Scielabfrontendoptics - Revision history

imported>Student2018: /* Appendix */

2018-12-16T10:57:09Z

Appendix

← Older revision		Revision as of 10:57, 16 December 2018
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	https://github.com/LCJebe/ColorCones		https://github.com/LCJebe/ColorCones

			We have also uploaded the results shown here plus a lot of additional examples on Google Drive:

			https://drive.google.com/drive/folders/1qOoLJDczsV-awDqCIU9doZJeh0IeN8iV?usp=sharing

	== References ==		== References ==

imported>Student2018: /* Spoke Targets */

2018-12-13T09:00:54Z

Spoke Targets

← Older revision		Revision as of 09:00, 13 December 2018
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	We observe a similar interesting behavior when comparing very similar colors with a small ∆E (Figure 12).		We observe a similar interesting behavior when comparing very similar colors with a small ∆E (Figure 12).

	[[File:FEO_magenta_comparisons.png\|300px\|thumb\|~~none~~\|'''Figure 12 '''Error maps for two pairs of magenta spokes. The first pair has a 5% difference in the blue channel, and the second pair has a 5% difference in all channels. Left: S-CIELAB, Right: CIELAB]]		[[File:FEO_magenta_comparisons.png\|300px\|thumb\|center\|'''Figure 12 '''Error maps for two pairs of magenta spokes. The first pair has a 5% difference in the blue channel, and the second pair has a 5% difference in all channels. Left: S-CIELAB, Right: CIELAB]]

	In the first comparison, we compared two magenta scenes that had a slight difference in the blue channel (5%). It seems that S-CIELAB is overestimating the ability to discriminate high spatial frequencies. In the second case, we use a magenta spoke target again, but this time all three channels have a 5% color change. As expected, the S-CIELAb ∆E becomes larger, and according to S-CIELAB its a bit easier now to discriminate higher spatial frequencies. This behavior, however, does not show up in the∆E of the cone responses. Instead, the “hole” in the middle of the ∆E of the cone responses appears bigger than it did in the first case. Again, we can also find cases, where S-CIELAB underestimated the ability to discriminate colors, as shown in Figure 13.		In the first comparison, we compared two magenta scenes that had a slight difference in the blue channel (5%). It seems that S-CIELAB is overestimating the ability to discriminate high spatial frequencies. In the second case, we use a magenta spoke target again, but this time all three channels have a 5% color change. As expected, the S-CIELAb ∆E becomes larger, and according to S-CIELAB its a bit easier now to discriminate higher spatial frequencies. This behavior, however, does not show up in the∆E of the cone responses. Instead, the “hole” in the middle of the ∆E of the cone responses appears bigger than it did in the first case. Again, we can also find cases, where S-CIELAB underestimated the ability to discriminate colors, as shown in Figure 13.

	[[File:FEO_spoke_bg.png\|300px\|thumb\|~~none~~\|'''Figure 13 '''Error maps for two green-blue spokes, one with a 5% difference in each channel. Left: S-CIELAB, Right: CIELAB]]		[[File:FEO_spoke_bg.png\|300px\|thumb\|center\|'''Figure 13 '''Error maps for two green-blue spokes, one with a 5% difference in each channel. Left: S-CIELAB, Right: CIELAB]]

	Here, it appears as if the cone responses recorded a higher spatial resolution of the two slightly different colors (Green-Blue with vs. Green-Blue with a 5% difference in each channel) than was predicted by S-CIELAB.		Here, it appears as if the cone responses recorded a higher spatial resolution of the two slightly different colors (Green-Blue with vs. Green-Blue with a 5% difference in each channel) than was predicted by S-CIELAB.

imported>Student2018: /* Noise Analysis */

2018-12-13T09:00:18Z

Noise Analysis

← Older revision		Revision as of 09:00, 13 December 2018
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	We found that the SNR (introduced by mainly photon / absorption noise) differs significantly depending on the color. Overall, blue cones show the fewest absorptions and the lowest SNR. We conducted the experiments for an integration time of 50ms, acknowledging that changing the integration time would only scales the photon noise by a constant factor and not yield significant additional information. Figure 5 shows the SNR for the cone responses. We labeled the Macbeth Colorchecker according to the scheme shown in Figure 4. From Figure 5 we can see that the SNR is generally higher for the L and M cones than it is for the S cones. Furthermore, the more absorptions we expect due to the color, the higher the SNR. This effect is especially visible for the white square, where all cones have high absorption rates.		We found that the SNR (introduced by mainly photon / absorption noise) differs significantly depending on the color. Overall, blue cones show the fewest absorptions and the lowest SNR. We conducted the experiments for an integration time of 50ms, acknowledging that changing the integration time would only scales the photon noise by a constant factor and not yield significant additional information. Figure 5 shows the SNR for the cone responses. We labeled the Macbeth Colorchecker according to the scheme shown in Figure 4. From Figure 5 we can see that the SNR is generally higher for the L and M cones than it is for the S cones. Furthermore, the more absorptions we expect due to the color, the higher the SNR. This effect is especially visible for the white square, where all cones have high absorption rates.

	[[File:FEO_004ColorCheck.png\|200px\|thumb\|~~none~~\|'''Figure 4:''' Macbeth Color Checker]]		[[File:FEO_004ColorCheck.png\|200px\|thumb\|center\|'''Figure 4:''' Macbeth Color Checker]]
	[[File:FEO_005SNR.png\|400px\|thumb\|~~none~~\|'''Figure 5:''' SNR by cone type for each color]]		[[File:FEO_005SNR.png\|400px\|thumb\|center\|'''Figure 5:''' SNR by cone type for each color]]
	We also calculated the CIELAB ∆E between two instances of cone responses that result from two identical scenes, to get a feeling for how much of a color (∆E) difference the noise is responsible for (see Figure 6.1). In order to reduce the influence of photon noise on our experiments, we chose the integration time to be 1000s, which resulted in a ∆E of 0.7 or less, as shown in Figure 6.2.		We also calculated the CIELAB ∆E between two instances of cone responses that result from two identical scenes, to get a feeling for how much of a color (∆E) difference the noise is responsible for (see Figure 6.1). In order to reduce the influence of photon noise on our experiments, we chose the integration time to be 1000s, which resulted in a ∆E of 0.7 or less, as shown in Figure 6.2.

	[[File:FEO_006DeltaE.png\|400px\|thumb\|~~left~~\|'''Figure 6.1:''' ∆E for each color with 50ms integration time]]		[[File:FEO_006DeltaE.png\|400px\|thumb\|center\|'''Figure 6.1:''' ∆E for each color with 50ms integration time]]
	[[File:FEO_007HighExposureDeltaE.png\|400px\|thumb\|~~none~~\|'''Figure 6.2:'''∆E for each color with 1000s integration time]]		[[File:FEO_007HighExposureDeltaE.png\|400px\|thumb\|center\|'''Figure 6.2:'''∆E for each color with 1000s integration time]]

	===Uniform Squares===		===Uniform Squares===

imported>Student2018: /* Spoke Targets */

2018-12-13T08:59:48Z

Spoke Targets

← Older revision		Revision as of 08:59, 13 December 2018
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	===Spoke Targets===		===Spoke Targets===
	In the last experiment, we record the two error measurements (S-CIELAB between the scenes and CIELAB between cone responses) as we did in the uniform squares experiment. Two different modes of comparison are made: The first mode is a comparison of a spoke with two colors, and a spoke with the same two colors, but with a phase shift of 180° (see Figure 2.1). The second mode is a comparison between a bi-colored spoke and a spoke where one color is slightly changed (see Figure 2.2). We started out by comparing colors that have a very large ∆E, but we also compared colors that have an almost unnoticeable ∆E, shown in Figure 2.3.		In the last experiment, we record the two error measurements (S-CIELAB between the scenes and CIELAB between cone responses) as we did in the uniform squares experiment. Two different modes of comparison are made: The first mode is a comparison of a spoke with two colors, and a spoke with the same two colors, but with a phase shift of 180° (see Figure 2.1). The second mode is a comparison between a bi-colored spoke and a spoke where one color is slightly changed (see Figure 2.2). We started out by comparing colors that have a very large ∆E, but we also compared colors that have an almost unnoticeable ∆E, shown in Figure 2.3.
	[[File:FEO_001Spoke.png\|200px\|thumb\|~~left~~\|'''Figure 2.1:''' Two bi-colored spoke targets with a 180° phase shift]]		[[File:FEO_001Spoke.png\|200px\|thumb\|center\|'''Figure 2.1:''' Two bi-colored spoke targets with a 180° phase shift]]
	[[File:FEO_002_Spoke.png\|200px\|thumb\|~~none~~\|'''Figure 2.2:''' Two bi-colored spoke targets where one color is different]]		[[File:FEO_002_Spoke.png\|200px\|thumb\|center\|'''Figure 2.2:''' Two bi-colored spoke targets where one color is different]]
	[[File:FEO_003_Spoke.png\|200px\|thumb\|~~none~~\|'''Figure 2.3:''' Two bi-colored spoke targets (180° shift) of very similar colors]]		[[File:FEO_003_Spoke.png\|200px\|thumb\|center\|'''Figure 2.3:''' Two bi-colored spoke targets (180° shift) of very similar colors]]

	==Results==		==Results==

imported>Student2018: /* Experiment Design */

2018-12-13T08:59:33Z

Experiment Design

← Older revision		Revision as of 08:59, 13 December 2018
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	We start by analyzing the noise that comes from the cone responses. This is necessary for us to interpret the results correctly, since we need to know which effects show up a as a result of noise and which ones show up as a result of S-CIELAB calculation or the front-end optics model (excluding noise). While the results below show the noise for an integration time of 50ms, we got rid of noise of the noise by using an integration time of 1000s for most experiments. In that case, we observed a maximum ∆E due to noise of about 0.7. We then compare two scenes of two uniformly colored squares of different colors to get information about the dependency of S-CIELAB, CIELAB, and the front-end optics model in complete absence of spatial frequencies. In the last experiment, we add the frequency component to the experiment by comparing spoke targets of different colors. Spoke targets contain spatial frequencies across a wide spectrum and are well suited to test to which extent S-CIELAB models front end optics. Figure 1 shows the general experiment setup for all three experiments.		We start by analyzing the noise that comes from the cone responses. This is necessary for us to interpret the results correctly, since we need to know which effects show up a as a result of noise and which ones show up as a result of S-CIELAB calculation or the front-end optics model (excluding noise). While the results below show the noise for an integration time of 50ms, we got rid of noise of the noise by using an integration time of 1000s for most experiments. In that case, we observed a maximum ∆E due to noise of about 0.7. We then compare two scenes of two uniformly colored squares of different colors to get information about the dependency of S-CIELAB, CIELAB, and the front-end optics model in complete absence of spatial frequencies. In the last experiment, we add the frequency component to the experiment by comparing spoke targets of different colors. Spoke targets contain spatial frequencies across a wide spectrum and are well suited to test to which extent S-CIELAB models front end optics. Figure 1 shows the general experiment setup for all three experiments.

	[[File:FEO_000Setup.png\|~~600px~~\|thumb\|center\|'''Figure 1:''' Experiment Setup]]		[[File:FEO_000Setup.png\|450px\|thumb\|center\|'''Figure 1:''' Experiment Setup]]

	===Noise Analysis===		===Noise Analysis===

imported>Student2018: /* Experiment Design */

2018-12-13T08:59:23Z

Experiment Design

← Older revision		Revision as of 08:59, 13 December 2018
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	We start by analyzing the noise that comes from the cone responses. This is necessary for us to interpret the results correctly, since we need to know which effects show up a as a result of noise and which ones show up as a result of S-CIELAB calculation or the front-end optics model (excluding noise). While the results below show the noise for an integration time of 50ms, we got rid of noise of the noise by using an integration time of 1000s for most experiments. In that case, we observed a maximum ∆E due to noise of about 0.7. We then compare two scenes of two uniformly colored squares of different colors to get information about the dependency of S-CIELAB, CIELAB, and the front-end optics model in complete absence of spatial frequencies. In the last experiment, we add the frequency component to the experiment by comparing spoke targets of different colors. Spoke targets contain spatial frequencies across a wide spectrum and are well suited to test to which extent S-CIELAB models front end optics. Figure 1 shows the general experiment setup for all three experiments.		We start by analyzing the noise that comes from the cone responses. This is necessary for us to interpret the results correctly, since we need to know which effects show up a as a result of noise and which ones show up as a result of S-CIELAB calculation or the front-end optics model (excluding noise). While the results below show the noise for an integration time of 50ms, we got rid of noise of the noise by using an integration time of 1000s for most experiments. In that case, we observed a maximum ∆E due to noise of about 0.7. We then compare two scenes of two uniformly colored squares of different colors to get information about the dependency of S-CIELAB, CIELAB, and the front-end optics model in complete absence of spatial frequencies. In the last experiment, we add the frequency component to the experiment by comparing spoke targets of different colors. Spoke targets contain spatial frequencies across a wide spectrum and are well suited to test to which extent S-CIELAB models front end optics. Figure 1 shows the general experiment setup for all three experiments.

	[[File:FEO_000Setup.png\|~~300px~~\|thumb\|~~right~~\|'''Figure 1:''' Experiment Setup]]		[[File:FEO_000Setup.png\|600px\|thumb\|center\|'''Figure 1:''' Experiment Setup]]

	===Noise Analysis===		===Noise Analysis===

imported>Student2018: /* Spoke Targets */

2018-12-13T08:57:34Z

Spoke Targets

← Older revision		Revision as of 08:57, 13 December 2018
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	===Spoke Targets===		===Spoke Targets===
	~~When comparing~~ spoke targets ~~with~~ the ~~same color combinations that were chosen for~~ the ~~uniform squares experiment, we observed a slightly different behavior~~. Figure 8 and 9 show a 180° phase shift comparison of a magenta / violet target.		To add the spatial frequency component to the experiments, we now compare spoke targets that contain low patial frequencies on the outside and high spatial frequencies on the inside. Figure 10.1 and 10.2 show a 180° phase shift comparison of a magenta / violet target, and Figure 11.1 and 11.2 show a comparison of a yellow blue target, where the ∆E difference between the two colors is even bigger than in the magenta / violet case.
	~~In this example~~, the ∆E ~~of the unbalanced image with white point 1 is closest to the S-CIELAB ∆E~~ between the two ~~scenes, shown on top.~~
	~~This~~ is ~~not always~~ the case~~, as the example in Figure 10~~.~~1 and 10.2 shows:~~

	[[File:FEO_008MagentaSpokes.png\|170px\|thumb\|left\|'''Figure 10.1:''' Magenta / Violet 180° phase shift visual comparison between scene (left) and reconstruction (right)]]		[[File:FEO_008MagentaSpokes.png\|170px\|thumb\|left\|'''Figure 10.1:''' Magenta / Violet 180° phase shift visual comparison between scene (left) and reconstruction (right)]]
	[[File:FEO_009MagentaError.png\|500px\|thumb\|none\|'''Figure 10.2:''' S-CIELAB ∆E between input scenes (left) and CIELAB ∆E between cone responses (right)]]		[[File:FEO_009MagentaError.png\|500px\|thumb\|none\|'''Figure 10.2:''' S-CIELAB ∆E between input scenes (left) and CIELAB ∆E between cone responses (right)]]

	[[File:FEO_010BlueSpokes.png\|170px\|thumb\|left\|'''Figure 11.1: '''~~Magenta~~ / ~~Violet~~ 180° phase shift visual comparison between scene (left) and reconstruction (right)]]		In this case, it seems that the S-CIELAB ∆E is overestimating the ability to see high spatial frequencies in the magenta / violet case. That can be seen from the size of the "circle / hole" in the middle of the error map, which is larger in the cone responses than in the input scene. Let's see whether that's the case for the high contrast blue / yellow scene as well (Figure 11.1 and 11.2).

			[[File:FEO_010BlueSpokes.png\|170px\|thumb\|left\|'''Figure 11.1: '''Blue / Yellow 180° phase shift visual comparison between scene (left) and reconstruction (right)]]
	[[File:FEO_spoke_blueyellow_comparisons.png\|500px\|thumb\|none\|'''Figure 11.2: '''S-CIELAB ∆E between input scenes (left) and CIELAB ∆E between cone responses (right)]]		[[File:FEO_spoke_blueyellow_comparisons.png\|500px\|thumb\|none\|'''Figure 11.2: '''S-CIELAB ∆E between input scenes (left) and CIELAB ∆E between cone responses (right)]]

	In ~~this example~~, the mean S-CIELAB ∆E is very high~~, since we are comparing blue and yellow~~. The mean ∆E of the reconstructed images is lower than the ∆E of the scenes. This is a recurring pattern we already noticed when comparing uniform squares. In addition, since the colors are fundamentally different, we can see that it’s much easier for the human eye to see high spatial frequencies, compared to the magenta / violet case. The error images show the high spatial frequencies very well, meaning that the human eye can still discriminate those frequencies. Is seems that S-CIELAB does not capture this relation as well as the cone responses do. The ~~“inner circle”~~ of the spoke target (that is, the threshold above which high spatial frequencies can’t be seen anymore) seems to be larger in the S-CIELAB ∆E, meaning the threshold lies at a lower frequency, than the cone responses suggest.		In the blue / yellow case, the mean S-CIELAB ∆E is very high. The mean ∆E of the reconstructed images is lower than the ∆E of the scenes. This is a recurring pattern we already noticed when comparing uniform squares. In addition, since the colors are fundamentally different, we can see that it’s much easier for the human eye to see high spatial frequencies, compared to the magenta / violet case. The error images show the high spatial frequencies very well, meaning that the human eye can still discriminate those frequencies, which is intuitive given the larger contrast between blue and yellow compared to magenta and violet. Is seems that S-CIELAB does not capture this relation as well as the cone responses do. The “circle / hole ” of the spoke target (that is, the threshold above which high spatial frequencies can’t be seen anymore) seems to be larger in the S-CIELAB ∆E, meaning the threshold lies at a lower frequency, than the cone responses suggest.

	~~We observe a similar interesting behavior when comparing very similar colors with a small ∆E (Figure XX).~~


			We observe a similar interesting behavior when comparing very similar colors with a small ∆E (Figure 12).

	[[File:FEO_magenta_comparisons.png\|300px\|thumb\|none\|'''Figure 12.1 '''Error maps for two pairs of magenta spokes. The first pair has a 5% difference in the blue channel, and the second pair has a 5% difference in all channels. Left: S-CIELAB, Right: CIELAB]]		[[File:FEO_magenta_comparisons.png\|300px\|thumb\|none\|'''Figure 12 '''Error maps for two pairs of magenta spokes. The first pair has a 5% difference in the blue channel, and the second pair has a 5% difference in all channels. Left: S-CIELAB, Right: CIELAB]]

	In the first comparison, we compared two magenta scenes that had a slight difference in the blue channel (5%). It seems that S-CIELAB is overestimating the ability to discriminate high spatial frequencies. In the second case, we use a magenta spoke target again, but this time all three channels have a 5% color change. As expected, the S-CIELAb ∆E becomes larger, and according to S-CIELAB its a bit easier now to discriminate higher spatial frequencies. This behavior, however, does not show up in the∆E of the cone responses. Instead, the “hole” in the middle of the ∆E of the cone responses appears bigger than it did in the first case. Again, we can also find cases, where S-CIELAB underestimated the ability to discriminate colors, as shown in Figure XX.		In the first comparison, we compared two magenta scenes that had a slight difference in the blue channel (5%). It seems that S-CIELAB is overestimating the ability to discriminate high spatial frequencies. In the second case, we use a magenta spoke target again, but this time all three channels have a 5% color change. As expected, the S-CIELAb ∆E becomes larger, and according to S-CIELAB its a bit easier now to discriminate higher spatial frequencies. This behavior, however, does not show up in the∆E of the cone responses. Instead, the “hole” in the middle of the ∆E of the cone responses appears bigger than it did in the first case. Again, we can also find cases, where S-CIELAB underestimated the ability to discriminate colors, as shown in Figure 13.

	[[File:FEO_spoke_bg.png\|300px\|thumb\|none\|'''Figure ~~12.2~~ '''Error maps for two green-blue spokes, one with a 5% difference in each channel. Left: S-CIELAB, Right: CIELAB]]		[[File:FEO_spoke_bg.png\|300px\|thumb\|none\|'''Figure 13 '''Error maps for two green-blue spokes, one with a 5% difference in each channel. Left: S-CIELAB, Right: CIELAB]]

	Here, it appears as if the cone responses recorded a higher spatial resolution of the two slightly different colors (Green-Blue with vs. Green-Blue with a 5% difference in each channel) than was predicted by S-CIELAB.		Here, it appears as if the cone responses recorded a higher spatial resolution of the two slightly different colors (Green-Blue with vs. Green-Blue with a 5% difference in each channel) than was predicted by S-CIELAB.

imported>Student2018: /* Spoke Targets */

2018-12-13T08:48:49Z

Spoke Targets

← Older revision		Revision as of 08:48, 13 December 2018
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	When comparing spoke targets with the same color combinations that were chosen for the uniform squares experiment, we observed a slightly different behavior. Figure 8 and 9 show a 180° phase shift comparison of a magenta / violet target.		When comparing spoke targets with the same color combinations that were chosen for the uniform squares experiment, we observed a slightly different behavior. Figure 8 and 9 show a 180° phase shift comparison of a magenta / violet target.
	In this example, the ∆E of the unbalanced image with white point 1 is closest to the S-CIELAB ∆E between the two scenes, shown on top.		In this example, the ∆E of the unbalanced image with white point 1 is closest to the S-CIELAB ∆E between the two scenes, shown on top.
	This is not always the case, as the example in Figure ?.1 and ?.2 shows:		This is not always the case, as the example in Figure 10.1 and 10.2 shows:

	[[File:FEO_008MagentaSpokes.png\|170px\|thumb\|left\|'''Figure 10.1:''' Magenta / Violet 180° phase shift visual comparison between scene (left) and reconstruction (right)]]		[[File:FEO_008MagentaSpokes.png\|170px\|thumb\|left\|'''Figure 10.1:''' Magenta / Violet 180° phase shift visual comparison between scene (left) and reconstruction (right)]]

imported>Student2018: /* Spoke Targets */

2018-12-13T08:47:16Z

Spoke Targets

← Older revision		Revision as of 08:47, 13 December 2018
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	[[File:FEO_magenta_comparisons.png\|300px\|thumb\|none\|'''Figure 12.1 '''Error maps for two pairs of magenta spokes. The first pair has a 5% difference in the blue channel, and the second pair has a 5% difference in all channels.]]		[[File:FEO_magenta_comparisons.png\|300px\|thumb\|none\|'''Figure 12.1 '''Error maps for two pairs of magenta spokes. The first pair has a 5% difference in the blue channel, and the second pair has a 5% difference in all channels. Left: S-CIELAB, Right: CIELAB]]

	In the first comparison, we compared two magenta scenes that had a slight difference in the blue channel (5%). It seems that S-CIELAB is overestimating the ability to discriminate high spatial frequencies. In the second case, we use a magenta spoke target again, but this time all three channels have a 5% color change. As expected, the S-CIELAb ∆E becomes larger, and according to S-CIELAB its a bit easier now to discriminate higher spatial frequencies. This behavior, however, does not show up in the∆E of the cone responses. Instead, the “hole” in the middle of the ∆E of the cone responses appears bigger than it did in the first case. Again, we can also find cases, where S-CIELAB underestimated the ability to discriminate colors, as shown in Figure XX.		In the first comparison, we compared two magenta scenes that had a slight difference in the blue channel (5%). It seems that S-CIELAB is overestimating the ability to discriminate high spatial frequencies. In the second case, we use a magenta spoke target again, but this time all three channels have a 5% color change. As expected, the S-CIELAb ∆E becomes larger, and according to S-CIELAB its a bit easier now to discriminate higher spatial frequencies. This behavior, however, does not show up in the∆E of the cone responses. Instead, the “hole” in the middle of the ∆E of the cone responses appears bigger than it did in the first case. Again, we can also find cases, where S-CIELAB underestimated the ability to discriminate colors, as shown in Figure XX.

	[[File:FEO_spoke_bg.png\|300px\|thumb\|none\|'''Figure 12.2 '''Error maps for two green-blue spokes, one with a 5% difference in each channel.]]		[[File:FEO_spoke_bg.png\|300px\|thumb\|none\|'''Figure 12.2 '''Error maps for two green-blue spokes, one with a 5% difference in each channel. Left: S-CIELAB, Right: CIELAB]]

	Here, it appears as if the cone responses recorded a higher spatial resolution of the two slightly different colors (Green-Blue with vs. Green-Blue with a 5% difference in each channel) than was predicted by S-CIELAB.		Here, it appears as if the cone responses recorded a higher spatial resolution of the two slightly different colors (Green-Blue with vs. Green-Blue with a 5% difference in each channel) than was predicted by S-CIELAB.

imported>Student2018: /* Uniform Squares */

2018-12-13T08:44:24Z

Uniform Squares

← Older revision		Revision as of 08:44, 13 December 2018
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	Figure 9 shows the measurements and calculated metrics for each of the 28 experiments, as well as the average ∆E for S-CIELAB, the cone responses, and the average ratio between ∆E of cones and ∆E of S-CIELAB.		Figure 9 shows the measurements and calculated metrics for each of the 28 experiments, as well as the average ∆E for S-CIELAB, the cone responses, and the average ratio between ∆E of cones and ∆E of S-CIELAB.

	[[File:FEO_uniform_square_results.png\|center\|678px\|thumb\|center\|'''Figure 9:''' ∆E values calculated from similarly colored uniform squares.]]		[[File:FEO_uniform_square_results.png\|center\|678px\|thumb\|center\|'''Figure 9:''' ∆E values calculated from similarly colored uniform squares. For each color, the experiments from left to right are changing all 3 channels, changing the red channel, changing the green channel, and changing the blue channel.]]

	===Spoke Targets===		===Spoke Targets===