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Chapter 5 - Encodings & Design Principles

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Chapter 5 - Encodings & Design Principles Chapter 5 - Encodings & Design Principles Raghu Machiraju Slides: Courtesy Profs. Pfister, Moeller, und Weiskopf Outline - Role of human perception - relative vs. absolute judgements. - Theory of marks and visual channels, - Principles of expressiveness and effectiveness - accuracy - discriminability - separability - propensity for visual popout - Rankings of channels according to data type Judging ? Read M. Livingstone, “Vision and Art” Human Visual System • Massive parallel processing • Left / Right • Shape / Color / Motion • Luminance: Depth and motion • Color: Form and function Weber’s Law • Just Noticeable Difference (JND) ∆I ∆S = k I • We can detect 0.5% change in brightness independent of the overall illumination • Only applies to small patches Weber’s Law Most continuous variations are perceived as discrete steps Based on slide from Agrawala Based on slide from Mazur Based on slide from Mazur Luminance Constancy • Light levels can vary by six orders of magnitude • Our visual system deals with that by responding to relative differences • Can cause errors in simple graphical environments • In the real world we make use of additional information Simultaneous Contrast Gray patch on dark looks brighter than gray patch on light background Contrast Crispening Differences are perceived large when samples are similar to the background color Color Processing Read Color Contrast Colors are also perceived relative to their overall context Color Size Illusion Cleveland & McGill, “A Color-Caused Optical Illusion on a Statistical Graph”, 1983 Which Area Bigger? Cleveland & McGill, “A Color-Caused Optical Illusion on a Statistical Graph”, 1983 Marks Channels Marks and Channels Channel Types Provide information on • What something is information • circle, square ? • categorical attributes • How much information • quantify/measure: size, etc. ? • ordered attributes - ordinal and quantitative Mark Types • Mark is an item • Connection marks • Containment marks (areas) Expressiveness - visual encoding should express all of, and only, the information in the dataset attributes. - E.g. - Our perceptual system intrinsically senses as ordered if data is indeed ordered - Conversely too Effectiveness - Importance of the attribute should match salience of channel - Most important attributes encoded with the most effective channels - Effectiveness has meaning in context of visual encoding Channel Rankings Univariate Data Representations http://www.smartmoney.com/marketmap/ Based on slide from Stasko Mapping to Positional Quantities • Mapping to positional quantities: 1.000 Position • 0.775 • Size 0.550 0.325 Orientation • 0.100 0.05 0.288 0.525 0.763 1 • Geometric mapping • Typically, very effective visual parameters 32 Bivariate Date Representations Based on slide from Stasko Mapping to Positional Quantities • Point diagrams • E.g. scatter plots: visual recognition of correlations 34 Mapping to Positional Quantities • Line and curve diagrams: • Effective perception of differences in • Position and • Length 10000 8750 7500 6250 5000 35 0 7.5 15 22.5 30 Mapping to Positional Quantities • Bar graph: • Discrete independent variable (domain): nominal/ ordinal/quantitative Quantitative dependent variable (data) • 10000 8750 7500 6250 5000 36 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Trivariate Data Representations Based on slide from Stasko Alternatives Based on slide from Stasko Channel Effectiveness • Accuracy • Discriminability • Separability • Popout Effectiveness - Accuracy Read Effectiveness of Mappings [Kandel et al.: Principles of neural science] 42 Steven’s Power Law S = kIp From Wilkinson 99, based on Stevens 61 Steven’s Power Law Sensation Exponent Brightness 0.33 - 0.5 Smell 0.55 (Coffee) - 0.6 (Heptane) Loudness 0.6 Underestimating Vibration 0.6 (250 Hz) - 0.95 (60 Hz) Taste 0.6 (Saccharine) - 1.3 (Salt) Temperature 1.0 (Cold) - 1.6 (Warm) Duration 1.1 Pressure 1.1 Heaviness 1.45 Overestimating Electric Shock 3.5 Stevens 61, “Psychophysics of Sensory Functions” Area Judgements • Follow Steven’s power law S = kAp • Stevens: p = 0.7 • Flannery: p = 0.87, k = 0.98 • Cleveland: p = 1.0 Cleveland et. al, “Judgments of Circle Sizes on Statistical Maps”, 1982 Accuracy of Channels • Directly map human response to visually encoded information • Explicit rankings of perceptual accuracy for each channel type • Position along common scale is most accurately perceived • Position along nonaligned scales, • Length and direction and angle judgements without scale • Area judgements are notably less accurate • Volume and curvature judgements worse yet • Greyscale shading and color saturation were the least accurate Discriminability Channel limited by number of bins for use Bins in a Channel Large line width makes appear polygon Best when # values to encode is small Separability + Separable channel pair best for two different data attributes + Position and color hue is a good choice + For single data attribute with three categories - + Integral channel pair of horizontal and vertical size is a reasonable choice Popout Popout Popout • All support popout individually • A few channel pairs support popout • Popout not possible with >3 channels • Popout depends on channel and how different target is from surrounding Channel Characteristics • Planar Position • Color • Size • Tilt/Angle • Shape Planar Position Planar Position • Attributes encoded with position will dominate users mental model • Vertical and horizontal position are combined into planar • Differences between up-down and side-to-side axes are relatively subtle • Perceive height differences along up-down axis more than horizontal differences • Why ? Physical effects of gravity •Information density considerations sometimes override this concern •Why ? Aspect ratio gives horizontal salience Visual Metaphors Semiology of Graphics [J. Bertin, 83] Color/Luminance Processing Human Visual System • Massive parallel processing • Left / Right • Shape / Color / Motion • Luminance: Depth and motion • Color: Form and function Newton Cartoon Spectra Red Cyan 400 500 600 700 nm 400 500 600 700 nm Green Magenta 400 500 600 700 nm 400 500 600 700 nm Blue Yellow 400 500 600 700 nm 400 500 600 700 nm Additive Color Mixing • Colors combine by adding the spectra • Examples: Monitors, projectors, lights, etc. Red + 400 500 600 700 nm Green = Yellow 400 500 600 700 nm 400 500 600 700 nm Subtractive Color Mixing • Colors combine by multiplying the spectra • Examples: Crayons, paint, most films, filters Cyan (“blue” in crayons) * 400 500 600 700 nm Yellow = Green 400 500 600 700 nm 400 500 600 700 nm 1 RefractiveP ower = Focal distance F ocalDistance(m) Wandell, Foundations of Vision Bear, Connors, Paradiso, “Neuroscience” Bear, Connors, Paradiso, “Neuroscience” M. Livingstone, “Vision and Art” Center-Surround Organization Color-Opponent Cells Bear, Connors, Paradiso, “Neuroscience” Color Opponency Ware, “Information Visualization” Color Blindness M. Livingstone, “Vision and Art” Recap • Massive parallel processing • Left / Right - depth • Center / surround - light / dark • P / M - detail / low-resolution vision • P / NonM-NonP - red-green / blue-yellow • Luminance: Depth and motion • Color: Form and function Further Reading Luminance conveys high-resolution edge information, while the red-green and blue-yellow channels are lower resolution. Color Systems • Our perception registers: – Hue – Saturation – Lightness or brightness • Artists often specify colours in terms of – Tint – Shade – Tone © Machiraju/Möller Tristimulus Response • Given spectral power distribution S(λ) • Given S1(λ) , S2(λ), if the X, Y, and Z responses are same then they are metamers wrt to the sensor • Used to show that three sensor types are same © Machiraju/Möller CIE Color Matching Experiment Basis for industrial color standards and “pointwise” color models © Machiraju/Möller CIE Experiment © Machiraju/Möller © Bill Freeman CIE Experiment Result • Three pure light sources: R = 700 nm, G = 546 nm, B = 436 nm. • r, g, b can be negative © Machiraju/Möller CIE Color Space • 3 hypothetical light sources which yield positive matching curves • Use linear combinations of real lights –R, G-2R,B+R • One of the lights is grey and has no hue • Two of the lights have zero luminance and provide hue • Gives X, Y, Z color values • Y corresponds to achromatic (no color) channel • Chromaticity values: • x=X/(X+Y+Z); y=Y/(X+Y+Z) • Typically use x,y,Y © Machiraju/Möller Chromaticity Diagram X x = X + Y + Z Y y = X + Y + Z € © Machiraju/Möller Chromaticity © Machiraju/Möller Chromaticity • When 2 colors are added together, the new color lies along the straight line between the original colors – E.g. A is mixture of B (spectrally pure) and C (white light) – B - dominant wavelength – AC/BC (as a percentage) is excitation purity of A – The closer A is to C, the whiter and less pure it is. © Machiraju/Möller Chromaticity • D and E are complementary colors • can be mixed to produce white light • color F is a mix of G and C • F is non-spectral its dominant wavelength is the complement of B © Machiraju/Möller Color Gamut • area of colors that a physical device can represent • hence - some colors can't be represented on an RGB screen © Machiraju/Möller Color Gamut © Machiraju/Möller RGB <-> XYZ • Just a change of basis • Need detailed monitor information to do this right – Used in high quality settings (movie industry, lighting design, publishing) • Normalized (lazy) way: – (1,1,1) in RGB <-> (1,1,1) in XYZ – matrices exist ⎡ X⎤ ⎡0 .5149 .3244 .1607⎤⎡ R⎤ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢Y ⎥ = ⎢0 .2654 .6704
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