Week Five Notes

Data Compression:


-the process of encoding information using fewer bits than unencoded representation would use through use of specific encoding schemes, example=zip file
-only works when all parties involved understand the encoding scheme
-helps reduce the consumption of expensive resources
-Lossless Compression- usually exploit statistical redundancy in such a way as to represent the sender's data more concisely without error
-lossy data compression- guided by research on how people perceive the data in question
-used to optimize disk space on office computers, or better use the connection bandwidth in a computer network
- For visual and audio data, some loss of quality can be tolerated without losing the essential nature of the data
-Many lossless data compression systems can be viewed in terms of a four-stage model. Lossy data compression systems typically include even more stages, including, for example, prediction, frequency transformation, and quantization
-The very best compressors use probabilistic models whose predictions are coupled to an algorithm called Arithmetic coding, invented by Jorma Rissanen

Data Compression Basics:


-I like this article because the writing is straight forward and seems to flow pretty well. The author also provides many examples, which are helpful and not complicated to understand
-Advantages of using data compression: lets you store more stuff in the same space, and it lets you transfer that stuff in less time, less bandwidth
-“The fundamental idea behind digital data compression is to take a given representation of information (a chunk of binary data) and replace it with a different representation (another chunk of binary data) that takes up less space (space here being measured in binary digits, better known as bits), and from which the original information can later be recovered.”
-Provides numerous examples on run-length encoding (“Run-length encoding replaces ‘runs’ (that is, sequences of identical characters) with a single character, followed by the ‘length of the run’ (the number of characters in that sequence), or vice-versa (first the length and then the character, the order isn't important as long as it's always the same”).
-RLE used in the compression of images
-Pixel color information is generally stored as a sequence of three values, representing the amounts of red, green and blue that define it.
-RLE by itself will never achieve high levels of compression on photographic images, or any other kind of image where there are no adjacent pixels with the same color
-Examples of algorithms and their uses; “Instead of writing "Red, Green and Blue", we often write just "RGB". The same goes for things like HTML ("Hyper-Text Markup Language"), PC ("Personal Computer"), and so on. Acronyms are a form of data compression. However, acronyms can't be "uncompressed" by themselves; the reader needs to know (or find out) their meaning by matching them to the "uncompressed" version.”
-Use of flying spaghetti monster reminds me of the Wii episode of South Park (side note)
-sliding window algorithm- technique of looking back and copying sequences using a length-distance pair
-Lempel Zip (LZ) algorithms are the basis of most modern lossless data compressors and compress data by locating and eliminating redundancy
-Entropy coding- technique that assigns codes to blocks of data (sometimes called "symbols") in such a way that the length of the code is inversely proportional to the statistical probability of that symbol
-Identifying such "typical patterns" or tendencies in specific kinds of data is the key to efficient compression, example: in photograph it is rare to have a red pixel followed by a blue pixel, followed by a white one
-Prediction and error coding is a very successful technique for compressing "natural media" (images, sounds and other data sets with an analog origin).

Imaging Pittsburgh:


-Question: is this part of the current website, Historic Pittsburgh?
-Based on a 2 year grant to the University of Pittsburgh’s Digital Research Library from the Institute of Museum and Library Services to provide online access to multiple photographic collections held by the University’s Archives Service Center, Carnegie Museum of Art, and the Historical Society of Western Pennsylvania.
-“When the project ends (or ended), the project team will have mounted over 7,000 visual images depicting the people, places and events of the greater Pittsburgh region during the mid–nineteenth and mid–twentieth centuries.”
-“main focus of our project is to create a single Web gateway for the public to access thousands of visual images from photographic collections held by the Archives Service Center of the University of Pittsburgh, Carnegie Museum of Art, and the Historical Society of Western Pennsylvania”
-Benefits include the increased access to these collections and the digital preservation of the images
-Summary includes examples of photos
-through the website, patrons will be able to; Conduct a keyword search across all the image collections;
· “Browse images within any given collection;
· Read about the collections and their contents, including provenance, date span, and coverage;
· Explore the image collections by time, place and theme; and,
· Order image reproductions.”
-The are challenges within the project as to communication, selection, workflow, metadata (like use of dates), and website development
-Website launch prediction was February 2004

YouTube and Libraries: it could be a beautiful relationship:


-ALA article written by Paula L. Webb
-idea that YouTube, deemed one of the most popular and frequented sites on the web, could provide libraries with opportunity to spread its messages and reach out to distant learning students
-article goes on to explain how to set up an account and the advantages of YouTube
-Possibilities of this resource include: digital storage of instructional videos, video tours, tutorials,
-Wide range of advantages if librarians choose to incorporate the website into their services.