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Automatic White Balance Algorithms Fordigital Stillcameras–A Automatic White Balance Algorithms for Digital Still Cameras – a Comparative Study G. Zapryanov, D. Ivanova, I. Nikolova Key Words: Automatic White Balance; Digital Photography; Color – Temperature. 2. Digital Photography Color Capture Abstract. Automatic white balance is an important function of digital A digital image color is formed by the intensity values of still cameras. Failure to estimate illumination chromaticity correctly will the red, green and blue channels. Each of these values is result in invalid overall color cast in the final image. The goal of automatic white balancing is to estimate accurately the color of the influenced by three physical characteristics: (i) source of light overall scene illumination and to make the image look as if is taken (also called illuminant), (ii) object reflectance or transmittance, under canonical light. This article discusses some of the basic white (iii) sensor spectral response (combination of spectral charac- balance algorithms, making a comparison between them, and pro- teristics of colorants used in the Bayer filter [1] and spectral poses a modification of one of the methods. For comparison pur- sensitivity of the photodetectors - CCD or CMOS). poses, test images are used, taken at different settings of white balance of digital still camera, various color temperatures of the 2.1. Sources of Light scenes, and under six calibrated illumination environments. To have an image, it is necessary to have a light source (illuminant). Normalized distribution of light at different wave- lengths gives spectral power distribution of the illumination as a function of wavelengths. The graph of the power emittance over 1. Introduction the visual spectrum is called relative spectral power distribution curve for this specific illuminant or source. Figure 1 shows the The human visual system (HVS) has the ability to map spectral power distribution of three common light sources: (a) “white” colors to the sensation of white, even though an object typical daylight, which is continuous over the visible spectrum; has different radiance when it is illuminated with different light (b) tungsten light (also very smooth); (c) fluorescent lamp, sources. In other words, if you were shown a sheet of white which has sharp spikes in the spectrum power distribution. paper under natural daylight or under incandescent lighting, or even under fluorescent lighting, you would say that it was white. 2.2. Object Reflectance/Transmittance This phenomenon is called color constancy. When an image is When electromagnetic waves from a light source reach an captured by a digital camera, the sensor response at each pixel object, part of them is absorbed and depending on the object, depends on the illumination. That is, each pixel value recorded the other part is reflected or transmitted. Regardless by the by the sensor is related to the color temperature of the light source of light or the individual perception, an object always source. When a white object is illuminated with low color tem- absorbs, reflects or transmits the same percent of each wave- perature light, it will appear reddish in the recorded image. length of the spectrum. That is why the spectral reflectance Similarly, it will appear bluish under a high color temperature. (transmittance) of an object, can be represented as a function The purpose of white balance is to process the image so that of the wavelength. The spectral reflectance corresponding to two visually it looks the same way, regardless of source of light. typical object colors is shown in figures 2(a) and figures 2 (b). There are various automatic white balance (AWB) algo- The red object on figures 2(a) absorbs almost all of the low rithms proposed in the literature: Gray world, Retinex theory, frequency components (blue to green) and reflects almost all perfect reflector, standard deviation-weighted gray world, and high frequency wavelengths. The amount of reflected low fre- etc. [2-6,9]. Most of these algorithms make certain assumptions quency components is very small and therefore the visible color of the color distribution of the image. They differ in the way the is bright red. illumination is estimated. The gray object (figure 2 (b)) has approximately constant The aim of this paper is to quantify the effects of white- value of spectral reflectance for the different wavelengths, which balancing errors in digital photography for different AWB algo- results in a light gray color (as the curve is raised up). rithms. This is achieved by means of subjective and non-subjec- Illumination and object reflectance (or transmittance) to- tive tests. Different natural and studio images are utilized in order gether determine what is known as color stimulus. to study the effect of image contents on the acceptability of white point shifts. 2.3. Sensor’s Spectral Response The structure of this paper is as follows: color capture in Almost all sensors used in digital cameras (except Foveon) digital photography is briefly examined in Section II. Section III are sensitive only to the intensity of light and not to its compo- contains short descriptions of the explored AWB algorithms. nents (different wavelengths). That is why having a color image Experimental setup and obtained results are reported in Section requires color filters, most often an RGB Bayer filter. Different IV. Some concluding remarks are made in Section V. filter elements have different spectral transmittance depending information technologies 16 1 2012 and control on the colorants used. The sensors themselves have different ⎡ I 2 I ⎤⎡a⎤ ⎡ I ⎤ sensitivity and even for the same manufacturer [1] they have ∑∑ R ∑∑ R = ∑∑ G differences in the spectral characteristics (figure 3). (2) ⎢ 2 ⎥⎢ ⎥ ⎢ ⎥ and max I max I ⎣b⎦ max I G Digital cameras need to “adapt” not only to the different ⎣ R R ⎦ ⎣ ⎦ color stimulus, but to the spectral response of the different ⎡ 2 ⎤ c ⎡ I ⎤ sensors and these define the need for and the challenges in front ∑∑I B ∑∑I B ⎡ ⎤ ∑∑ G ⎢ ⎥ = ⎢ ⎥ of the automatic white balance algorithms in digital photography. 2 ⎢d⎥ ⎣ max I B max I B ⎦⎣ ⎦ ⎣ max IG ⎦ where I , I and I are the RGB values of the original image, a – R G B 3. Explored Algorithms A Breif and b are the correction coefficients for the red channel, and c Description and d for the blue channel. 3.4. Standard Deviation-weighted Gray World 3.1. Gray World Theory The standard deviation-weighted gray world (SDWGW) al- One of the simplest and the most often used assumptions gorithm extends the Gray world assumption and is proposed by about the color constancy is the so-called Gray World Theory Lam et al. [4]. It subdivides the image into p blocks and for each (GWT): the majority of all visual scenes in the world can be σ σ σ one of them calculates standard deviations ( R, G, B) and R = G = B μ μ μ integrated to the gray, i.e. avg avg avg . The most means ( R, G, B) of the R, G, and B channels (in this paper direct solution for automatic correction of the white is to calcu- the number of blocks p is 12, as the used Olympus camera has late the mean values for each color channel of the captured a frame format 4/3). After that, the mean values for each channel are calculated using the weight coefficients of the blocks. For image ( ) and to use them to calculate the example, for the k-th block weighted average standard deviation ratios between them as the green channel’s average is most for green channel can be calculated according Eq. (3) p often used for a base: corrR = Ravg Gavg and σ (k) StdAvgG = G * μ (k) (3) ∑ p G . corrB = B G . The obtained coefficients are used to k=1 σ (i) avg avg ∑i=1 G correct the color values of the pixels in the image. Based on these values for each channel, the correction 3.2. Retinex Theory/ Perfect Reflector Method coefficient for green channel is calculated as it is shown in Eq. (4) R avg ,G avg , BAnotheravg possible method for white balance is based on the assumption that a sample of white color is associated with the StdAvgR + StdAvgG + StdAvgB (4) corrG = . maximum cone response of the human visual system [2] and 3∗ StdAvgG should provoke maximum signal in the camera for all the three color channels - that is the so-called Retinex Method (RM) or According the green channel, the correction coefficients Perfect Reflector Method (this method can be regarded as a for blue and red channels are calculated as it is shown special limiting case of Retinex). It locates the brightest pixel in Eq. (5) R , G and B , and assigns it as a reference white StdAvgR + StdAvgG + StdAvgB max max max (5) corrR = , point. 3∗ StdAvgR If R, G and B are respectively the red, green and blue channels in the image, the white balancing may be obtained StdAvgR + StdAvgG + StdAvgB corrB = . 3∗ StdAvgB through R Rmax , G Gmax and B Bmax for each pixel of the image. Another method to determine the correction coeffi- 3.5. Standard Deviation cients is calculated by Eq. (1), where the green channel stays unchanged: and Luminance-weighted Gray World The standard deviation and luminance-weighted gray world Gmax Gmax (1) corrR = , corrB = , corrG =1. algorithm (SDLWGW1) is also proposed by Lam et al. [5] and it Rmax Bmax is a variation of the SDWGW algorithm. The image is similarly subdivided, but the weights are defined as follows - Eq. (6) 3.3. Gray World + Retinex Theory (green channel, for example): This method (GWR), proposed by Edmund Lam [3], is a m n L _ w (i, j) μ _ lum _ g(k) = k *G (k) combination from the upper two methods - the Gray world (6) ∑∑ m n i, j i==11j L _ w (x, y) assumption and the Retinex theory.
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