Abstract media spaces
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Abstract Media Spaces. Rob Diaz-Marino CPSC 781 University of Calgary 2005. Outline. What is Abstraction? Simple Media Spaces Abstract Media Spaces (AMS) Benefits and Drawbacks Methods for Abstracting Media Designing an AMS Summary. What is Abstraction?. “Guernica” by Pablo Picasso

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Abstract media spaces

Abstract Media Spaces

Rob Diaz-Marino

CPSC 781

University of Calgary

2005


Outline

Outline

  • What is Abstraction?

  • Simple Media Spaces

  • Abstract Media Spaces (AMS)

    • Benefits and Drawbacks

    • Methods for Abstracting Media

    • Designing an AMS

  • Summary


What is abstraction

What is Abstraction?

“Guernica” by Pablo Picasso

Reaction to the bombing of Guernica in World War II.


What is abstraction 2

What is Abstraction? (2)

  • Throwing away information [1]

Abstract

Realistic

Photo

Cartoon

Art Piece

  • Female

  • Blond Hair

  • Yelling

  • Emotion?

  • Female

  • Brown Hair

  • Yelling / Angry

  • Female

  • Yelling / Distressed

  • In a barn/cellar

Fact

  • Image cropped

  • Unclear context

  • Part not drawn

  • No context

  • Lips, eyebrows,

  • fingers, mouth,

  • hair…

  • Eyes on same side

  • of head

  • -Eyes in wrong place

  • Distorted body shape

  • Hair on back of head

  • Blue-gray color

Abstraction


Simple media spaces

Transmission

Video-In

Video-Out

Transmission

Picture-In

Picture-Out

Transmission

Audio-In

Audio-Out

Simple Media Spaces

…and so on.


Simple media spaces 2

Simple Media Spaces (2)

  • Literal Transmission

    • Input = Output

    • Low degree of abstraction

    • Some loss from original (reality)

      • Capture limitations

      • Compression – bandwidth limitations

    • Still perceptually equivalent


Why use abstraction in a ms

Why use Abstraction in a MS?

Bob is at his computer

Bob is not wearing clothes

Bob is has a fruit-basket hat on his head

Bob is yelling at his girlfriend on the phone

Bob looks angry

  • To control information

    • Preserve Privacy

      • Shield sensitive details

    • Reduce Distraction

      • Eliminate unnecessary details

      • Re-map awareness cues

  • Reduce bandwidth needs

Abstraction

[1]

Someone is at the computer

The person is mostly flesh-colored

The person has something large on their head

The person is speaking loudly

No details can be seen on their face


Drawbacks of abstraction

Drawbacks of Abstraction

Bob is at his computer

Bob is not wearing clothes

Bob is has a fruit-basket hat on his head

Bob is yelling at his girlfriend on the phone

Bob looks angry

  • Loss of information

    • Useful: Identity, Actions, Availability, etc.

    • Incidental: Details, Emotional state, etc.

  • Loss of understanding

    • Unclear meaning

    • Unclear context

Abstraction

Someone is at the computer

The person is mostly flesh-colored

The person has something large on their head

The person is speaking loudly

No details can be seen on their face


Methods of abstraction

Methods of Abstraction

  • Simple Degradation

  • Feature Extraction (Silhouetting)


Simple degradation

Simple Degradation

  • Video

    • Still resembles original

    • Ex. Mike Boyle’s Video Filters [4]

Blur Filter

Video-Out

Pixelation

Video-In

Video-Out


Simple degradation 2

Simple Degradation (2)

  • Audio

    • Still resembles original

Echo

Audio Out

Audio-In

Muffling

Audio Out


Feature extraction

Feature Extraction

  • Video

    • Vision Techniques

      • Motion detection

      • Presence detection

      • Eye tracking

      • Face tracking


Feature extraction 2

Feature Extraction (2)

  • Audio

    • Ex. Smith et al’s Low Disturbance Audio [3]

      • Speech  Non-Speech

      • Can still recognize voice

      • Cannot understand words

      • Similar to Blur Filter but for Audio!

Extract

Synthesize

Audio-In

Audio Out


Feature extraction 3

Feature Extraction (3)

  • Text

    • Ex. Syllable replacement

Jim: Hi! How are you doing?

Bob: Doing okay…

Jim: Are you busy?

Bob: I’m on the friggin’ phone!!

Jim: Oh, sorry!

Extract

Jim: Bla! Bla bla bla blabla?

Bob: Blabla blabla…

Jim: Bla bla blabla?

Bob: Bla bla bla blabla bla!!

Jim: Bla, blabla!


Media translation

Media Translation

  • Convert one media form to another

  • No direct translation

    • Feature Extraction

    • Synthesizer – Visualization, Sonification, etc.

Extract

Synthesize

Audio-Out

Video-In


Media translation 2

Media Translation (2)

  • Ex. AROMA [1]

    • Peripheral awareness


Media translation 3

Media Translation (3)

  • Ex. Cambience (my thesis project)

    • Inputs

      • Web Cam (video)

    • Feature extraction

      • Motion detection, partitioning, thresholding, etc.

    • Outputs

      • Audio – volume, pan, etc.


Translation pitfalls

Translation Pitfalls

  • Extreme abstraction

    • No longer understandable

    • Usable only as art piece

  • Learning Curve

    • Arbitrary mappings

    • Users may need to see literal data [1]


Designing an ams

Designing an AMS

  • Processing

    • Must be done in REAL TIME

    • Can lower sampling rate to compensate

  • Peripheral vs. Foreground

  • Draw Inspiration

    • Ambient Displays

    • Visualizations


Designing an ams 2

Designing an AMS (2)

  • 3 Architectures

    • Client-side processing

    • Server-side processing

    • Distributed processing


Ams architectures 1

Server

Client

Output

Extract &

Synth

Input

Output

Extract &

Synth

Extract &

Synth

Output

AMS Architectures (1)

  • Client-Side Processing

    • Transmit raw data – privacy risk!

    • High Bandwidth usage

    • Low CPU for Server, high for Clients


Ams architectures 2

Server

Client

Output

Input

Output

Extract &

Synth

Output

AMS Architectures (2)

  • Server-Side Processing

    • Transmit synthesized media

    • High Bandwidth usage

    • High CPU for Server, low for Clients


Ams architectures 3

Server

Client

Output

Synthesize

Input

Output

Extract

Synthesize

Synthesize

Output

AMS Architectures (3)

  • Distributed Processing

    • Transmit extracted features

    • Lower Bandwidth usage

    • Lower CPU for Clients and Server


Designing an ams 3

Designing an AMS (3)

  • Ex. Cambience

    • Video Input

      • Feature extraction on Server

    • Transmission

      • Scalar values

    • Audio Output

      • Sound synthesis on Client


Summary

Summary

  • Abstract Media Spaces

    • Throw away information

      • Simple Degradation

      • Feature Extraction

    • Can provide a privacy shield

    • Can provide better peripheral awareness

    • Allow media re-mapping

    • Can lower bandwidth usage


Questions

Questions?


References

References

  • Pedersen, E. R., Sokoler, T. (1997) AROMA: Abstract Representation of presence supporting Mutual Awareness. Proceedings of CHI’97, 51-58.

  • Wikipedia: The Free Encyclopedia. (n.d.) Retrieved October 2005 from http://en.wikipedia.org/wiki/Abstract_art, http://en.wikipedia.org/wiki/Pablo_Picasso,http://en.wikipedia.org/wiki/Cubism

  • Smith, I., Hudson, S. (1995) Low Disturbance Audio For Awareness and Privacy in Media Space Applications. In proceedings of ACM Multimedia ’95, ACM Press, p. 91-97

  • Boyle, M. (2005) Privacy in Media Spaces. PhD Thesis, Department of Computer Science, University of Calgary, Calgary, Alberta CANADA T2N 1N4. April.


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