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Chapter 2: Problem 10

In-camera color processing-challenging If your camera supports a RAW pixel mode, take a pair of RAW and JPEG images, and see if you can infer what the camera is doing when it converts the RAW pixel values to the final color- corrected and gamma-compressed ight-bit JPEG pixel values. 1\. Deduce the pattern in your color filter array from the correspondence between colocated RAW and color-mapped pixel values. Use a color checker chart at this stage if it makes your life easier. You may find it helpful to split the RAW image into four separate images (subsampling even and odd columns and rows) and to treat each of these new images as a "virtual" sensor. 2\. Evaluate the quality of the demosaicing algorithm by taking pictures of challenging scenes which contain strong color edges (such as those shown in in Section 10.3.1). 3\. If you can take the same exact picture after changing the color balance values in your camera, compare how these settings affect this processing. 4\. Compare your results against those presented by Chakrabarti, Scharstein, and Zickler \((2009)\) or use the data available in their database of color images. \({ }^{26}\)

Short Answer

Expert verified
The analysis includes capturing images in RAW and JPEG formats, deducing the color filter array pattern, assessing the demosaicing algorithm's quality, adjusting the color balance, and comparing the findings to professional studies.

Step by step solution

01

Deduce the color filter array pattern

For this step, capture RAW and JPEG images using your camera, making sure the scene's lighting, subject, and environment are maintained. Analyze the differences in the RAW and the color-mapped pixel values to deduce the pattern in your color filter array. Use a color checker chart if necessary. Also, try to split the RAW image into four separate images, each image representing the even or odd rows and columns. This will create virtual sensors that can be used to better understand what is happening with each of the colors in the image.
02

Evaluate the quality of the demosaicing algorithm

Capture photographs of scenes with strong color edges to evaluate the robustness of the camera's demosaicing algorithm. You can understand the effectiveness of the algorithm by inspecting the changes in these areas under both RAW and JPEG formats.
03

Change color balance and compare

Take two identical photos while varying the color balance settings in your camera. By comparing the differences between these two photos, evaluate how the color balance affects the final color corrections.
04

Comparison with professional studies

Compare your findings with the results presented in the Chakrabarti, Scharstein, and Zickler (2009) study. If the full study is not available, use the publicly accessible data in their database of color images to make a comparison.

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Most popular questions from this chapter

-numbers and shutter speeds List the common f-numbers and shutter speeds that your camera provides. On older model SLRs, they are visible on the lens and shutter speed dials. On newer cameras, you have to look at the electronic viewfinder (or LCD screen/indicator) as you manually adjust exposures. 1\. Do these form geometric progressions; if so, what are the ratios? How do these relate to exposure values (EVs)? 2\. If your camera has shutter speeds of \(\frac{1}{60}\) and \(\frac{1}{125}\), do you think that these two speeds are exactly a factor of two apart or a factor of \(125 / 60=2.083\) apart? 3\. How accurate do you think these numbers are? Can you devise some way to measure exactly how the aperture affects how much light reaches the sensor and what the exact exposure times actually are?

Ex 2.6: Noise level calibration Estimate the amount of noise in your camera by taking repeated shots of a scene with the camera mounted on a tripod. (Purchasing a remote shutter release is a good investment if you own a DSLR.) Altematively, take a scene with constant color regions (such as a color checker chart) and estimate the variance by fitting a smooth function to each color region and then taking differences from the predicted function. 1\. Plot your estimated variance as a function of level for each of your color channels separately. 2\. Change the ISO setting on your camera; if you cannot do that, reduce the overall light in your scene (tum off lights, draw the curtains, wait until dusk). Does the amount of no?se vary a lot with \(1 \mathrm{SO} / \mathrm{gain}\) ? 3\. Compare your camera to another one at a different price point or year of make. Is there evidence to suggest that "you get what you pay for""? Does the quality of digital cameras seem to be improving over time?

how the intersection of two 2D lines can be expressed as their cross product, assuming the lines are expressed as homogeneous coordinates. 1\. If you are given more than two lines and want to find a point \(\tilde{\boldsymbol{x}}\) that minimizes the sum of squared distances to each line, $$ D=\sum_{i}\left(\bar{x} \cdot \bar{l}_{i}\right)^{2} $$ how can you compute this quantity? (Hint: Write the dot product as \(\tilde{\boldsymbol{x}}^{T} \overline{\boldsymbol{l}}_{i}\) and turn the squared quantity into a quadratic form, \(\overline{\boldsymbol{x}}^{T} \boldsymbol{A} \tilde{\boldsymbol{x}}\).) 2\. To fit a line to a bunch of points, you can compute the centroid (mean) of the points as well as the covariance matrix of the points around this mean. Show that the line passing through the centroid along the major axis of the covariance ellipsoid (largest eigenvector) minimizes the sum of squared distances to the points. 3\. These two approaches are fundamentally different, even though projective duality tells us that points and lines are interchangeable. Why are these two algorithms so apparently different? Are they actually minimizing different objectives?

White point balancing-tricky A common (in-camera or post-processing) technique for performing white point adjustment is to take a picture of a white piece of paper and to adjust the RGB values of an image to make this a neutral color. 1\. Describe how you would adjust the RGB values in an image given a sample "white color" of \(\left(R_{w}, G_{w}, B_{w}\right)\) to make this color neutral (without changing the exposure too much). 2\. Does your transformation involve a simple (per-channel) scaling of the RGB values or do you need a full \(3 \times 3\) color twist matrix (or something else)? 3\. Convert your RGB values to XYZ. Does the appropriate correction now only depend on the XY (or xy) values? If so, when you convert back to RGB space, do you need a full \(3 \times 3\) color twist matrix to achieve the same effect? 4\. If you used pure diagonal scaling in the direct RGB mode but end up with a twist if you work in XYZ space, how do you explain this apparent dichotomy? Which approach is correct? (Or is it possible that neither approach is actually correct?) If you want to find out what your camera actually does, continue on to the next exercise.

Skin color detection Devise a simple skin color detector (Forsyth and Fleck 1999; Jones and Rehg 2001; Vezhnevets, Sazonov, and Andreeva 2003; Kakumanu, Makrogiannis, and Bourbakis 2007 ) based on chromaticity or other color properties. 1\. Take a variety of photographs of people and calculate the \(x y\) chromaticity values for each pixel. 2\. Crop the photos or otherwise indicate with a painting tool which pixels are likely to be skin (e.g. face and arms). 3\. Calculate a color (chromaticity) distribution for these pixels. You can use something as simple as a mean and covariance measure or as complicated as a mean-shift segmentation algorithm (see Section 5.3.2). You can optionally use non-skin pixels to model the background distribution. 4\. Use your computed distribution to find the skin regions in an image. One easy way to visualize this is to paint all non-skin pixels a given color, such as white or black. 5\. How sensitive is your algorithm to color balance (scene lighting)? 6\. Does a simpler chromaticity measurement, such as a color ratio \((2.116)\), work just as well?
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