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15.10.4 Gamma Correction and Contrast Adjustment
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Image gamma correction and contrast adjustment are often implemented using lookup tables. For an 8-bit image system, this method requires a 256-value table to map the image. Figure 15.20 shows the relations between the input image (solid line) and the table mapped output image (dotted line). Figure 15.20(b) maps each pixel according to the gamma curve = 2.20. Figure 15.20(c) maps input image pixels to a low-contrast image. Finally, Figure 15.20(d) maps the input image to produce an image with high contrast. The tables used for table-lookup methods are usually generated off line or during the system initialization. In this experiment, we generate the gamma table, low contrast table, and high contrast table during initialization. If a dynamic table generation is required at run time, an ef cient DSP program with fast
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(a) Linear 200 200
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(c) Low contrast 200 200
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(d) High contrast
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Figure 15.20 high-contrast
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Table-lookup methods for different image mappings: (a) linear; (b) gamma; (c) low-contrast; and (d)
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Table 15.12 Files gammaContrast.m tableGen.c imageMapping.c gammaContrastTest.c gammacontrast.h gammaContrast.cmd gammaContrast.pjt boat.jpg temple.jpg File listing for experiment exp15.10.4_gammaContrast Description MATLAB script controls the experiment Fixed-point C function generates gamma table Fixed-point C function for gamma correction Program for testing experiment C header le DSP linker command le DSP project le Image le Image le
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implementation will be needed. Some of the math functions may be realized using function approximation methods introduced in 3. As discussed in Section 15.5, gamma correction is a prewarping process to compensate the display devices nonlinear output characteristics. Most personal computers use a gamma value of 2.20. The contrast adjustment is achieved by changing the image distribution as described by the contrast curves shown in Figure 15.20. The experiment programs and MATLAB scripts are under the directory exp15.10.4_ gammaContrast. Two images are used for this experiment. One is for gamma correction and the other is for contrast adjustment. Table 15.12 lists the les used for this experiment. Procedures of the experiment are listed as follows: 1. Connect the C5510 DSK or simulator to the computer. 2. Start MATLAB and set the MATLAB working directory to the directory ..\exp15.10. 4_gammaContrast where the MATLAB script gammaContrast.m is located. 3. Start the experiment using the MATLAB script and observe the resulting images. The gamma curve correction makes the image more bright for viewing. Applying low contrast adds more information in the middle range, but reduces the image dynamic range in bright and dark regions. High contrast can provide better details in the bright areas, but the image loses ne details in darken areas. 4. We can use MATLAB to prepare static data for DSP programs. We can remove the C le tableGen.c and use MATLAB to generate the gamma table, low contrast table, and high contrast table. Modify the script gammaContrast.m such that the MATLAB will generate these tables and use the MATLAB write function to initialize the gamma table and contrast table. 5. Some applications require both gamma correction and contrast adjustment. In order to improve the run-time ef ciency, these two table-lookup implementations can be combined. Modify the experiment such that it will use a combined table that is generated with gamma = 1.80 and high contrast a = 0.00035.
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15.10.5 Histogram and Histogram Equalization
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In Example 15.6, we have shown the histogram equalization on luminance of the YCb Cr color space. In this experiment, we will implement the histogram equalization using the C5510 DSK. The histogram equalization includes three important functions as shown in Figure 15.21.
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Make equalization table from the histogram Remap each pixel using the equalization table
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Process ow of the histogram equalization
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We write the computation of histogram and equalization functions using C55x assembly language. The assembly program takes advantages of zero-overhead local-block-repeat loop (rptblocal) instruction and bit- eld extract (bfxtr) instruction to effectively improve the run-time speed. Tables 15.13 and 15.14 list the histogram and histogram equalization routines, respectively.
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Table 15.13 _historgam: mov dbl(*AR0+), XAR4 mov dbl(*AR0+), XAR2 mov *AR0+, T0 mov *AR0+, AR1 sub #1, AR1 mov AR1, BRC0 sftl T0, #-1 sub #1, T0 mov T0, BRC1 rptblocal hightLoop-1 rptblocal widthLoop-1 mov *AR4+, AC0 bfxtr #0xff00, AC0, T0 add #1, *AR2(T0) and #0x00ff, AC0, T0 add #1, *AR2(T0) widthLoop: nop hightLoop: ret Assembly implementation of histogram ; ; ; ; x = hist->x; hBuf = hist->histBuf; Get width Get height
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; for (j=0; j<hist->height; j++){ ; for(i=0; i<hist->width>>1; i++){ ; pixel = *x++; ; hBuf[(pixel>>8)&0xff] += 1; ; ; } ; } hBuf[pixel&0xff] += 1;
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Table 15.14 _histEqualizer: mov dbl(*AR0), XAR3 || aadd #4, AR0 mov *AR0+, AR1 sftl AR1, #-1 || mov *AR0+, AR2 sub #1, AR1 mov AR1, BRC1 sub #1, AR2 mov AR2, BRC0 mov dbl(*AR0), XAR2 || rptblocal heightLoop-1
Assembly implementation of histogram equalization ; x = hist->x; ; Add 4 for large memory pointer offset ; Get width ; Get height
; eqTbl = hist->eqTable; ; for (j=0; j<hist->height; j++){