Smartphone Camera Quality - Revision history

imported>Student2017: /* Color Checker and Grey Card */

2017-12-15T09:30:56Z

Color Checker and Grey Card

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	===Color Checker and Grey Card===		===Color Checker and Grey Card===
	For the color checker/grey card image target, we computed the CIELAB ~~deltaE~~ value of each color patch imaged by the smartphone camera relative to the true patches in the color checker. We are using values measured in the lab with a spectrophotometer under the same illuminant conditions (the daylight illuminant in the light booth) as the true values for each patch in the color checker and the grey card as well as the white point. The ~~deltaE~~ value is on the vertical axis in the plot. A value of zero means that it would be difficult to distinguish the color produced by the smartphone camera from the true color in the color checker, while a positive value means that it would be easier to distinguish the difference.		For the color checker/grey card image target, we computed the CIELAB ∆E value of each color patch imaged by the smartphone camera relative to the true patches in the color checker. We are using values measured in the lab with a spectrophotometer under the same illuminant conditions (the daylight illuminant in the light booth) as the true values for each patch in the color checker and the grey card as well as the white point. The ∆E value is on the vertical axis in the plot. A value of zero means that it would be difficult to distinguish the color produced by the smartphone camera from the true color in the color checker, while a positive value means that it would be easier to distinguish the difference.

	Prior to data collection, we expected each smartphone camera to have values that were non-zero, and that the relative magnitude of the ~~deltaE~~ values would be somewhat consistent across smartphones made by the same manufacturer (i.e. all iPhones should produce images with similar colors). As can be seen in the plots, the 6th-generation and most recent generation iPhones appear to be fairly consistent unit-to-unit, while the 7th-generation iPhones appear to show more unit-to-unit variation. For the dark skin patch, the iPhone models appear to have a similar magnitude relative to zero, but for other color patches, we do not see this trend.		Prior to data collection, we expected each smartphone camera to have values that were non-zero, and that the relative magnitude of the ∆E values would be somewhat consistent across smartphones made by the same manufacturer (i.e. all iPhones should produce images with similar colors). As can be seen in the plots, the 6th-generation and most recent generation iPhones appear to be fairly consistent unit-to-unit, while the 7th-generation iPhones appear to show more unit-to-unit variation. For the dark skin patch, the iPhone models appear to have a similar magnitude relative to zero, but for other color patches, we do not see this trend.

	Our results for the grey card are given below:		Our results for the grey card are given below:

imported>Student2017: /* Grey Card */

2017-12-15T09:29:44Z

Grey Card

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	#Accuracy: Was the average grey color similar to that of the known value?		#Accuracy: Was the average grey color similar to that of the known value?
	#Consistency: Was there a large spread between the grey pixels across the field of view?		#Consistency: Was there a large spread between the grey pixels across the field of view?
	Since the images were now consistent, we were able to hardcode in the locations (as percentages of the width and height since different cameras had different resolutions) that corresponded to the dimensions of the grey card. Since each pixel had an RGB value, we were able to compute the ∇E value for each pixel relative to the known grey with a white point that was measured in the light booth. However, when this proved to be too computationally demanding, we ended up instead determining the average red, green, and blue values and then only computing the ∇E with those values. Due to the nonlinear properties of the CIELAB 1976 functions, we knew this value would not be the true average, but we determined that since our main objective was the compare the difference in results (instead of absolute results) that this would be a consistent offset for all samples. Therefore, we would still be able to compare ∇E values relative to each other. This average ∇E value was returned.		Since the images were now consistent, we were able to hardcode in the locations (as percentages of the width and height since different cameras had different resolutions) that corresponded to the dimensions of the grey card. Since each pixel had an RGB value, we were able to compute the ∇E value for each pixel relative to the known grey with a white point that was measured in the light booth. However, when this proved to be too computationally demanding, we ended up instead determining the average red, green, and blue values and then only computing the ∆E with those values. Due to the nonlinear properties of the CIELAB 1976 functions, we knew this value would not be the true average, but we determined that since our main objective was the compare the difference in results (instead of absolute results) that this would be a consistent offset for all samples. Therefore, we would still be able to compare ∆E values relative to each other. This average ∆E value was returned.
	In order to determine the spread of the variations, we paired together both the minimum red, green, and blue values as well as the maximum ones. Since the final step in computing the ∇E is the Euclidean distance of the sample and known L^,a^,b^* values, the value will always be positive. Therefore, the maximum ∆E created by either the min or max RGB values was returned to show the magnitude of the spread of color.		In order to determine the spread of the variations, we paired together both the minimum red, green, and blue values as well as the maximum ones. Since the final step in computing the ∆E is the Euclidean distance of the sample and known L^,a^,b^* values, the value will always be positive. Therefore, the maximum ∆E created by either the min or max RGB values was returned to show the magnitude of the spread of color.

	===Color Checker===		===Color Checker===

imported>Student2017: /* Image Standardization */

2017-12-15T09:29:06Z

Image Standardization

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	Once all photos were collected and uploaded, it was time to start processing the images. Even though we did our best to control the exact image that a camera should see, varying resolutions, fields of view, and even the slight change in tilt based on how heavily the photographer pressed the camera against our light booth, provided a surprising amount of contrast between the pictures. Since it would have been a poor use of time to manually crop 500 images, the first thing we had to do was standardize each of the samples using the following process:		Once all photos were collected and uploaded, it was time to start processing the images. Even though we did our best to control the exact image that a camera should see, varying resolutions, fields of view, and even the slight change in tilt based on how heavily the photographer pressed the camera against our light booth, provided a surprising amount of contrast between the pictures. Since it would have been a poor use of time to manually crop 500 images, the first thing we had to do was standardize each of the samples using the following process:

	*'''Rotating the photo 2 degrees counterclockwise''' – Since Matlab is only capable of cropping photos into perfect rectangles, it was important to find out where the corners of the edges existed. Built-in Matlab functions (discussed next) are capable of detecting where edges exist in an image, but these edges are stored as arrays of 2-dimensional points. Since Matlab would not know the what shape these points were forming, we extracted the maximum and minimum X and Y coordinates in order to identify which points identified the corners of the rectangle. Since the images were imperfect, there would be cases where the extreme values would be in unexpected locations. Therefore, the image was rotated to ensure that the top left corner ~~had~~ was located at the minimum X coordinate. While rotation may have introduced error into the equation, the expectation is that this experiment is comparing the difference between similar phones, so the error would be present but offset in all photos.		*'''Rotating the photo 2 degrees counterclockwise''' – Since Matlab is only capable of cropping photos into perfect rectangles, it was important to find out where the corners of the edges existed. Built-in Matlab functions (discussed next) are capable of detecting where edges exist in an image, but these edges are stored as arrays of 2-dimensional points. Since Matlab would not know the what shape these points were forming, we extracted the maximum and minimum X and Y coordinates in order to identify which points identified the corners of the rectangle. Since the images were imperfect, there would be cases where the extreme values would be in unexpected locations. Therefore, the image was rotated to ensure that the top left corner was located at the minimum X coordinate. While rotation may have introduced error into the equation, the expectation is that this experiment is comparing the difference between similar phones, so the error would be present but offset in all photos.

	*'''Detecting the edges in the photo''' – Matlab has quite a few built in functions for detecting the edges of the photo. We ended up having the most success converting the photo to grayscale (rgb2gray), binarizing the photo using the Canny method with a threshold varying between 0.1 and 0.5 (edge), and grouping these resultant values together based on similar locations (bwboundaries).		*'''Detecting the edges in the photo''' – Matlab has quite a few built in functions for detecting the edges of the photo. We ended up having the most success converting the photo to grayscale (rgb2gray), binarizing the photo using the Canny method with a threshold varying between 0.1 and 0.5 (edge), and grouping these resultant values together based on similar locations (bwboundaries).

imported>Student2017: /* Appendix I */

2017-12-15T09:11:28Z

Appendix I

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	*[[Media:MfakCode.zip\|Matlab Code]]		*[[Media:MfakCode.zip\|Matlab Code]]
	*~~Image targets (~~PowerPoint ~~file)~~		*[[Media:MfakImageCharts.zip\|PowerPoint files for grid of dots + grid of lines]]
	*[[Media:MfakPresentation.pdf\|Final Project Presentation]]		*[[Media:MfakPresentation.pdf\|Final Project Presentation]]
	*[[Media:MfakSpd.zip\|Measured SPD Files]]		*[[Media:MfakSpd.zip\|Measured SPD Files]]

imported>Student2017: /* Appendix I */

2017-12-15T09:09:24Z

Appendix I

← Older revision		Revision as of 09:09, 15 December 2017
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	=Appendix I=		=Appendix I=

	*~~Image processing code (~~Matlab ~~file)~~		*[[Media:MfakCode.zip\|Matlab Code]]
	*Image targets (PowerPoint file)		*Image targets (PowerPoint file)
	*[[Media:MfakPresentation.pdf\|Final Project Presentation]]		*[[Media:MfakPresentation.pdf\|Final Project Presentation]]
	*Measured SPD ~~files (zip file)~~		*[[Media:MfakSpd.zip\|Measured SPD Files]]
	*SPD processing code (Matlab file)
	*Color Checker: https://www.amazon.com/gp/product/B000JLO31C/ref=oh_aui_detailpage_o03_s00?ie=UTF8&psc=1		*Color Checker: https://www.amazon.com/gp/product/B000JLO31C/ref=oh_aui_detailpage_o03_s00?ie=UTF8&psc=1
	*Grey card: https://www.amazon.com/gp/product/B074W1VF2Y/ref=oh_aui_detailpage_o03_s00?ie=UTF8&psc=1		*Grey card: https://www.amazon.com/gp/product/B074W1VF2Y/ref=oh_aui_detailpage_o03_s00?ie=UTF8&psc=1

imported>Student2017: /* Appendix I */

2017-12-15T09:08:25Z

Appendix I

← Older revision		Revision as of 09:08, 15 December 2017
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	=Appendix I=		=Appendix I=

	*Summary of data collected (Excel spreadsheet)
	*Image processing code (Matlab file)		*Image processing code (Matlab file)
	*Image targets (PowerPoint file)		*Image targets (PowerPoint file)
	*[[~~File~~:MfakPresentation.pdf\|Final Project Presentation]]		*[[Media:MfakPresentation.pdf\|Final Project Presentation]]
	*Measured SPD files (zip file)		*Measured SPD files (zip file)
	*SPD processing code (Matlab file)		*SPD processing code (Matlab file)

imported>Student2017: /* Data Collection */

2017-12-15T09:03:18Z

Data Collection

← Older revision		Revision as of 09:03, 15 December 2017
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	We encouraged participants to complete the form while they were with us at the light booth, as there is the possibility that they would forget to submit after the fact. We used the form responses to measure our progress and number of participants and it does not appear that many, if any, took the photos but failed to complete the form. We spent most of our data collection time at events where attendees are interested in image systems engineering and our project topic, as we believed they would be more willing to participate and more likely to complete the process once they started. Over two weeks, we had 51 different participants take photos, with a total of 472 images uploaded (some people submitted additional photos beyond the required nine).		We encouraged participants to complete the form while they were with us at the light booth, as there is the possibility that they would forget to submit after the fact. We used the form responses to measure our progress and number of participants and it does not appear that many, if any, took the photos but failed to complete the form. We spent most of our data collection time at events where attendees are interested in image systems engineering and our project topic, as we believed they would be more willing to participate and more likely to complete the process once they started. Over two weeks, we had 51 different participants take photos, with a total of 472 images uploaded (some people submitted additional photos beyond the required nine).

	After we had received all of these form responses, we exported the metadata (responses to the questions) into a spreadsheet and the image files into a folder on our local machines for further processing. For the metadata, we created a unique ID for each participant and used that to associate their images with the correct smartphone model for analysis. A summary of the metadata is included in the Results section below.		After we had received all of these form responses, we exported the metadata (responses to the questions) into a spreadsheet and the image files into a folder on our local machines for further processing. For the metadata, we created a unique ID for each participant and used that to associate their images with the correct smartphone model for analysis. A summary of the metadata is included in the [[#Results\|Results]] section below.

	For each image target, we computed values to use as our evaluation metrics and compared these values across images captured by the same model of smartphone.		For each image target, we computed values to use as our evaluation metrics and compared these values across images captured by the same model of smartphone.

imported>Student2017: /* Image Targets */

2017-12-15T09:02:55Z

Image Targets

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	*'''Color checker + grey card:''' One X-Rite ColorChecker Classic and one 5.3 in x 7.28 in Anwenk 18% grey card affixed to the cardboard panel. Both were ordered online from Amazon.com.		*'''Color checker + grey card:''' One X-Rite ColorChecker Classic and one 5.3 in x 7.28 in Anwenk 18% grey card affixed to the cardboard panel. Both were ordered online from Amazon.com.

	*'''Grid of dots:''' A grid of 14 x 22 dots printed on 11 in x 17 in white paper at 1200 dpi. Each dot has diameter 0.12 in and 0.39 in spacing between dots. The dots were located inside of a 10 in x 16 in box with 0.5 in margins on all sides, with a blue outline of width 1 pt. The image target was created in PowerPoint and is attached in [[#Appendix II]].		*'''Grid of dots:''' A grid of 14 x 22 dots printed on 11 in x 17 in white paper at 1200 dpi. Each dot has diameter 0.12 in and 0.39 in spacing between dots. The dots were located inside of a 10 in x 16 in box with 0.5 in margins on all sides, with a blue outline of width 1 pt. The image target was created in PowerPoint and is attached in [[#Appendix I\|Appendix I]].

	*'''Grid of lines:''' A 10 in x 16 in grid of squares, each with side length 0.5 in. The lines are black and of width 1 pt. The grid has 0.5 in margins on all sides and is printed on 11 in x 17 in white paper at 1200 dpi. The image target was created in PowerPoint and is attached in [[#Appendix II]].		*'''Grid of lines:''' A 10 in x 16 in grid of squares, each with side length 0.5 in. The lines are black and of width 1 pt. The grid has 0.5 in margins on all sides and is printed on 11 in x 17 in white paper at 1200 dpi. The image target was created in PowerPoint and is attached in [[#Appendix I]\|Appendix I].

	{\|style="margin: 0 auto;"		{\|style="margin: 0 auto;"

imported>Student2017: /* Image Targets */

2017-12-15T09:02:14Z

Image Targets

← Older revision		Revision as of 09:02, 15 December 2017
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	*'''Color checker + grey card:''' One X-Rite ColorChecker Classic and one 5.3 in x 7.28 in Anwenk 18% grey card affixed to the cardboard panel. Both were ordered online from Amazon.com.		*'''Color checker + grey card:''' One X-Rite ColorChecker Classic and one 5.3 in x 7.28 in Anwenk 18% grey card affixed to the cardboard panel. Both were ordered online from Amazon.com.

	*'''Grid of dots:''' A grid of 14 x 22 dots printed on 11 in x 17 in white paper at 1200 dpi. Each dot has diameter 0.12 in and 0.39 in spacing between dots. The dots were located inside of a 10 in x 16 in box with 0.5 in margins on all sides, with a blue outline of width 1 pt. The image target was created in PowerPoint and is attached in [[Appendix II]].		*'''Grid of dots:''' A grid of 14 x 22 dots printed on 11 in x 17 in white paper at 1200 dpi. Each dot has diameter 0.12 in and 0.39 in spacing between dots. The dots were located inside of a 10 in x 16 in box with 0.5 in margins on all sides, with a blue outline of width 1 pt. The image target was created in PowerPoint and is attached in [[#Appendix II]].

	*'''Grid of lines:''' A 10 in x 16 in grid of squares, each with side length 0.5 in. The lines are black and of width 1 pt. The grid has 0.5 in margins on all sides and is printed on 11 in x 17 in white paper at 1200 dpi. The image target was created in PowerPoint and is attached in [[Appendix II]].		*'''Grid of lines:''' A 10 in x 16 in grid of squares, each with side length 0.5 in. The lines are black and of width 1 pt. The grid has 0.5 in margins on all sides and is printed on 11 in x 17 in white paper at 1200 dpi. The image target was created in PowerPoint and is attached in [[#Appendix II]].

	{\|style="margin: 0 auto;"		{\|style="margin: 0 auto;"

imported>Student2017: /* Image Targets */

2017-12-15T09:01:08Z

Image Targets

← Older revision		Revision as of 09:01, 15 December 2017
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	*'''Color checker + grey card:''' One X-Rite ColorChecker Classic and one 5.3 in x 7.28 in Anwenk 18% grey card affixed to the cardboard panel. Both were ordered online from Amazon.com.		*'''Color checker + grey card:''' One X-Rite ColorChecker Classic and one 5.3 in x 7.28 in Anwenk 18% grey card affixed to the cardboard panel. Both were ordered online from Amazon.com.

	*'''Grid of dots:''' A grid of 14 x 22 dots printed on 11 in x 17 in white paper at 1200 dpi. Each dot has diameter 0.12 in and 0.39 in spacing between dots. The dots were located inside of a 10 in x 16 in box with 0.5 in margins on all sides, with a blue outline of width 1 pt. The image target was created in PowerPoint and is attached in Appendix II.		*'''Grid of dots:''' A grid of 14 x 22 dots printed on 11 in x 17 in white paper at 1200 dpi. Each dot has diameter 0.12 in and 0.39 in spacing between dots. The dots were located inside of a 10 in x 16 in box with 0.5 in margins on all sides, with a blue outline of width 1 pt. The image target was created in PowerPoint and is attached in [[Appendix II]].

	*'''Grid of lines:''' A 10 in x 16 in grid of squares, each with side length 0.5 in. The lines are black and of width 1 pt. The grid has 0.5 in margins on all sides and is printed on 11 in x 17 in white paper at 1200 dpi. The image target was created in PowerPoint and is attached in Appendix II.		*'''Grid of lines:''' A 10 in x 16 in grid of squares, each with side length 0.5 in. The lines are black and of width 1 pt. The grid has 0.5 in margins on all sides and is printed on 11 in x 17 in white paper at 1200 dpi. The image target was created in PowerPoint and is attached in [[Appendix II]].

	{\|style="margin: 0 auto;"		{\|style="margin: 0 auto;"

← Older revision		Revision as of 09:11, 15 December 2017
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	*[[Media:MfakCode.zip\|Matlab Code]]		*[[Media:MfakCode.zip\|Matlab Code]]
	*~~Image targets (~~PowerPoint ~~file)~~		*[[Media:MfakImageCharts.zip\|PowerPoint files for grid of dots + grid of lines]]
	*[[Media:MfakPresentation.pdf\|Final Project Presentation]]		*[[Media:MfakPresentation.pdf\|Final Project Presentation]]
	*[[Media:MfakSpd.zip\|Measured SPD Files]]		*[[Media:MfakSpd.zip\|Measured SPD Files]]