WangMoreno - Revision history

imported>Projects221: /* Appendix I */

2014-03-21T02:25:53Z

Appendix I

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	[[File:Winter14_deltaE_scripts.zip]]		[[File:Winter14_deltaE_scripts.zip]]

			=== Further Simulated Images ===

			Beyond the images used in this report, we examined many other simulated camera exposures; however, due to the size and number of the tif files as well as the fact they can be easily re-generated with the above scripts, they are not included here.

	=Appendix II=		=Appendix II=

imported>Projects221: /* Scripts */

2014-03-21T02:23:43Z

Scripts

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	makeCamera: a script to instantiate a camera model (optics,sensor,and processor). The goal is to have a consistent camera setup, where the parameters that turn out to be ''relevant'' to the experiment can be easily varied by simple inputs.		makeCamera: a script to instantiate a camera model (optics,sensor,and processor). The goal is to have a consistent camera setup, where the parameters that turn out to be ''relevant'' to the experiment can be easily varied by simple inputs.

	makeImage: a script to load a multispectral scene of user's choice, set up illumination type and intensity, call "makeCamera" with chosen parameters, set up camera conversion to one of our pre-computed CCM's, and "take a picture". It then saves the image as a .tif file with a distinctive name. The parameters are chosen by setting string variables in Matlab's workspace before calling the script. It also has ability to measure deltaE if the scene chosen is a macbeth color ~~char~~		makeImage: a script to load a multispectral scene of user's choice, set up illumination type and intensity, call "makeCamera" with chosen parameters, set up camera conversion to one of our pre-computed CCM's, and "take a picture". It then saves the image as a .tif file with a distinctive name. The parameters are chosen by setting string variables in Matlab's workspace before calling the script. It also has ability to measure deltaE if the scene chosen is a macbeth color chart. It depends on finding the (included) .mat files with the pre-calculated CCM matrices for pixel size / illumination combinations; it also depends on being able to find the scene files mentioned above.

	This last script can then be called from inside a (nested) loop to produce many variations of images without user intervention.		This last script can then be called from inside a (nested) loop to produce many variations of images without user intervention.

			[[File:Winter14_deltaE_scripts.zip]]

	=Appendix II=		=Appendix II=

imported>Projects221: /* Scripts */

2014-03-21T02:18:42Z

Scripts

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	makeCamera: a script to instantiate a camera model (optics,sensor,and processor). The goal is to have a consistent camera setup, where the parameters that turn out to be ''relevant'' to the experiment can be easily varied by simple inputs.		makeCamera: a script to instantiate a camera model (optics,sensor,and processor). The goal is to have a consistent camera setup, where the parameters that turn out to be ''relevant'' to the experiment can be easily varied by simple inputs.

	makeImage:		makeImage: a script to load a multispectral scene of user's choice, set up illumination type and intensity, call "makeCamera" with chosen parameters, set up camera conversion to one of our pre-computed CCM's, and "take a picture". It then saves the image as a .tif file with a distinctive name. The parameters are chosen by setting string variables in Matlab's workspace before calling the script. It also has ability to measure deltaE if the scene chosen is a macbeth color char

	~~TODO: include~~ (~~attach?~~) ~~scripts used and description~~ of ~~what they do~~.		This last script can then be called from inside a (nested) loop to produce many variations of images without user intervention.

	=Appendix II=		=Appendix II=

imported>Projects221: /* Conclusions */

2014-03-21T02:04:13Z

Conclusions

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	* The Auto White Balance methods evaluated are an unreliable way to correct improper conversion, both subjectively and by deltaE measure.		* The Auto White Balance methods evaluated are an unreliable way to correct improper conversion, both subjectively and by deltaE measure.

	Thus, we found that the deltaE color accuracy metric may be acceptable for coarse grain use, perhaps by an image processor to detect that an altogether incorrect color correction matrix has been applied (e.g., a camera doing automatic illuminant detection by examining a MCC target under a mystery illuminant). However, for the intended CPIQ application as a metric of comparison between different cell phone cameras, we find the deltaE metric insufficient. Amongst reasonably accurate images, we found little (and subjective) correlation between the deltaE value and our perception of the quality of an output image. As such, a consumer looking to compare multiple phone cameras with deltaE values that differ by less than about 10 cannot reliably use this metric to compare color accuracy in order to find ''the one they prefer''. (especially given that many other variables such as jpg encoding, the quality of the display, etc on said device might affect the experience of the photos in a more substantial way)		Thus, we found that the deltaE color accuracy metric may be acceptable for coarse grain use, perhaps by an image processor to detect that an altogether incorrect color correction matrix has been applied (e.g., a camera doing automatic illuminant detection by examining a MCC target under a mystery illuminant). However, for the intended CPIQ application as a metric of comparison between different cell phone cameras, we find the deltaE metric insufficient. Amongst reasonably accurate images, we found little (and subjective) correlation between the deltaE value and our perception of the quality of an output image. As such, a consumer looking to compare multiple phone cameras with deltaE values that differ by less than about 10 cannot reliably use this metric to compare color accuracy in order to find ''the one they prefer''. (especially given that many other variables such as jpg encoding, the quality of the display, etc on said device might affect the experience of the photos in a more substantial way).

	== Future Work / What We Didn't Try ==		== Future Work / What We Didn't Try ==

imported>Projects221: /* Conclusions */

2014-03-21T02:00:01Z

Conclusions

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	* Sensor pixel size has a significant effect on color accuracy at low light levels.		* Sensor pixel size has a significant effect on color accuracy at low light levels.

	* The optimal deltaE value varies between different illuminant types, with fluorescent light being the most color accurate.		* The optimal deltaE value varies between different illuminant types, with fluorescent light being the most color accurate for still somewhat-mysterious reasons.


	Afterwards, we moved to real-world scenes and applied various combinations of settings, producing images both good and bad. By looking at the output images and comparing their subjective qualities and measured deltaE values, we determined that:		Afterwards, we moved to real-world scenes and applied various combinations of settings, producing images both good and bad. By looking at the output images and comparing their subjective qualities and measured deltaE values, we determined that:

	* Large differences in deltaE seem ~~useful for avoiding~~ awful photos.		* Large differences in deltaE seem to correlate decently well with "good" vs. "awful" photos.

	* ~~Fine~~-grain differences (e.g., <10) don’t always match subjective judgement.		* Finer-grain differences (e.g., <10) don’t always match subjective judgement / preference. Even finer differences (e.g., <5) are sometimes hard to spot depending on scene contents.

	* Auto White Balance unreliable way to correct improper conversion, both subjectively and by deltaE measure.		* The Auto White Balance methods evaluated are an unreliable way to correct improper conversion, both subjectively and by deltaE measure.

			Thus, we found that the deltaE color accuracy metric may be acceptable for coarse grain use, perhaps by an image processor to detect that an altogether incorrect color correction matrix has been applied (e.g., a camera doing automatic illuminant detection by examining a MCC target under a mystery illuminant). However, for the intended CPIQ application as a metric of comparison between different cell phone cameras, we find the deltaE metric insufficient. Amongst reasonably accurate images, we found little (and subjective) correlation between the deltaE value and our perception of the quality of an output image. As such, a consumer looking to compare multiple phone cameras with deltaE values that differ by less than about 10 cannot reliably use this metric to compare color accuracy in order to find ''the one they prefer''. (especially given that many other variables such as jpg encoding, the quality of the display, etc on said device might affect the experience of the photos in a more substantial way)
	Thus, we found that the deltaE color accuracy metric may be acceptable for coarse grain use, perhaps by an image processor to detect that an altogether incorrect color correction matrix has been applied. However, for the intended CPIQ application as a metric of comparison between different cell phone cameras, we find the deltaE metric insufficient. Amongst reasonably accurate images, we found little correlation between the deltaE value and our perception of the quality of an output image. As such, a consumer looking to compare multiple phone cameras with deltaE values less than 10 cannot reliably use this metric to compare color accuracy.

	== Future Work / What We Didn't Try ==		== Future Work / What We Didn't Try ==

imported>Projects221: /* Future Work / What We Didn't Try */

2014-03-21T01:49:55Z

Future Work / What We Didn't Try

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	== Future Work / What We Didn't Try ==		== Future Work / What We Didn't Try ==
			Some ideas that occurred to us as we worked on this, which we did not have time to pursue:

	* LEDs are becoming popular as indoor lighting; ~~Their~~ color accuracy performance should be evaluated.		* LEDs are becoming popular as indoor lighting; Color spectra from common variants should be included in ISET, and their color accuracy performance should be evaluated. Colloquially, they are said to offer better color performance than Fluorescent light, but what does that mean?
	* Night photography vs. human night vision: what does color accuracy mean in this situation?		* Night photography vs. human night vision: what does color accuracy mean in this situation?
	* DeltaE after Auto White Balance: content-dependence of "grayworld" and "whiteworld" methods ~~requires~~ a more carefully designed experiment (<math>\Delta E</math> calculation uses macbeth chart scene, but we want to check the white balance transform generated from a different scene).		* DeltaE after Auto White Balance: as mentioned, content-dependence of "grayworld" and "whiteworld" methods would require a more carefully designed experiment (<math>\Delta E</math> calculation uses macbeth chart scene, but we want to check the white balance transform generated from a different scene).
			* Maybe take a different approach: move the illuminant slowly along (for example) the black-body radiation curve, and find points of significant difference in noticeability or human preference. what is the corresponding difference in deltaE? also, evaluate the relative color inaccuracy of images taken along the black-body radiation curve, vs. those from synthetic illuminants significantly deviating from the curve.

	=References=		=References=

imported>Projects221: /* References */

2014-03-21T01:43:25Z

References

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	Daxter, Donald; Frederic Cao; Henrik Eliasson; Jonathan Phillips. "Development of the I3A CPIQ spatial metrics." Web. 8 Mar 2014. <http://proceedings.spiedigitallibrary.org/data/Conferences/SPIEP/64097/829302_1.pdf>.		Daxter, Donald; Frederic Cao; Henrik Eliasson; Jonathan Phillips. "Development of the I3A CPIQ spatial metrics." Web. 8 Mar 2014. <http://proceedings.spiedigitallibrary.org/data/Conferences/SPIEP/64097/829302_1.pdf>.

	The ISET toolbox		The ISET toolbox, particularly the scripts related to color accuracy and camera processing.

			Thanks to Prof. Farrell as well for further guidance.

	=Appendix I=		=Appendix I=

imported>Projects221: /* References */

2014-03-21T01:41:07Z

References

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	Daxter, Donald; Frederic Cao; Henrik Eliasson; Jonathan Phillips. "Development of the I3A CPIQ spatial metrics." Web. 8 Mar 2014. <http://proceedings.spiedigitallibrary.org/data/Conferences/SPIEP/64097/829302_1.pdf>.		Daxter, Donald; Frederic Cao; Henrik Eliasson; Jonathan Phillips. "Development of the I3A CPIQ spatial metrics." Web. 8 Mar 2014. <http://proceedings.spiedigitallibrary.org/data/Conferences/SPIEP/64097/829302_1.pdf>.

			The ISET toolbox

	=Appendix I=		=Appendix I=

imported>Projects221: /* Pixel Size */

2014-03-21T01:37:11Z

Pixel Size

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	=== Pixel Size ===		=== Pixel Size ===
	we saw before that the color accuracy of small pixels (1um in our case) suffers more acutely under low light conditions. in the image below we visualize the low-light condition for 1um, 2.8um, and 5um pixels (in each case the number of pixels in the image is adjusted for equal sensor size, such that the overall optical zoom / FOV / etc are the same):		we saw before that the color accuracy of small pixels (1um in our case) suffers more acutely under low light conditions. in the image below we visualize the low-light condition for 1um, 2.8um, and 5um pixels (in each case the number of pixels in the image is adjusted for equal sensor size, such that the overall optical zoom / FOV / etc are the same):

	[[File:image4_pxsize_lowlight.png]]		[[File:image4_pxsize_lowlight.png]]

	There is a clear loss of contrast as our pixels get smaller, with a clear loss of color "saturation" (esp. visible on the blues and yellows) which might correspond to a sharper loss of SNR on the blue-filtered pixels of the sensor; These two scenes are under tungsten light, which already is biased towards having less energy in the "blue" part of the spectrum, and this is exacerbated here by the very low illumination level.		There is a clear loss of contrast as our pixels get smaller, with a clear loss of color "saturation" (esp. visible on the blues and yellows) which might correspond to a sharper loss of SNR on the blue-filtered pixels of the sensor; These two scenes are under tungsten light, which already is biased towards having less energy in the "blue" part of the spectrum, and this is exacerbated here by the very low illumination level.

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	when we move to better lighting levels, this difference in color accuracy between smaller and larger pixels goes away:		when we move to better lighting levels, this difference in color accuracy between smaller and larger pixels goes away:

	[[File:image5_pxsize_goodlight.png]]		[[File:image5_pxsize_goodlight.png]]
	There are no significant visual color accuracy effects, and the deltaE metric is also similar.
			There are no significant visual color accuracy effects, and the deltaE metric is also similar.

	=== Effect of Poor Estimation / Mismatch of Illuminant ===		=== Effect of Poor Estimation / Mismatch of Illuminant ===

imported>Projects221: /* Auto White Balance */

2014-03-21T01:36:49Z

Auto White Balance

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	in both AWB cases, the face image is, arguably, only little better, and far from "accurate" compared to the proper image (bottom right). On the Macbeth chart, it's hard to decide among the two images in the top row (both inaccurate colors, different average hue), though one might agree that the bottom left chart is better than both of the top row, and not as good as the proper processing.		in both AWB cases, the face image is, arguably, only little better, and far from "accurate" compared to the proper image (bottom right). On the Macbeth chart, it's hard to decide among the two images in the top row (both inaccurate colors, different average hue), though one might agree that the bottom left chart is better than both of the top row, and not as good as the proper processing.

	to highlight our heightened human sensitivity to skin tones, we can examine another scene under the same 4 cases:		To highlight our heightened human sensitivity to skin tones, we can examine another scene under the same 4 cases:

	[[File:image3_whitebalance.png]]		[[File:image3_whitebalance.png]]

	In this case, human reaction to the poor processing as well as the results of the Auto-White-Balance would arguably be less disapproving, though the difference is still noticeable.		In this case, human reaction to the poor processing as well as the results of the Auto-White-Balance would arguably be less disapproving than the above portrait, though the difference is still noticeable.

	It is worth noting that, though we can use our deltaE's as a rough guide when considering other scenes, they are probably an inaccurate indicator of the expected color accuracy in AWB: As Auto-White-Balance is a content-dependent transformation on ''each image''(the offset applied depends on the average of the existing pixels), a standard deltaE measurement, post-AWB, is valid really only for that very image of the MCC. More accurate deltaE calculation on different scenes would require an experiment with more complex processing, which we did not undertake.		It is worth noting that, though we can use our deltaE's as a rough guide when considering other scenes, they are probably an inaccurate indicator of the expected color accuracy in AWB: As Auto-White-Balance is a content-dependent transformation on ''each image''(the offset applied depends on the average of the existing pixels), a standard deltaE measurement, post-AWB, is valid really only for that very image of the MCC. More accurate deltaE calculation on different scenes would require an experiment with more complex processing, which we did not undertake.