megapixel madness: technologies for ultra-high resolution display systems
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MegaPixel Madness: technologies for ultra-high resolution display systems. Kevin Ponto October 2009. About Myself. Grew up in Iowa City City High 2000 graduate B.S. Computer Engineering (2004) University of Wisconsin - Madison M.S. Arts Computation Engineering (2006) - PowerPoint PPT PresentationTRANSCRIPT
MegaPixel Madness: technologies for ultra-high resolution display systemsKevin Ponto
October 2009
About Myself
Grew up in Iowa City City High 2000 graduate
B.S. Computer Engineering (2004) University of Wisconsin - Madison
M.S. Arts Computation Engineering (2006) University of California, Irvine
C.Ph. Computer Science Engineering (2009)- University of Californina, San Diego
Projects
Pigeon Blog Discovering a Lost da Vinci Painting Locating the Tomb of Genghis Khan Multi-touch and Mixed Reality Interfaces
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Ultra-High Resolution Displays
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Size vs Resolution
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Resolution
INFORMATION
UNIT
Resolution
Can be temporal, spatial, etc Can also be thought of as measurement of detail Larger sizes do not necessarily increase resolution
Especially true for display technology
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http://en.wikipedia.org/wiki/Image_resolution
A Few Common Uses
Print Media Imaging Technologies Display Technologies
Print Media
DPI
Physical measure of resolution
Dots
Inch
http://en.wikipedia.org/wiki/Dots_per_inch
Imaging Technologies
Mega-Pixels
Millions of Pixels
Image
3264 x 2448
3264 (pixels wide) x 2448 (pixels tall)
7,990,272 (pixels total)
= 8 MegaPixels
Display Technologies
Standard
Vertical Scanlines
Display
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http://en.wikipedia.org/wiki/Television
Display technologies
Vertical Scanlines Progressive scan / Interlace
http://en.wikipedia.org/wiki/HD_TV
Display Technologies
Video Format
Natvie Resolution
Acutal
Pixel Count
MegaPixels
480i 720x480 172,800 .2
480p 720x480 345,600 .3
720p 1280x720 921,600 .9
1080i 1920x1080 1,382,400 1.4
1080p 1920x1080 2,073,600 2.1
HIPerSpace
Highly Interactive Parallelized Display Space
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http://vis.ucsd.edu/mediawiki/index.php/Research_Projects:_HIPerSpace
HIPerSpace Stats
70 Dell 30 Inch Monitors 2,560 x 1,600 = 4,096,000 ( 4 MegaPixels)
Driven by 18 nodes (Dell XPS) Each node drives 2-4 Monitors (8-16 MegaPixels)
Total Resolution 35,840 x 8,000 pixels
Total Pixel Count: 286,720,000 Approximately 300 MegaPixels 150 times HD
One Pixel Per American
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http://en.wikipedia.org/wiki/United_States
Hardware
Walls can be made in several ways Projector based HDTVs Computer Monitor LCD Screens
Each of these have different advantages and disadvantages Cost to build and maintain Size Seams Resolution
Projection Walls
Currently the only method to create a bezelless high resolution display wall Require seam matching
May be easier to create passive and active stereo display spaces
High maintenance cost Bulbs, power, etc
LCD Walls
generally higher pixel density (DPI) 20/20 vision is the name of the game
smaller physical footprint no throw distance issues no issues with front vs rear-projection
smaller energy footprint smaller heat signature no noise emission better contrast easy to scale
State of the Art Technologies
New LCD screens have very small bezels 5 mm bezel (1 cm when stacked side-by-side)
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http://ucsdnews.ucsd.edu/newsrel/general/09-09KAUST.asp
State of the Art Technologies
Passive Stereo Displays Use polarization to create 3D effects
Previously done with multiple projectors
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http://ucsdnews.ucsd.edu/newsrel/general/09-09KAUST.asp
Challenge
How do you efficiently and effectively drive this many pixels?
Three Different Methods Geometry Broadcast Pixel Streaming Distributed Application
Geometry Broadcast
Intercept GL calls and forward them to the display environment
Geometry Broadcast
Head Node Render Nodes
Geometry Broadcast
Pros Little if no recompilation necessary May “work out of the box” Can use on programs not designed for tiled display
environments
Cons Slow! Shaders, textures, etc are problematic Only really useful for looking at 3D geometry only
Applications WireGL Chromium
Pixel Streaming
One node generates fills an image buffer with content
The buffer is split into regions for the viewpoint of each of the render nodes
These data segments are streamed to each of the render nodes.
Pixel Streaming
Head Node
Render Nodes
Buffer
Pixel Streaming
Pros Only one node needs to render content
Only one node needs access to data, applications etc Render nodes do not need to be powerful
Multiple applications/streams can be used once
Cons Only as high resolution as the buffer Massive network requirements
Applications SAGE
Distributed Application
Start the same application on all nodes at the same time
Use a different viewpoint for render nodes Forward all events from head node to render
nodes User I/O Display Swaps
Distributed Application
Head Node
Render Nodes
Distributed Application
Pros Enables almost limitless scalability Shaders, textures, etc are native Minimal network
Cons Requires recompilation / redesign Guarantee events are received and processed at the
same time on every node
Applications CGLX
CGLX
Distributed master-slave environment GLUT-like programming environment Viewpoints are configured on render nodes I/O reliably forwarded using UDP Open API
Free to universities
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What is this useful for?
Users can now see multimedia at unprecedented detail
Distributed approach allows for interactive manipulation of large amounts of data
Works well in the field of visual analytics
Human Centric Data Analysis
Visual Analytics “Science of analytical reasoning facilitated by interactive visual
interfaces.” (Thomas:2005) Synergy between human and machine analysis Synthesize information to detect important features in massive
datasets “Detect the expected and discover the unexpected”
(Thomas:2005)
Presenting data in a way such that the human mind is able to efficiently process
Why use the human mind?
Humans have a lifetime of experience in their profession The human mind is the best general-purpose pattern
recognizer compared with AI algorithms. (Moravec:1998) It only takes the human brain a little over a tenth of a
second in order to identify and classify an object in a complicated environment (Riesenhuber:2000)
The human mind can find patterns and differences even when the differences seen in objects are not easily quantifiable the symbol grounding problem (Harnad:1999).
Visual Analytics: Challenges
Data must be organized and presented in a meaningful way to be effective
Visual Analytics techniques need to be catered to the data being analyzed as well the users of the system Large image collection needs different visual analytic
paradigms compared to the visual analytics for detecting intruders on a network
No “one size fits all” solution
Why use Large Scale Display Walls? Historically researchers work on a single display
Suboptimal
Large Tiled Display Walls Allow human body's resources to interact and physically navigate with
large displays. (Ball:2007) Allow multiple users to interact with a work space all at once The human retina can process approximately ten one-million- point
images per second (Moravec:1998) High resolution displays are more effective than lower resolution with
pan and zoom(Ball:2005)
Small Multiples
Use display real-estate to display many variations of similar data High resolution allows data to be displayed with out sub-
sampling Many users can view the data simultaneously Users can analyze the data physically
Small Multiples on HIPerSpace
Environment is fully interactive Can be repositioned and rescaled interactively
Case Study
Researchers at UCI used tiled display wall to show many variations of brain activity of schizophrenia patients Data was grouped
and sorted Patterns were
found Two patents
resulted fromthe analysis
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Diffusion Tensor Imaging
High resolution displays allow us to analyze these type of models in greater detail
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Microscopy Imaging
Offer very high resolution images
Cancer Images
Real-time Color Filtering
Multi-Layered Data
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Video
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Conclusion
Ultra-high resolution displays provide new opportunities for human centric computation
Multiple users can analyze data simultaneously These display environments allow researches to
discover the unexpected Abundant opportunities for new research and
collaborations
Questions