RayChenPsych2012Project - Revision history

imported>Psych2012: /* Max RGB */

2012-03-19T22:41:06Z

Max RGB

← Older revision		Revision as of 22:41, 19 March 2012
Line 19:		Line 19:
	A similar algorithm to Gray World is called max-RGB. This is based on Land's theory that the human visual system achieves color constancy by detecting the area of highest reflectance in the field of view. He hypothesizes that it does this separately by the three types of cones in our eye (which detect long, medium, and short wavelengths - corresponding to R, G, and B respectively), and that we achieve color constancy by normalizing the response of each cone by their highest value.		A similar algorithm to Gray World is called max-RGB. This is based on Land's theory that the human visual system achieves color constancy by detecting the area of highest reflectance in the field of view. He hypothesizes that it does this separately by the three types of cones in our eye (which detect long, medium, and short wavelengths - corresponding to R, G, and B respectively), and that we achieve color constancy by normalizing the response of each cone by their highest value.

	So, to approximate this, we take the maximum in each color channel and normalize the pixels in each channel according to the maximal value using the equations below.		So to approximate this, the Max RGB method does the same: take the maximum in each color channel and normalize the pixels in each channel according to the maximal value using the equations below.
	<center>[[File:rc_mrgbeqn.png \|170px]]</center>		<center>[[File:rc_mrgbeqn.png \|170px]]</center>


	== Gray Edge & Max Edge ==		== Gray Edge & Max Edge ==

imported>Psych2012: /* Results - What you found */

2012-03-19T22:39:04Z

Results - What you found

← Older revision		Revision as of 22:39, 19 March 2012
Line 36:		Line 36:
	I used this particular implementation [1] due to the fact that this method applies gamut mapping to the derivative structure of images. The reasoning was to compare the performance of Gray Edge and Max Edge to a more complex approach. Since I did not implement this, please refer to [1] for details on the inner workings of this algorithm.		I used this particular implementation [1] due to the fact that this method applies gamut mapping to the derivative structure of images. The reasoning was to compare the performance of Gray Edge and Max Edge to a more complex approach. Since I did not implement this, please refer to [1] for details on the inner workings of this algorithm.

	= Results ~~- What you found~~ =		= Results =

	For the results, I compare the output of the algorithms to the original image generated under D65 daylight as the standard. I use the Delta E metric between these for a numerical value in difference.		For the results, I compare the output of the algorithms to the original image generated under D65 daylight as the standard. I use the Delta E metric between these for a numerical value in difference.

imported>Psych2012: /* Gray Edge & Max Edge */

2012-03-19T22:38:22Z

Gray Edge & Max Edge

← Older revision		Revision as of 22:38, 19 March 2012
Line 31:		Line 31:
	<center>[[File:rc_edgeeqn.png \|400px]]</center>		<center>[[File:rc_edgeeqn.png \|400px]]</center>

			We note that this uses a more generalized equation which apply to both Gray Edge (use p=1) and Max Edge (use p=infinity). This is based on Finlayson's work which generalized the Gray World and Max RGB algorithms based on the Minkowski p-norm. However, the basic theory behind these algorithms are still the same.

	== Gamut Mapping ==		== Gamut Mapping ==

imported>Psych2012: /* Conclusions */

2012-03-19T22:31:46Z

Conclusions

← Older revision		Revision as of 22:31, 19 March 2012
Line 57:		Line 57:
	= Conclusions =		= Conclusions =

			We see from this sample of results that these algorithms are highly dependent on the input scene. Even a small area in the scene can widely affect the performance of these methods. We would need to test on a larger variety of illuminants to reach more conclusive results, especially since the edge based algorithms seem to work better for outdoor images.

			We also conclude that simple algorithms can achieve similar performance to more complex algorithms. While the gamut mapping method referenced in this project needs heavy computation (calibration over a set of canonical gamuts and needs to use convex optimization), the other methods described can be run in real time without having to use too much special hardware. For future work, it may be useful to see how robust a combination of these methods would be.

	= References - Resources and related work =		= References - Resources and related work =

imported>Psych2012: /* Results - What you found */

2012-03-19T22:22:48Z

Results - What you found

← Older revision		Revision as of 22:22, 19 March 2012
Line 39:		Line 39:
	For the results, I compare the output of the algorithms to the original image generated under D65 daylight as the standard. I use the Delta E metric between these for a numerical value in difference.		For the results, I compare the output of the algorithms to the original image generated under D65 daylight as the standard. I use the Delta E metric between these for a numerical value in difference.

	In image set 1 (Macbeth chart), we see that the Gray World method actually performs best due to the fact that we have a large variety of reflectance. Without comparing the results to the D65 image, we might feel that Gray Edge performs best. However, our delta E values tell a different story; if we were to examine the Gray Edge result more closely, we would see that this method is slightly biased towards a light blue. This is more apparent in the other image sets - the numbers don't lie! In image set 2, this bias in Gray Edge is more apparent, as it makes the faces look ~~more unnatural~~. Gray World no longer performs too well since a single color now dominates the image. We also see in this set that Max RGB has the best performance as in our original picture, the highest reflectance happened to be close to the illuminant. In image set 3, Max Edge dominates the performance. This is probably due to the presence of the white collar which coincided with the max edge.		In image set 1 (Macbeth chart), we see that the Gray World method actually performs best due to the fact that we have a large variety of reflectance. Without comparing the results to the D65 image, we might feel that Gray Edge performs best. However, our delta E values tell a different story; if we were to examine the Gray Edge result more closely, we would see that this method is slightly biased towards a light blue. This is more apparent in the other image sets - the numbers don't lie! In image set 2, this bias in Gray Edge is more apparent, as it makes the faces look unnaturally white. Gray World no longer performs too well since a single color now dominates the image. We also see in this set that Max RGB has the best performance as in our original picture, the highest reflectance happened to be close to the illuminant. In image set 3, Max Edge dominates the performance. This is probably due to the presence of the white collar which coincided with the max edge.

	In image set 4, we can see that none of the algorithms perform very well. Perhaps it is due to a combination of the orange-ish illuminant and the green-ish background in the painting, but we see that all of the color balancing methods biased towards a blue background. This accounts for the high delta E values. Although the results may look acceptable by themselves (such as Max Edge), the goal is to reproduce the most similar color to the original; therefore, we can only conclude that the methods fail in this case.		In image set 4, we can see that none of the algorithms perform very well. Perhaps it is due to a combination of the orange-ish illuminant and the green-ish background in the painting, but we see that all of the color balancing methods biased towards a blue background. This accounts for the high delta E values. Although the results may look acceptable by themselves (such as Max Edge), the goal is to reproduce the most similar color to the original; therefore, we can only conclude that the methods fail in this case.

imported>Psych2012: /* Results - What you found */

2012-03-19T22:21:59Z

Results - What you found

← Older revision		Revision as of 22:21, 19 March 2012
Line 38:		Line 38:

	For the results, I compare the output of the algorithms to the original image generated under D65 daylight as the standard. I use the Delta E metric between these for a numerical value in difference.		For the results, I compare the output of the algorithms to the original image generated under D65 daylight as the standard. I use the Delta E metric between these for a numerical value in difference.

			In image set 1 (Macbeth chart), we see that the Gray World method actually performs best due to the fact that we have a large variety of reflectance. Without comparing the results to the D65 image, we might feel that Gray Edge performs best. However, our delta E values tell a different story; if we were to examine the Gray Edge result more closely, we would see that this method is slightly biased towards a light blue. This is more apparent in the other image sets - the numbers don't lie! In image set 2, this bias in Gray Edge is more apparent, as it makes the faces look more unnatural. Gray World no longer performs too well since a single color now dominates the image. We also see in this set that Max RGB has the best performance as in our original picture, the highest reflectance happened to be close to the illuminant. In image set 3, Max Edge dominates the performance. This is probably due to the presence of the white collar which coincided with the max edge.

			In image set 4, we can see that none of the algorithms perform very well. Perhaps it is due to a combination of the orange-ish illuminant and the green-ish background in the painting, but we see that all of the color balancing methods biased towards a blue background. This accounts for the high delta E values. Although the results may look acceptable by themselves (such as Max Edge), the goal is to reproduce the most similar color to the original; therefore, we can only conclude that the methods fail in this case.

	== Image set 1 ==		== Image set 1 ==
	<center>[[File:rc_macbethresults.png \|1200px]]</center>		<center>[[File:rc_macbethresults.png \|1200px]]</center>

	~~We see that in this case, Gray World actually performs best due to the fact that~~

	== Image set 2 ==		== Image set 2 ==

imported>Psych2012: /* Methods */

2012-03-19T21:58:32Z

Methods

← Older revision		Revision as of 21:58, 19 March 2012
Line 30:		Line 30:
	So, we can take advantage of this and estimate the illuminant using the equations below, where h is our original image passed through a Gaussian filter to reduce noise.		So, we can take advantage of this and estimate the illuminant using the equations below, where h is our original image passed through a Gaussian filter to reduce noise.
	<center>[[File:rc_edgeeqn.png \|400px]]</center>		<center>[[File:rc_edgeeqn.png \|400px]]</center>


			== Gamut Mapping ==
			I used this particular implementation [1] due to the fact that this method applies gamut mapping to the derivative structure of images. The reasoning was to compare the performance of Gray Edge and Max Edge to a more complex approach. Since I did not implement this, please refer to [1] for details on the inner workings of this algorithm.

	= Results - What you found =		= Results - What you found =

imported>Psych2012: /* Results - What you found */

2012-03-19T21:57:45Z

Results - What you found

← Older revision		Revision as of 21:57, 19 March 2012
Line 37:		Line 37:
	== Image set 1 ==		== Image set 1 ==
	<center>[[File:rc_macbethresults.png \|1200px]]</center>		<center>[[File:rc_macbethresults.png \|1200px]]</center>

			We see that in this case, Gray World actually performs best due to the fact that

	== Image set 2 ==		== Image set 2 ==

imported>Psych2012: /* Image set 3 */

2012-03-19T21:53:19Z

Image set 3

← Older revision		Revision as of 21:53, 19 March 2012
Line 42:		Line 42:

	== Image set 3 ==		== Image set 3 ==
	<center>[[File:rc_results3.png \|~~1200px~~]]</center>		<center>[[File:rc_results3.png \|900px]]</center>

	== Image set 4 ==		== Image set 4 ==

imported>Psych2012: /* Image set 2 */

2012-03-19T21:49:52Z

Image set 2

← Older revision		Revision as of 21:49, 19 March 2012
Line 39:		Line 39:

	== Image set 2 ==		== Image set 2 ==
	<center>[[File:rc_results2.png \|~~1200px~~]]</center>		<center>[[File:rc_results2.png \|1000px]]</center>

	== Image set 3 ==		== Image set 3 ==