Presentation - Kristin Martin

This is my presentation on Rafael Lozano-Hemmer.

Presentation - Jon Good

The here is my presentation

Final Project Proposal - Jon Good

Here is my proposal

Final Project Proposal - Sarah Proctor

Here is my proposal.

Final Project Proposal - Taoxi Li

My proposal

Final Project Proposal - Michael Jennings

Here is my proposal

Final Project Proposal - Truc Le

Here is my final project proposal.

Final Project Proposal - Panpan Deng

This is Panpan's Final Project Proposal

Final Project Proposal- Kristin Martin

This is my Final Project Proposal.

Final Project Proposal - Katherine Martin

Here is my final project proposal. 

Survey of Generative and Interactive Art

Golan Levin - Various Interactive Applets

Casey Reas - Twitch

Siebren Versteeg - If My Heart is Narrow, What is the Good of the World Being So Wide?

Mark Napier - PAM

Jesus Duran - Can I Hear That in a Sentence Please?

Tiffany Funk - National UFO Reporting 
Cameron Brand - Bustracker
Ben Rubin, Mark Hansen, Jer Thorp - And That's the Way it is

Exercise 5 - Michael Jennings

Here is my nexus app

I'm not sure what's wrong with the camera but for whatever reason it won't show up, and the GPS coordinates seem to be within in the range I set up, but for some reason the text does not appear either.

Here is my updated app. The geolocation finally works with the text, but I still could not get the camera and the GPS to work on the same sketch.

Poke - Sarah Proctor

Poke
Revised

Exercise 5 - Katherine Martin

Here is my exercise 5.

Scream - Jon Good

Here is my screamer

Exercise 5 - Taoxi Li

Here's my Exercise 5.

Revision.

Exercise 5 -Truc Le

This is my revision of the tablet game - Candy Catch.

Exercise 5 - Panpan Deng

My exercise 5
Revised Exercise 5

Exercise 5 - Kristin Martin

This is my Exercise 5 Project.

This is my Revision.

Presentation - Truc Le

Here is the PDF file for my presentation on Scott Sona Snibbe.

Reading 5 - Sarah Proctor

In Cory Arcangel's article, On Compression, he explains the finer points of what really happens when a file, such as an image, sound file, or video file gets compressed.

The original file, which is clean, clear and sharp, is often times to big to move around quickly and cheaply, meaning that you either wait 12 years on a full fledged picture to upload, or you send it to the recipient via snail mail. Instead, computer scientists have created a way to compress these images and make them both smaller in data, while still being pretty much recognizable to the viewer.

There are two major types of compression, Lossless and Lossy. Lossless makes the data file of an image seem smaller, while not actually removing any information.

When a file is compressed in a Lossy format, you lose some information that you can never get back, altering the picture forever. Once a picture is compressed Lossy, you can never get the uncompressed file back, you can only further compress it.

While the cat on the right is still recognizable, it has become visibly blurred. The right hand cat will upload faster and smoother then the left hand cat, but it doesn't look as good.

It is really all a matter of preference as to which type of compression you use, but either way, you can still get fast and free photos like we have never been able to before.

Reading 5 - Michael Jennings

In Cory Arcangel's article, "On Compression," he explains how files, specifically JPEGs, are compressed. This compression is a lossy compression which causes images to appear blurry when transferred from one place to another, usually the Internet.

Because of lossy compression, finding a good image to use from the internet can be tricky, especially for a project that requires high attention to detail. Although most changes to images are minor, losing information about an image, a song, or a video can prove annoying to people trying to work with these files at a microscopic level.

I have taken graphic design classes in the past, and when trying to find an image for a project involving a  car, I had to search for a highly specific image. I wanted to find a photo of a 2002 Mazda Protege. I could not find one of high enough quality for a long time because it was a very specific car that is not necessarily a popular or luxurious car. Everyone else in the class chose crazy expensive cars such as  Audi R8's, Ferrari's, Lamborghini's, etc. High quality images of these cars are easy to find, but mine was not.

I'm not sure if lossy image compression is to blame for this or just simply the size of files. Images of nice cars are usually higher resolution images to be used for computer backgrounds and other things, but the 2002 Mazda Protege only has low quality, low resolution images from used car dealerships.

This reminded me of the point Arcangel made about slow transportation of full quality images. Because luxury cars are more expensive and more relevant than the Mazda Protege, people are more willing to transport high quality images of an Audi R8 or a Bentley.

Reading 5 - Panpan Deng

In the article, On Compression, Cory Arcangel explain how does the use of the Discrete Cosine Transform(DCT) makes the difference between lossy and lossless images.      

Before I read this article, I know that the obviously, the lossless images are more clear and sharp than the lossy ones. Similarly, the lossless songs are more mellow than the lossy ones. Another difference is that the lossless images are much larger than the lost data one. When I open my ORF (raw image format using by Olympus ) image, it takes nearly 20 seconds, by contrast, the JPEG image only takes me 5 seconds to open. Because of the time-consuming, I always avoid saving the images in raw format and hold an opinion that the raw images are just like those useless expensive luxuries such as golden toilets. However, when I start playing with cameras, one of the seniors tell me one golden rule about the photograph which is : always saving the images in raw formats. Right now it makes sense to me, lossy images are satisfying in small screens but dim in large screens. Even though I right now I have ideas about the advantage of raw images, I still do not think that raw images overweight the lossy images. The transportation of JPEG images are economic, as Arcangel states, “the less we need to send the cheaper and faster it is”(Arcangel, pp.221). Some perfectionist may not agree with me on this but I really think JPEG image’s quality is satisfying to the non-professionals. Arcangel also says that “our eyes and ears are pretty crap, and we don’t usually notice missing bits here and there”.

Then why do we ever need to care about the things we will never notice? Believe it or not, after listen ten times of lossless version of Rhymes of the Rain, I still can not tell the difference between the lossless version and lossy version. Maybe people just want to pretend to be professionals through the format of what they are listening and watching.

Reading 5 - Jon Good

In Cory Arcangel’s article “On Compression” he discusses the different techniques of image compression.  He presents very understanding examples and metaphors on how compression works.  He also provides the formulas that it takes to create the compression of data.  There are two types of data compression; Lossy and Lossless.

Lossless is the compression of data without losing any information.  Cory's example of how to understand Lossless compression is very interesting.  He states that if a bit of data such as this: 'a a a a a a a a a b a' would be transmitted without compression it would be a larger file size.  Transmitting large amounts of data is slow and expensive.  Now if the file was compressed then it would be able to retain the same information but using less space.  The way the computer can do this is it describes the information in a way that takes less space such as '9a's, one b, and one a'.  It translates to the original data.



Lossy compression actually loses some of the data after compression.  Since there is some loss of data it can not be used for text, or any application that all data is needed.  It is used for images, music, and video.  The way the computer gets away with this is after the file is compressed, it takes away some of the data that our senses can't notice.  Using the same example of before it would say there are just '11a's'. 



I find it very interesting that files can be compressed in this way.  It seems as if the file could have just had all the information needed in the first place.  I now can understand how the JPEG image doesn't have a very high quality of image because of the process the computer goes through to compress the image in to a smaller size.   

Reading 5 - Katherine Martin

    

  I really enjoyed Cory Arcangel's article about compression. I have never really thought about what makes up a JPEG file smaller so it was a new idea to me. I've always understood compression like zip files, but  I never really knew JPEGs were just Lossy compressed data. It was interesting to here what the two types of compression are. The first type Arcangel talked about was Lossless compression. In order to explain what Lossless compression was Arcangel used a great example. He said Lossless works like wanting to transfer "a, a, a, a, a, a, a, a, a, b, a," to someone over the phone that instead of saying each individual letter, instead you say "nine a's, one b, one a".  Lossless compression is nice because you don't loss any of the information unlike the second type of compression; Lossy.        

         Lossy compression actually loses data when it compresses, and because of this it cannot be used for text. I think it’s interesting that our eyes don’t notice the lost data. I feel like because our eyes are so advanced that they would be able to pick up lost data when looking at an image. Arcangel brought up the fact that because we don’t notice the loss of data “crappy images” are being used in ads, digital cameras, digital video, etc. Arcangel also said that all JPEG’s have a “crappy compressed blocky” look, but in my experience I haven’t ever really thought JPEG’s were that bad. For the most part JPEGs are successful transferring data even though some of it is lost, because if they weren’t they wouldn’t be so widely used. I think images aren’t blocky or pixelated simply because they’re JPEGs, but instead they look that way because they’re blow up larger than they should be or overly manipulated somehow.

Reading 5 - Truc Le

In the article On Compression, Cory Arcangel explained the way the Discrete Cosine Transform (DCT) technique works in JPEG pictures. According to Arcangel, DCT is a mathematical method which is used to compress images. By taking out unimportant coefficients of an image’s input values, the DCT technique helps lessen both the image’s file size and its quality. However, the human eyes usually do not recognize the quality reduction so the DCT technique is widely applied (2007-2008).

The purpose of the DCT technique is clearly to take up less computer space in saving or sending images. However, I find it more interesting in the way the technique works. In my opinion, the DCT method can accomplish its goal because it knows how to deceive the human eyes in the least obvious way (by disregarding an image’s minimal details through complicated math functions.) Basically, the DCT technique simplifies the image’s input values and displays only its key components.

The DCT’s concept of doing complicated calculations backstage in order to show the audience a simplified version of the image really relates to me. I feel connected to it because simplifying tricky details and making them appear simple to people is now my goal in programming. To be more specific, this is my story: when I first started to learn about coding and working with Processing, I used to feel like I have to incorporate as many computer functions in my project as possible. At that time, I evaluated my Processing project based on its details and complexity. However, as I learn more about coding and see complex art works by GLI.TC/H, I change my goal in programming. For example, I felt really uncomfortable and anxious when I look at chaotic works of GLI.TC/H. Their pieces made me rethink of myself as an artist: I want to make my art appears user-friendly to people, not confusing them. The old “must make things appear complicated” way of thinking in me is gradually replaced by “the simpler things looks, the better.” Now, although I may write long and confusing computer codes, I try to make my program look neat and easy to understand when people hit the “Play” button. That is how I relate to the DCT technique: doing complicated works in the back so as to make the outcome looks simpler.

Reading 5 - Taoxi Li

       The article “On Compression” talked about the two ways that compression of data is achieved. The first kind is called Lossless compression, which keeps all information from input data and can be expanded later to reproduce the original input information. This method can keep all information intact and unchanged but cannot save space, and it is often used to compress text and data files. The second kind is Lossy, and it selectively leaves out some information when compressing, but it keeps enough amount of data that is needed to recreate images/video/audio files that look/sound good enough for viewers. Below are three images that use lossy compression at different level. Lower compression means higher quality and larger size. It is quite surprising to me that over 90% of the original information are lost yet the images are still recognizable—our eyes are so easily deceivable!

(Low compression : 84% less information than uncompressed PNG, 9.37 KB)

(Medium compression (92% less information than uncompressed PNG, 4.82 KB)

(High compression (98% less information than uncompressed PNG, 1.14 KB)

     This article also makes me think that compression is compromise between efficiency and quality. This decision happens very often in across internet today: whether it is emailing your friends a 3-minute video of your vacation or uploading an 2-hour film documentary for your portfolio, information is constantly being compressed and decompressed. In out class for example, I believe we mostly used the Lossless method to “zip” and uploaded our files to Google Drive and then downloaded and unzipped it to demonstrate in class, because we want the input information—the codes— to maintain unchanged. However, when making the video documentation and sharing them on YouTube/Vimeo, the video needs to be compressed to save space and also time for people to view it. I’m not sure if this is the reason why our video documentations sometimes look blurry and even fragmented. Video/image sharing websites should inform the authors how much information is maintained and how much is lost before they put their work to the public, since the quality of the video significantly affects the feel and look of the actual work. When uploading videos to websites, we should also consider how often they will be watched online and downloaded, because that affects whether we choose either the quality or efficiency of the file.

      According to the video above, compression methods also differ depending on what media is used to present information—images for print media are often saved with lossless compression because it’s much more important to keep the integrity of image than when it’s put on the internet. I also learned that the lossy compression is an irreversible process, so it’s important to keep the original work.

Reading 5 - Kristin Martin

In Cory Arcangel’s article “On Compression” he explores the concepts of the Discrete Cosine Transform (DCT) technique in relation to JPEGS. I have never really explores the technical side of JPEGS and data compression so it was interesting to learn about the different types of compression and how data is sent in compressed amounts for more efficient sharing.

Image Link

Arcangel stated, “Data, especially large amounts, is expensive and slow to transport” and the most efficient was to send and receive large amounts of data would be mailing it (221). Imagining a world where large amounts of data couldn’t be compressed and everyone was force to send it on a hard drive via the post office, I am thankful that isn’t the case.  We are able to send large amounts of data because of compression. Arcangel explains that there are two kinds of compression, Lossless and Lossy.

Image Link

  

            Lossless compression doesn’t lose any data compares to Lossy. Lossless condenses the data from the original source making it smaller and easier to send. Arcangel explains this in computer language by saying “we have stores all the information using less space.” (221). An example of Lossless compression is “zip files” which we use in class to share our Proccessing projects on Google Drive.

            

Image Link

The second compression type is Lossy which is similar to Lossless by simplify the information just it doesn’t summarize it or make it smaller it loses some information. Lossy compression cannot be used for text because the data wouldn’t make sense, its like trying to write this blog post and the taking out all the vowels. Lossy compression is used for images, music and video about our ears and eyes will fill in the blanks of what is missing and not even notice it is gone in the first place.

It was really cool to learn how data is really stored and compressed for easy sharing. I have never really looked behind the scene of what was going on when I compressed a folder or emailed a photo and I can see why that would be hard to send.

Exercise 4 - Taoxi Li

Here're the codes and video for assignment 4.