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Table 15.7 Color temperatures of different light sources Light sources Candle light Incandescent (household) lights Sunrise or sunset Fluorescent (of ce) lights Electronic ash lights Sunny daylight Bright overcast sky
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Color temperature (Kelvin) 1000 2000 K 2500 3500 K 3000 4000 K 4000 5000 K 5000 5500 K 5000 6500 K 6500 8000 K
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15.10.3 White Balance
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Under different lighting sources, the object will show in different colors. Human vision can adapt these differences to view the proper colors. However, machine vision devices as well as digital cameras and camcorders cannot automatically adjust the colors. If the device is not correctly con gured, the image color will differ under various light sources. White balance is a process that corrects the unrealistic color resulted from different light sources. Traditional mechanical cameras use special optical lters for color corrections when taking pictures, while digital cameras use the auto-white balance and the manual-white balance. Auto-white balance uses the data from image sensor to compute the true white color to balance the R, G, and B color components. Manual (or xed) white balance uses prede ned color balance setting on the particular lighting conditions, such as beach and snow scenes, outdoor daylight, and indoor candescent light. The light sources are usually described using color temperatures. Table 15.7 lists the common light sources and their temperatures. Incorrect color balance will produce the bluish or reddish images. Example 15.4 in Section 15.5 shows an example of performing image auto-white balance. In this experiment, we will write a C55x program for auto-white balance. MATLAB Link for CCS will be used to control the experiment. Figure 15.19 shows the ow of the auto-white balance color correction. Table 15.8 lists a section of the xed-point C function that computes the sum of the pixels for white balance process, Table 15.9 shows a portion of the C program that calculates the white-balance gain, and Table 15.10 provides an example of the white balance correction on image pixels. The experiment is controlled by MATLAB script whiteBalance.m. MATLAB Link for CCS allows MATLAB program to access global data variables using the write( ) function. The experiment also uses MATLAB function size( ) to nd the width and height of the image le and pass these parameters to DSP object as follows:
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[height, width, color] = size(RGB); write(dsp,address(dsp,'imWidth'), uint16(width)); write(dsp,address(dsp,'imHeight'),uint16(height));
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Compute sum for each color component, R, G, and B
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Calculate gain factor for each R, G, and B component
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Table 15.8 Auto-white balance code example for computing the sum
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AC0 = 0; for (j=0; j<height; j++) { for (i=0; i<width/2; i++) { AC0 += (*r>>8)&0xff; AC0 += (*r++)&0xff; } }
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Table 15.9
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rGain = 255; gGain = 255; bGain = 255; if ( (gSum <= rSum)&&(gSum <= bSum) ) { rGain = (unsigned short)((gSum*255) / rSum); bGain = (unsigned short)((gSum*255) / bSum); }
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In the above example, the image width and height are obtained from the RGB matrix. The width and height are then written to processor s global data variables imWidth and imHeight as 16-bit unsigned integers. This feature creates a exible program that is no longer depending on the parameter le. This experiment also uses two packed pixels for each 16-bit data memory. The white balance program is written for handling dual-pixel data format. We use three pictures taken by a digital camera, which is set to xed white balance at 4150 K for this experiment. These pictures are taken under incandescent light source (2850 K), uorescent light source (4150 K), and daylight (6500 K). Before applying the white balance, the picture taken under candescent light looks reddish, while the picture taken under the bright daylight
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Table 15.10
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for (j=0; j<height; j++) { for (i=0; i<width/2; i++) { AC0 = (*r>>8)&0xff; AC0 *= rGain; if (AC0 > 0xff00) AC0 = 0xff00; AC0 &= 0xff00; AC1 = (*r)&0xff; AC1 *= rGain; AC1 >>= 8; if (AC1 > 0xff) AC1 = 0xff; *r++ = (unsigned short)(AC0| AC1); } }
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