10-12-2012, 01:36 PM
EC 2214: Coding & Data Compression
Coding & Data.ppt (Size: 926.5 KB / Downloads: 23)
Why Compress? (3)
Bottom line--without compression:
Many applications/services will still not be feasible
E.g., streaming video
Many others will be much more expensive
E.g. analog vs. digital cell phones
Why not keep data compressed at all times?
It is difficult to live out of a suitcase …
Similarly, non-compressed data formats are related to data acquisition/consumption, not efficient storage
Modeling & Coding
Developing compression algorithms:
Phase I: Modeling
Develop the means to extract redundancy information
Redundancy => predictability
Phase II: Coding
Binary representation of the difference between the model and the observed data
A.k.a. residual
Summary
We defined (informally) the notion of data compression
Lossless
Lossy
Presented the basic approach behind compression algorithms:
Modeling + coding
Presented some early examples of international standardized encoding schemes:
Braille, Morse
Representing Data
Analog (continuous) data
Represented by real numbers
Note: cannot be represented by computers
Digital (discrete) data
Given a finite set of symbols {a1, a2, …, an},
All data represented as symbol sequences (or strings) in the symbol set
E.g.: {a,b,c,d,r} => abc, car, bar, abracadabra, …
We use digital data to approximate analog data