Week 3 Readings

 

Articles

Galloway, Edward A. (2004, May 3). Imaging Pittsburgh: Creating a shared gateway to digital image collections of the Pittsburgh region. First Monday, 9(5). Retrieved from http://firstmonday.org/ojs/index.php/fm/article/view/1141/1061

            When reading the article, I was struck by the benefits for using digitization which Edward A. Galloway voiced. Many of the advantages appear to be for users – the website that provides direct access to the collections had “greatly increased public access to significant collections of historic material documenting the growth and development of Pittsburgh and the surrounding western Pennsylvania region during the nineteenth and early twentieth centuries” (Galloway, 2004, Project Summary, para. 2) and users could gain a deeper understanding of overall events, localities, infrastructure, land use, and populations (Galloway, 2004, The Online collection, para. 2). As such, the focus is on the users – they learn more and get more information through the projects. While such benefits are explicit, that does not mean that the content partners of the project do not gain advantages as well. While Galloway notes only one benefit – income and financial funding (Galloway, 2004, Characteristics, para. 2) – they would gain more than that. By attracting more people to the site, they would get potential visitors for their main sites; the attention of other institutes to collaborate on projects with; and developing expertise in digitization, communication, and partnerships. Such results are good in the long term.

 

Webb, Paula L. (2007, June). YouTube and libraries: It could be a beautiful relationship. College & Research Libraries News, 68(6), 354-355. Retrieved from http://crln.acrl.org/content/68/6/354.full.pdf

           Overall, the article presents an interesting point of view. Usually when I access YouTube, I only see it from a consumer point of view – I try to find clips and videos to watch for fun rather than for business pursuits. Paula L. Webb, though, analyses it from a career point. She addresses the librarians as her core group and attempts to transform YouTube into a tool for libraries to use, describing how to sign-up (Webb, 2007, 354), the advantages (Webb, 2007, 354-355), and suggestions on how librarians can use the media at their hands (Webb, 2007, 355). Such a stance implies confidence, that those involved in the library sciences should not be afraid of or dismiss the internet, but embrace it, which I think works better than remain afraid of change.

            One claim, though, pushed me to investigate. When describing the advantages of using YouTube, Webb (2007) notes that some of the regulations include a maximum file size of 100 MB and at most 10 minutes worth of footage per video (p. 354). At first I (having never operated a YouTube account before) thought she meant that the site regulated this due to its own limitations; that it could not store any more megabytes to last more than 10 minutes. But this could not be true; in undergrad, I had taken a couple of film courses that required watching movies outside of class. In some cases, I could find whole movies on YouTube, such as His Girl Friday – a download of around 1 hour and 31 minutes total in one viewing – and Hedwig and the Angry Inch – over 1 hour and 31 minutes as well, but with a fee. I realized then that the regulations had more to do with copy-right infringement than technological limitations (the movies listed would pass – His Girl Friday was first produced in 1940 and the later has fees involved). It was enlightening, and makes me wonder about what sorts of tension must exist between digitization, technological advantages, and commercial reality.

 

Data compression. (2013, September 9). Retrieved September 10, 2013, from Wikipedia: http://en.wikipedia.org/wiki/Data_compression

 

            The article (at the time of my first viewing) was very informative, though heavy at times, in explaining data compression. I got confused at some points in its explanation, such as in the descriptions about the theories, including Machine Learning and Data Differencing (Data compression, 2013, Theory). After Monday’s course, though, I think I have a better understanding.

            Looking over the text again, I am drawn towards the examination of lossy data compression. The article not only describes it, but links it to devices. In particular, it claims that users can use lossy image compression in digital cameras “to increase storage capacity with minimal degradation of picture quality” (Data compression, 2013, para. 2). This reminded me of the first assignment. The images we will be working on for the assignment will deteriorate when we use digital cameras (or, presumably, scanners). That is understandable; we are taking pictures of objects, so they are not exact replicas of their subjects. By how much, though, will the images deteriorate? What is “minimal degradation”? Will we notice?

 

del-Negro, Rui. (2013). Data compression basics. DVD-HQ. Retrieved from http://dvd-hq.info/data_compression_1.php

 

            Rui del-Negro (2013) writes in a clear fashion using lots of examples, so I was able to follow along most of the time. The “Note…” sections were especially interesting. They provided more details that I would not have thought of, such as how the Huffman coding has encompassed other prefix-free entropy codings beyond its original form (del-Negro, 2013, Entropy coding, para. 9). Del-Negro’s note on the reference to RLE/“squeezing algorithm” in Monstrous Regiment, written by Terry Pratchett, (Run-length encoding, para. 31) caught my attention. I am a fan of his series, so when I reread the book, I will definitely keep the encoding in mind.

One part, however, confused me. When describing the prediction algorithm, what it actually is was related in a round-about way (at least for me). I understand that the algorithm is based on studying two values only while assuming linear variation (del-Negro, 2013, Prediction, para. 9) but the specifics are unclear. From what I can read, the goal is to acquire efficient compression of an image file (del-Negro, 2013, Prediction, para. 3). The procedure involves storing errors, or subtracting predictions from the real values (whatever that is) of pixels that come after two known pixels (del-Negro, 2013, Prediction, para. 4). The results would show that the values might fit in less bits and – if the values are in a small range – it would mean that there are repeated values, meaning there are repeated sequences, which would allow a person to apply other compression techniques on the errors list (del-Negro, Prediction, para. 7). Since the description is spread out over several paragraphs, I do not know if this is the right impression.