working demo

I have been writing some code to get this concept working and I have a prototype. The code takes the live video input from a camera and measures each pixel against a .csv file filled with RGB goodness.

The algorithm is very simple so that we can maintain a high framerate when running the sketch. I am using RGB colors and presorting them into a list. Ryan does the measurement to LAB and then does a conversion to RGB. I take the list and put it in a .csv file then I run this command on it so it sorts the list.

sort -g -n -r -t ‘,’ -k 1,1 myfile.csv

Then I am calculating the distance from the Red value of the pixel from the camera to the nearest red value in the list of colors.

Why did I do this? Well, RGB is the native format for the hardware. I did a rough calculation and if we run the simulation at 30fps at 640×480 we do about 10,000,000 calculations per second. Every conversion we do from one representation to the next is going to multiply this number, as does the number of colors we compare. If we choose …

If anyone has experience with using rendering intents as a form of color mapping onto an extremely limited color space, any help here would be immensely useful. We are currently using physical distance in LAB space to determine color relationships, but a perceptual intent may be more useful.

The original Macbeth ColorChecker Color Rendition Chart was introduced in the summer of 1976, and was “developed to facilitate quantitative or visual evaluations of color reproduction processes employed in photography, television, and printing…” . It was a tool to provide a window back to reality and was largely based on colors used for “analytical studies” (although a few simulated colors are supposed to represent human skin, foliage, blue sky, and a blue flower).

Along with these colors, for this first test, we chose colors for our custom target that would better simulate the colors that would be found in common gallery environments- ones directly measured from life, as well as reproduced skin tones from google. Essentially, our color target blends historically important colors that photographers have relied on to determine accuracy with colors that extend the experiment to evaluate the effects of quantizing the real. The nature of the 6-bit image will always exaggerate and expose the inherent error within the scientific representation of reality.

A summary of the colors initially chosen for the 6 bit image posted on June 12th:

• The 24 original color patches of the original ColorChecker- a nod to one of the most

…

We have developed a sketch model for determining colour accuracy based on a calculation of the distance between two points in lab colour space (CIELAB). In lab space, colour is mapped as a point on a 3-dimensional Cartesian plane. In order to determine accuracy of colour reproduction we have decided to pre-determine our colour space as indexed colour (only using a set number of specific colours) with a 6-bit per pixel colour space (64 colours).

Once we map these colours onto the lab space, we use the following formula D= √((L₁-L₂)² + (a₁-a₂)²+(b₁-b₂)²)) to calculate the distance between the colours. The complication here is that each pixel will have to run this equation 64 times (assuming 6-bit per pixel) to determine which of our indexed colours is closest (distance in CIELAB 3d space) and then replace the original colour with its’ closest approximation yielding something like the image below.Many of the colours will have visual relationships and approximate the colours we have selected, but others may have a less corollary visual relationship.

If you want to get a visual representation of CIELAB space beyond the screen shots below, download PerfX 3D Gamut Viewer.

We have yet to run any tests …

The first installation of the work created out of the TINT Arts Lab Residency will be in Paris, France at the TPTP Art Space in October 2010. Through our first Skype meeting, we discussed how we individually envision the process and product of the piece, and make sure we are all on the same page with the idea.

Ryan Boatright’s Error v.1, Brown Pear utilizes color measurements taken with a spectrophotometer on an actual object, which are then translated to a particular color space (sRGB). A picture of the same object is made, the color measurements are compared, and the quantitative information is gathered pertaining to the differences found in the RGB values. A problem with the implementation of this exact process exists. When attempting to use this process in a gallery space in real time, the color measurements on the individual subjects (art viewers), during the exhibition, will not be able to be made. Pre-determined measurements would be possible via color targets measured and then photographed with the same camera, with all ambient lighting conditions standardized before the exhibition and then repeated during it. We discussed how the colors measured through the live recording could, pixel by pixel, shift …

CDP is a collaborative endeavour that merges Lossless Processing (Adam Tindale + Jordan Tate) and Error (Ryan Boatright) to investigate conceptual concerns of image sorting with the determination of the accuracy of colour reproduction in a live loop that compares reality with its digital reproduction. While the exact nature of the project is still in flux, the basic tenets of the project are in place. We share an interest in questioning the representational nature of photography from technological, conceptual, and theoretical perspectives. We intend to create an installation whereby we can determine the veracity of colour reproduction in a video feed in real-time while simultaneously allowing all data that is not reproduced accurately to be visualized as a second channel video feed. Essentially, we plan to separate accurate data from inaccurate data and address both as art object.