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)  University of California, Irvine C.Ph. Computer Science Engineering (2009) - University of Californina, San Diego

Projects Pigoen Blog Da Vinci Painting Genghis Khan Ultra High Resolution Displays

Size vs Resolution

Resolution INFORMATION UNIT

A Few Common Uses Print Media Imaging Technologies Display Technologies

Print Media DPI Dots Inch

Imaging Technologies Mega-Pixels Millions of Pixels Image 3264 x (pixels wide) x 2448 (pixels tall) 7,990,272 (pixels total) = 8 MegaPixels

Display Technologies Standard Vertical Scanlines Display

Display technologies Vertical Scanlines  Progressive scan / Interlace

Display Technologies Video Format Natvie Resolution Acutal Pixel Count MegaPixels 480i720x480172, p720x480345, p1280x720921, i1920x10801,382, p1920x10802,073,6002.1

HIPerSpace Highly Interactive Parallelized Display Space

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

One Pixel Per American

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 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)

State of the Art Technologies Passive Stereo Displays  Use polarization to create 3D effects Previously done with multiple projectors

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 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 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 Pros  Enables almost limitless scalability  Shaders, textures, etc are native  Minimal network Cons  Requires recompilation / redesign  Guarentee 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

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

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 from the analysis