3M’s Display Quality Score3M’s tool to guide the development of higher quality displays
How can we build a great display?
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How can we build a great display?
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Approach 1: Copy the physics
Limited by current technology and efficiency considerations
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Approach 2: Recreate Visual Response
Because the visual system does not encode all of the information in the light field, recreating a visual experience does not require recreating the light field with perfect fidelity
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Approach 2: Recreate Visual Response
DQS uses knowledge of the visual system and visual preferences to guide developers toward displays that create superior visual experience
Quantitative accurate prediction of how human quality judgment is influenced by engineered display parameters (resolution, luminance, etc.) and thereby avoid costly experimentation with human observers
Goal of the 3M DQS
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Allow display engineers to identify display attributes and combinations of attributes that have the largest impact on image quality
Purpose of the 3M DQS
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Background and Current Status
Image quality metrics for hardware and software identify 6 dimensions underlying image quality judgment1 : • Color, Luminance (Brightness/lightness), Contrast,
Resolution/Sharpness, Arifacts (e.g. non-uniformity, noise), Parameters of the display/print media (e.g. screen gloss)
DQS v1.0 characterizes quality from Color, Luminance, Contrast, Resolution and Display SizeArtifacts and media influences are being considered for DQS 2.0
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How does DQS work?
How does DQS work?
DQS relies on well established facts about the visual processing and visual preferences
These facts are described in mathematical models
To understand how DQS works, we need to understand some basic facts about the visual system
Overview
Present and explain some of the key empirical bases for DQS, explain why they are important in display development and describe how they are modeled
Describe some of the data and modeling methods used in 3M’s development of DQS
Explain how to use and interpret DQSs
Human visual processingThe Basics
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Visual experience is created by the pattern of activity in neural networks that start in the eye and project across many regions of the brain
Human visual processing1. Transducing light into a neural response
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Fovea: central high resolution vision
Neural network in the eye
Human visual processing2. What detail can the visual system resolve? The Contrast Sensitivity Function
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Increasing spatial frequency
Dec
reas
ing
cont
rast
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More contrast is required for the visual system to detect fine details
Campbell & Robson, 1968
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2.9 x 10-4
cd/m2
290 cd/m2.
van Nes, M. A. Bouman (1967)
Sensitivity to different levels of detail depends on luminance
1. Greater image detail can be seen at higher luminance
2. Small scale artifacts are less visible that larger scale
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DQS employs a model of the CSF and its changes with luminance to predict visibility of patterns in displays
We can model this!
Human Visual ProcessingHow does the visual system create color from light?
Trichromatic Basis of Human Color Vision
Color is our experience of the relative response of the 3 cone types
Any light spectra creating the same relative response should appear the same
Summary of DQS Ingredients Forced Choice
Experiments
Data & Model on
human vision
Data & Model on
display system
Experimental MethodologyForced choice paradigms
Do you prefer the image on the left or the image on the right?
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How Image Pairs Were Selected: Method 1Pre-select images and perform all pairwise comparisons
A B
C D
A B C D
A .5 .7 .1 .95
B .3 .5 .25 .55
C .9 .75 .5 .95
D .05
.45
.05
.5
Table cells show (fictional) proportion that stimulus in row was selected over stimulus in column
Especially useful when we do not know what direction people’s preferences will go (e.g. Will they prefer less or more luminance under dim illumination?)
“Unidimensionalscaling” used to recover preference scale
How Image Pairs Were Selected: Method 2
Vary one of two images along one dimension (e.g. color gamut or luminance) to determine the size of the difference along that dimension that gives some proportion of times the fixed “standard” is preferred to the varied image (“Staircase procedures”)
Useful when can assume preference “transitivity” along the dimension being varied (e.g. if a medium color gamut is preferred to small gamut and large is preferred to medium, then large will be preferred to small)
standard
comparisonProportion standard
preferred to comparison.55
.72
.96
Experimental Set-Up
High color gamut displays (HP Dreamcolors)
Viewing conditions: Indoor lighting
Data collected from 6 countries
28 images calibrated to meet display parameters
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Proportion of people preferring larger over smaller color gamut. The 0 point on the x-axis represents the smallest gamut tested (~72% NTSC)
Results: All countries
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Results by country
Examples of DQS Model Fits to Color Preferences Data
“Staircase data” Preselected image pair data
‘standard’ gamuts
Examples of DQS Model Fits to Color Preferences Data
“Staircase data” Preselected image pair data
14 participants (USA)>3,000 observations
212 participants6 countries, 8 locations>58,000 observations
Key points
1. Data directly show the proportion of people prefer different color gamut2. The vast majority of people consistently prefer large over small3. ~14% of the population is indifferent
Summary of Development of the DQS
Survey of existing metrics indicated Barten’s 19872 square root integral (SQRI) was best suited for characterizing display image quality• SQRI uses the human contrast sensitivity function to predict the effects of resolution, image
size, luminance and luminance contrast• Early DQS studies on luminance (viewing angle) and image size verified SQRI correlation with preference
Limitations of SQRI: Color is absent and we know color impacts perceived display quality
3M study and other studies indicate that effects of black point and contrast on preferences not adequately predicted by SQRI
3M improves on SQRI metric based on color and black point research
2P. Barten, “Evaluation of subjective image quality with the square-root integral method,” JOSA A, vol. 7, no. 10, pp. 2024–2031, 1990.
DQS: Current Form
Barten’s SQRI integrated influence of: Luminance, Size, Resolution, Contrast
Influence of color gamut and black point
Overview of DQSThe User’s perspective: Calculation and Interpretation
Diagonal screen size & viewing distance
# pixels (horizontal/vertical)
Luminance (black & white)
Chromaticity of 3 primaries
Display type (LCD, OLED)
DQS Input Parameters
Landmark Examples of Absolute DQS
Diagonal Dist pixels H pixels V white black color DQSExtreme TV 85 108 4096 2160 600 0.02 135.0% 50High end TV 65 108 4096 2160 500 0.17 87.2% 42.3
High Average TV 55 108 1920 1080 450 0.18 87.2% 37.2Low Average TV 32 108 1920 1080 300 0.38 78.5% 26.0Great notebook 15.6 22 4096 2160 350 0.35 120.0% 45.5
Low end notebook 15.6 14 1366 768 250 0.5 50.0% 21.5Great smart phone 5 14 1920 1080 550 0.55 120.0% 36.1Ave. smart phone 4.3 14 800 460 400 0.5 60.0% 20.1
Old cell Phone 1.6 14 320 240 250 2.5 25.0% -11.9
Interpreting the DQS
DQS provides an absolute score but, from an engineering, manufacturing and marketing perspective, the meaning is in the differencebetween scores
ΔDQS
Proportion of Population
Preferring One Over Other
-2 ≤14%
-1 ≤25%
0 50%
1 ≥75%
2 ≥86%
DQS as a tool to guide display development
DQS Predictions: Resolution
Device type LCD
Diagonal 48”
viewing distance 275cm
black point .5 cd/m2
white point250
cd/m2
color gamut sRGB
DQS Predictions: Luminance
Device type LCD
Diagonal 48”
viewing distance 275cm
# pixels horizontal 1920
# pixels vertical 1080
black point .5 cd/m2
color gamut sRGB
DQS Predictions: Color
Device type LCD
Diagonal 48”
viewing distance 275cm
# pixels horizontal 1920
# pixels vertical 1080
black point .5 cd/m2
white point250
cd/m2
DQS Predictions to Guide Display Development: TV
DQS allows developers to determine the combinations of display parameters that maximize the proportion of the population that will prefer their display. Cost, supply and other constraints can then be used in deciding how to build the most appealing display for customers
e.g. diminishing returns to resolution: look to other factors to improve quality in cost-effective manner
DQS: Mobile
D2,
P7
P7G71
6
D2,
P6
Model Display Size DQSG716 5’’ 28.3
P6 4.7’’ 28.9D2 5” 32.7P7 5‘‘ 32.7
D2
G71
6P6
, P7
P6, G
716
Isoquality Curves: Understanding Trade-Offs
Device type LCD
Diagonal 48”
# pixels horizontal 1920
# pixels vertical 1080
black point .5 cd/m2
viewing distance 275cm
Each curve represents combinations of gamut and
luminance that have the same DQS
Isoquality Curves: Understanding Trade-Offs
Device type LCD
Diagonal 48”
# pixels horizontal 1920
# pixels vertical 1080
black point .5 cd/m2
viewing distance 275cm
Each curve represents combinations of gamut and
luminance that have the same DQS
Each curve represents combinations of gamut and
luminance that have the same DQS
Device type LCD
Diagonal 48”
# pixels horizontal 1920
# pixels vertical 1080
black point .5 cd/m2
viewing distance 275cm
Increasing distance between isoquality curves reflect saturation shown in previous slides
Isoquality Curves: Understanding Trade-Offs
DQS provides the ability to understand
tradeoffs in display quality
Device type LCD
Diagonal 48”
# pixels horizontal 1920
# pixels vertical 1080
black point .5 cd/m2
viewing distance 275cm
Relatively small gamut change produces large gains when luminance is high
Isoquality Curves: Understanding Trade-Offs
DQS provides the ability to understand
tradeoffs in display quality
Device type LCD
Diagonal 48”
# pixels horizontal 1920
# pixels vertical 1080
black point .5 cd/m2
viewing distance 275cm
Similarly, relatively small luminance change produces large gains at high gamut
Isoquality Curves: Understanding Trade-Offs
Further Points Regarding DQS Interpretation
DQS is designed to predict display quality over a large set of images. Apparent quality is highly image dependent.
Example 1: The pair of images on the top correspond to approximately the DQS difference as the pair on the bottom but most people perceive a larger difference in the bottom two images
Further Points Regarding DQS Interpretation
DQS is designed to predict display quality over a large set of images. Apparent quality is highly image dependent.
Example 2: High resolution only matters for images with fine detail. That is, increasing the resolution of a display will not improve the appearance of images with fuzzy content.
A Note About Image Processing
Image processing has become increasingly important for image compression, image enhancement and artistic control
Good image processing can influence the apparent quality of a display
However, DQS is designed only to quantify the influence of the physical parameters of the display
That is, it captures the limits of what a display can do but is silent in regard to what can be done within those limits
A Note About Image Processing: Example
Image processing can enhance image appearance within the limits set by the physical display properties. It cannot operate outside of these limits.
It can be difficult to determine whether the apparent quality of a display is a result of better image processing or superior display manufacture/design.
Further Points Regarding DQS Interpretation
1. DQS is designed to predict display quality over a large set of images. Apparent quality is highly image dependent
2. It is based on side-by-side viewing
3. Does not account for form factors (e.g. may give a 10” mobile phone a high display score but this is impractical)
4. It is a work in progress that will benefit greatly from customer feedback
Future Development
DQS: Future Developments
Future development directions will be guided by feedback from users but here are some initial proposals:
1. Develop better characterization of image dependence (i.e. a set of quality scores based on image type)
2. Incorporate effects of material properties (e.g. screen gloss) and common defects (color mura, sparkle, missing pixels)
Thank You!
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or experience that 3M believes are reliable. However, many factors beyond 3M’s control can affect the use and performance of a 3M product in a particular application, including the conditions under which the product is used and the time and environmental conditions in which the product is expected to perform. Since these factors are uniquely within the user’s knowledge and control, it is essential that the user evaluate the 3M product to determine whether it is fit for a particular purpose and suitable for the user’s method of application.
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Additional Support Slides
The Eye’s Optics
Campbell & Green, 1965; Rodeick 1998
Point Spread Function
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More contrast is required for the visual system to detect fine details
Why does sensitivity decrease with high levels of detail?
Campbell & Robson, 1968
Modulation Transfer Function
5.8 mm pupil
2 mm pupil
The Eye’s Optics
Campbell & Gubisch, 1958; Campbell & Green, 1965
Point Spread Function
The Eye’s Optics
5.8 mm pupil
2 mm pupil
Campbell & Gubisch, 1958; Campbell & Green, 1965
Contrast of fine detail is relatively reduced by eye’s optical system
Explains why sensitivity decreases with detail
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More contrast is required for the visual system to detect fine details
Decrease in sensitivity at high levels of detail largely due to optical factors (aberrations & diffraction)
Campbell & Robson, 1968
The Eye’s Optics
People cannot see images that have finer detail than 60 cycles per degree of visual angle. Why?
Campbell & Robson, 1968
The Eye’s OpticsHard cut-off at ~60 cycles per degree of visual angle due to sampling (“Nyquist”) limits of the human photoreceptor mosaic
The Eye’s OpticsNyquist limit of the fovealcone sampling mosaic is ~60 cycles per degree
DQS: Current Form
Barten’s SQRI integrated influence of: Luminance, Size, Resolution, Contrast
Influence of color gamut on display quality
s
Thank you
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