can be obtained by the base reflection spectrum model established in the steps S11 to S12. Preferably, in step S14, steps φ S141 to S142 as shown in Fig. 1B are repeated. In step S141, the data processing device selects a corresponding base reflection spectrum from the plurality of base reflection spectra according to a predetermined light source. In detail, the light source used is not limited in the pre-established base reflection spectrum model, so when estimating the reflection spectrum of the image, it is necessary to select a base reflection spectrum corresponding to a predetermined light source, usually for shooting. The light source at the time of the image, and further the reflection spectrum of the object under the predetermined light source. Then, it proceeds to step S142. In step S142, the data processing device is configured to estimate the reflection spectrum of the image according to the color of the image and the base reflection spectrum of the light source corresponding to the predetermined source. It should be noted that the reflection obtained by the base reflection spectrum model is Spectral renectance, that is, the reflectance per unit wavelength, where the reflectance is the ratio of the incident light to the reflected light reflected by the object. . More preferably, the reflection spectrum estimation method of the object of the present invention includes steps S143 to S144 as shown in Fig. 1C. In step S143, the data processing device causes the data processing device to estimate the color value of the image according to the reflection spectrum of the image, that is, convert the reflection spectrum into a color value. Then it proceeds to step S144. In step S144, the color value of the image is converted into a three-color color value of the image according to the color temperature data of the predetermined light source, that is, the color value is converted into an RGB value defined by the RGB three-color color space via a linear matrix. It can be seen from the foregoing steps of the first embodiment to the first embodiment of FIG. 1C that after the base reflection spectrum model is established based on the light source, the image color, and the color matching data, only the color data of an image needs to be input, and the base reflection spectrum molding can be matched. The reflection spectrum of the image is estimated. Therefore, in addition to rapidly estimating the reflection spectrum of the image through the reflection spectrum estimation method of the object of the present invention, different light sources can be selected to estimate the reflection spectrum of the object under different light sources, thereby improving the efficiency and quality of the image processing. In the following, the basic concepts and principles of the present invention are expressed by mathematical formulas. The following formulas relate to the conversion of different color spaces, which are merely illustrative and are not intended to limit the scope of the invention. 8 111659 201200851 The predetermined formula used in the foregoing step S12 is a tristimulus values formula combined with a color mixing principle, and the tristimulus value formula is.

X = kjRW-PW-x(A)dA Y = kjR(A).P(4)

Z = kjR(A)p(^)z(A)dA , 100 ~ \?{λ)·γ(λ)άλ (1) where R(;L) is the object reflection spectrum, ρμ) is the spectrum of the light source, I·, f, [for the human eye color matching function, yt as a normalization. In addition, in the color mixing method theory, when the three primary colors of R, G, and B are used as the base of the simulated reflection spectrum, the simulation functions are & (4), bW, and φ to (4), respectively, and the optimized reflection spectrum of the object is : R0(乂) = cR .rR(/l)+cG .rG(/l)+cB .rj/i·) _ Pixel r,g,b ^B=_25Γ" (2) where c 〆cc and cs are the R, G, and B component value addition coefficients, respectively. Combined with the above formula (1) and formula (2), we can get:

X = k J(cR · rR(4) + cG · rG (4) + cB . rB(4)).P(A). x(A)cU 9 111659 201200851 Y - k ί (cr · rR (4) + cG · rG (4)+cB · rB(4) ).P(A). Only know that Z = k I(cr .rR W+cc .rc(A)+cB. rB(;l)). p(4)·do as...(3-1) Equation (3-1) shows that the input source spectrum data p(/i), the human eye color matching function iΪ5, and the image color data, including the color values χ, γ, z, and color data 4, Cc, ~, can estimate the object Base reflection spectrum ~ (4), with α), must). In the present embodiment, the present invention is applied to digital image processing, and the human eye color matching function is used to limit the spectrum data of the light source to the range of visible light, that is, the wavelength is between 38 〇 nm and 780 nm, and the color temperature of the light source is added ( C〇1〇r temperature) parameter t, so formula (3-1) becomes 780

Xt ck, Jr〇 (again)·ρ丨(4)·X(ja 380 780 =kt i (c r · rR (4)+CG · rG (4)+ cB . rB (4)).pt (4)·do as 380 780

Y = kjR〇(A)Pt(^).xdA 380 780 -kt J(cR rR(;l)+CG .rG(4)+CR.rB(again))·ρ丨(4).y(乂沁乂 380 780

z = kjR〇W.Pt(;l).icU 380 780 -kt J(cR .rR(/l)+cG -rG(A)+cB -rB(A)).乂乂). 380 ... (3-2) Therefore, from equation (3-2), the input spectrum of the visible light, the color matching function of the human eye, and all the colors that the object can reflect, that is, the image color data, can establish a base reflection spectrum database. Π1659 10 201200851 Next, input a Cc, Cfl of an image, and select a base reflection spectrum hW, 4(4), (4)' corresponding to the light source from the base reflection spectrum database according to the light source at the time of the image capture, Cc , Cfl 'The object reflection spectrum R of the object can be calculated by the formula (2). (4). Furthermore, the reflection spectrum R of the object can be obtained by the formula (1). (4) Converted to three spikes X, γ, Z. Second, the tristimulus values can be converted to RGB values by the color space conversion formula (4).

• X; 'R' Y, = [M]t GB ... (4) where t is the color temperature of the source, because the matrix 色彩 in the color space conversion formula is related to the selected reference white point, so it will be under a light source with a different color temperature There are different conversion matrices. Concretely, the invention uses Munsell Color Science

The light source spectrum data (Daylight series light source) experimented by the Laboratory and the color matching experiment conducted by the International Commission on φ Illumination (CIE) in 1931 according to the principle of color mixing, the response of the human eye to light The quantization system obtains the Color Matching Function and self-produces the image data of the average distribution as the basis for calculating the reflection spectrum of the RGB three-color substrate. According to the above formula (1) and formula (2), the relationship between the object reflection spectrum and the color tristimulus value can be derived (3-2), and the XYZ value on the left side of the equal sign can be converted by the color space conversion formula (4). Into RGB values. Therefore, the spectrum data distribution of the color temperature r is Ρ, (4), the human eye color matching function 5, ί, 〗, and the image color data are substituted into the formula (3-2), and the unknown RGB trichromatic base can be inversely derived. 11 111659 201200851 The reflection spectrum & (4), bW, ~ (4), and then the three-color base reflection spectrum is used to estimate the object reflection spectrum. Please refer to Figures 2A to 2K for a spectrum data distribution diagram of the light source with a color temperature of 4000K to 9000K. First, select the visible range from the full-band spectrum. For example, the Daylight series source starts from 4000K every 10K to 9000K for a total of 11 sets of source spectrum data. As shown in Figures 2A to 2K, the spectral composition of the light source is expressed by energy. , representing the distribution of the Korean laser power of different wavelengths. The human eye color matching function is shown in Figures 3A to 3C, which are the human eye color matching functions Xy and Z, respectively.

According to the above formula (3-2), input R, (}, b each image color data from 〇 to 255 combined with each other according to the color temperature ί, with the 11 sets of color temperature of 4000 Κ to 9000 色 shown in Figure 2 The spectrum data will be inversely derived from the 11 sets of RGB three-color base reflection spectra shown in Figures 4 to 4, ~(4), "(3), ~(4). Then, the light source selection phase based on digital image shooting. Corresponding RGB three-color base reflection spectrum, the object reflection spectrum of the digital image under the light source can be calculated. I In addition, the 苐5 A to 5K map shows the object reflection spectrum of the light source of the same input image at different color temperatures, That is, from the 4000K, 4500K, 5000K, 5500K, ... 9000K a total of 11 groups of object reflection spectrum, wherein the input image color data ~ ~, ~ are respectively R: 140, G: 90, B: 70. by the 5th As can be seen, when the color temperature is higher, the pattern of the object's reflection spectrum will shift toward the direction of short wavelengths. The reflectance at 5OOnm will decrease, and the object representing the color temperature of the original image will reflect 5 pixels for short wavelength energy. The reflectivity will increase. Therefore, for digital images In other words, when the image color of the image is 111659 12 201200851 and the light source at the time of shooting, the color spectrum of the light source with different color temperatures can be adjusted to adjust the color temperature of the image, so that the image is adjusted under different light sources. To verify the program, set the white balance mode to Dawn 5200K in advance, and use the spectrometer (JETI spectroradiometers specbos) to record the color temperature during shooting. Then, take two real shot images A and B, the color temperature is 5400K and 5760K respectively. The image B is converted to a color temperature of 5400K, which is the same as the real shot image A. The converted image is called image B2, and finally the color difference between the sample color and the estimated color is calculated. The maximum color difference between the sample color and the estimated color is 4.14. The color difference value is 2.54. Therefore, the verification procedure can prove that the object reflection spectrum estimation method provided by the present invention is applied to the color temperature adjustment of the object image, and the adjusted image condition is similar to the real shooting situation. The color temperature adjustment of the digital image performed by the object reflection spectrum estimation method of the present invention has a good effect Therefore, the present invention is based on the tristimulus value formula and the color mixture method, and based on the relationship between the human eye, the light source and the object, the object can be estimated under the condition that the human eye and the light source parameters are known. Reflective spectrum. In other words, using a tristimulus value formula and color mixing method, human eye color matching function, source spectral distribution and image color data (including color data and color values) to construct a base reflection spectrum model, which is known for an image. In the color data, the reflection spectrum of the object under a certain light source can be estimated by matching the aforementioned base reflection spectrum model and making the light source a light source or another predetermined light source at the time of shooting. In summary, the object reflection spectrum estimation method of the present invention can estimate the reflection spectrum of the object under the original shooting light source, and can also assign the 13 111659 201200851 light source with different color temperatures to estimate the reflection spectrum of the object after changing the light source. It has been experimentally proved that the image obtained by changing the light source (that is, adjusting the color temperature of the light source) has a small color difference from the image captured under the variable light source. Therefore, the object reflection spectrum estimation method of the present invention is applied to digital images such as image synthesis, image processing, virtual reality, etc., which can shorten the operation time, improve work efficiency and improve content quality, for computer animation, video content, etc. The field has further good results. The above-described embodiments are merely illustrative of the principles and effects of the invention and are not intended to limit the invention. Modifications and changes may be made to the above-described embodiments without departing from the spirit and scope of the invention. Therefore, the scope of protection of the present invention should be as set forth in the scope of the patent application to be described later. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1A is a flow chart showing a method for estimating an object reflection spectrum of the present invention; FIG. 1B is a flow chart showing a more specific embodiment of an object reflection spectrum estimation method of the present invention; The flow chart of one embodiment of the object reflection spectrum estimation method of the present invention; the 2A to 2K diagrams show the spectrum data distribution map of the light source with the color temperature of 4000K to 9000K in the specific implementation method of the object reflection spectrum estimation method of the present invention; The 3C diagram shows the human eye color matching function used in the object reflection spectrum estimation method of the present invention, respectively; 14 111659 201200851 The 4A to 4K diagrams respectively show the color temperature of 4000 Κ to 9000 具体 in the specific implementation of the object reflection spectrum estimation method of the present invention. The RGB three-color base reflection spectrums ~(8), 、, 〜4(4); and the 5A to 5K diagrams show the object reflection spectrum under the light source of the color temperature of 4000K to 9000K in the specific implementation of the object reflection spectrum estimation method of the present invention. [Main component symbol description] S11~S14 Steps S141~S144 Steps

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Claims (1)

201200851 VII. Patent application scope ··· An object reflection spectrum estimation, ^- , is applied to digital image processing. The object reflection spectrum estimation method includes the following steps: a (1) Touching the stomach material processing device a plurality of color matching data and a plurality of image color data of the light source spectrum data; (2) causing the data processing device to estimate a plurality of base reflection spectra according to the plurality of (four) color matching materials and the plurality of image color woven materials () for 4 data processing devices to input the image - and to take the color data of the image; and ▲ (4) to enable the data processing device to estimate the reflection of the object based on the color data of the image and the number of base reflection spectra Spectrum. 2. For the method of applying the object reflection spectrum according to the item (4), the predetermined formula is the formula of the tristimulus value. 3. The method for estimating an object reflectance spectrum according to the scope of the patent application, wherein the image color data comprises color data and color values. 4. The method of estimating an object reflectance spectrum according to the scope of claim 2, wherein the color data is color component data of the image. 5. The object reflection spectrum estimation method according to claim 1, wherein the step (4) further comprises the following steps: (4-1) causing the data processing device to reflect from the plurality of substrates according to a predetermined light source. Selecting a corresponding base reflection spectrum from the spectrum; and (4-2) causing the data processing device to estimate the reflection of the image according to the color data of the image and the base reflection spectrum of the corresponding light source. A method for estimating an object reflection spectrum according to claim 5, wherein the predetermined light source is a photographing light source of the image. The method of estimating the reflection spectrum of an object as described in claim 5, wherein the step (4) complex comprises the following steps: (4-3) the data processing device is configured to reflect the reflection spectrum of the image. The predetermined formula estimates a color value of the image; and H (4-4) causes the data processing device to convert the color value of the image to a three-color color value of the image according to the color temperature data of the predetermined light source. The object reflection spectrum estimation method according to claim 7, wherein the color value of the image is a tristimulus value. 9. The object reflection spectrum estimation method according to claim 7, wherein the three color color values are RGB values. 17 111659