How is a digital photo stored?

Just as the word digital suggests, a digital camera digitizes every image that it captures. What this means is that a typical rectangular photo is divided into a large number of squares which form the basic picture element called the pixel. Within a given pixel, the color of the image does not change. In the simple case where the pixel can either be black or white, the state of the pixel can be binary - either 0 (white) or 1 (black). The state of each pixel is stored at each location and when all the pixels are put together, you get your image back. Hence, the image is said to be digitized now.

In the real world, most pictures are stored either in color or in various layers of grey scale. For example, when each pixel element is stored as 8 bits, the number of states would be 256 - a number from 0 to 255. 0 represents white, 1 to 254 represents various scales of grey in increasing intensity, and 255 represents black. So when a color photo taken with a digital camera is converted to black and white, the correct term is actually a grey scale photo, because it has varying levels of black and white.

In color photographs, each pixel is made by combining various levels of primary colors. When an artist mixes colors with paints, he combines 2 colors to make a 3rd color. The primary colors are the colors which can combine to make all the colors required in a digital camera. In a typical camera, the primary colors used are Red, Green, and Blue (forming the RGB colors). Another set of primary colors used often are Cyan, Magenta, Yellow, and the Black (called the CMYK colors). Each color set has it's own advantages as it can capture a certain range of colors well. The rest of the post will be based on a RGB photo though it will equally apply to CMYK or any color set.

In the case of a RGB photo, each pixel uses up three storage units (for each color) of certain bit size associated with it. In the case of the 8-bit size storage unit, each pixel will have one storage unit each for intensity of red color (numbers 0 to 255 signifying varying levels of red color similar to the grey scale pictures discussed above), green color, and blue color. The various levels of red, green, and blue colors are mixed to form the actual color of that pixel. A picture stored in this manner would be 24-bit picture because each pixel would be stored in a 24-bit memory unit. The fact that the color of a pixel does not vary can be seen when you blow a picture beyond 100%. When the picture on the left is blown up to look at each pixel, the picture will look as the one on the right:



(Picture courtesy http://photo.net/equipment/digital/basics/)


The larger the resolution of the picture, the larger the number of pixels per inch (PPI). If one were to print a photo on paper, the resolution should be greater than 200 PPI on each side of the rectangle. A 3 MP camera can hence be used to print out photos of the 4" * 6" prints. The greater the resolution of the camera, the larger the size of the photo that can be printed out from it.

I will follow this post with a post on how the digital camera senses each photograph.

To get more information on this:
Photo.net's tutorial
Cambridge's tutorial

Living in a distributed world

In this post I had mentioned how ants work together to find a short way past obstacles, and said that they are not the only living things that work in that way. Engineers, though, have a different way of looking at it than pure scientists. These types of systems are called Distributed Control Systems, meaning that there is no central controlling authority, but instead, each individual follows a set of simple rules and this alone is enough to achieve the system's objectives.

Such a concept may seem alien at first. We humans are used to a hierarchical system of getting things done... in governing citizens, in managing corporations, in controlling manufacturing systems, in practically everything, we have a system of smaller parts reporting to progressively larger parts. But there is evidence to show that simple rules followed by a large number of peers can lead to as complicated a global behaviour as might be required.

I'm going to take an example of a non-living thing this time. The following experiment is called Conway's Game of Life. Consider a two dimensional matrix, in which each cell can have a state of "dead" or "alive". Each cell has eight neighbours; if zero or one of them are alive, the cell dies of loneliness. If two or three neighbours are alive, the cell lives. If four or more neighbours are alive, the cell dies of overpopulation. A dead cell "comes to life" if it has exactly three live neighbours.

Depending upon the initial state, an astounding variety of beautiful and complex patterns have been observed to arise out of these simple rules, including different types of oscillators, explosions, firing guns, even moving spaceships!

A snapshot of the Game of Life Applet: From http://www.ibiblio.org/lifepatterns/

While I used this only to demonstrate that simple rules can result in complex, self-organizing behaviour in a distributed world, the fact remains that a lot of the "management" in nature occurs this way. It doesn't take an engineer to understand the problems with centralized control in a large hierarchy; if you've tried getting something past the bureaucracy of your university, workplace or government, you know it all too well yourself! But engineers understand how difficult it is to design a distributed control system. It is only in extreme circumstances that they do indeed deploy systems in such a manner - for example, in large sensor networks, where thousands of sensor nodes may be released in forests, underwater, anywhere!

We live in a distributed world, in which the sum of parts is inevitably greater than the whole. Sometimes we scientists get a glimpse of it in our computer simulations, but you only need to look out of your window to understand. When you see a flock of birds flying in formation. When you see a beehive. And once you truly understand it, you will see the world through brand new eyes.

X Chromosome Inactivation

That cute kitten is Copycat or CC – the first cloned cat. She is genetically identical to her “mom” (to the right) but phenotypically different. The reason is because of the peculiar phenomenon called X-chromosome inactivation (XCI) that occurs in all the female cells in mammals.
In mammals sex is determined by differential inheritance of the sex chromosomes. Females are XX while males are XY. To compensate for having an extra dosage of X chromosome genes that might be developmentally fatal, the cells in the female embryo randomly select one X chromosome and shut it off (transcriptionally silence it). This “silent” state is maintained throughout life in the female cell. Since the phenomenon is random (each cell independently decides its fate), mammalian females are essentially mosaics. CC is different from her progenitor because the gene for coat color lies on the X chromosome and is randomly silenced in the clone, giving it it’s unique calico pattern.
The phenomenon is evolutionarily conserved in the mammalian lineage – since there is preliminary evidence to suggest that XCI occurs in Prototherians (egg laying mammals) who arose 200-300 million years ago and also in Metatherains (marsupials). Since X and Y differentiation also occurs in these lineages, it is likely that XCI arose as a way of dosage compensation.
How is it done? The random X chromosome inactivation has been linked to a region on the X chromosome, called Xic (X- inactivation center). What is so unique about this region is that all the genes characterized so far are non- coding genes – they make the RNA but this RNA is never used to make a cellular protein. The ncRNA (non coding RNA) from this region are responsible for counting the number of X chromosomes present in the cell, marking one for silencing and maintaining the silenced state. The most talked about transcript from this region is Xist which is essential for the silencing step.
The mechanism of how the ncRNA form Xist or other mapped locus do the inactivation is not yet known. It is most likely that transcription of these genes help to bring in chromatin remodeling factors (things that signal that the chromosome is silenced). Since Xist transcription would take about 30 minutes, during which the RNA is physically attached to the X chromosome, this explanation seems plausible. The unraveling of the mechanism would help us understand another dosage compensation mechanism (that of imprinting) but that is another blog.

Learning from the humble ant

The next time you're outdoors and you see a trail of marching ants, try a little experiment. Place an obstruction asymmetrically in their path, which is steep enough that they can't climb over it, and see what happens. At first, they would spread out equally in both directions to rejoin the trail. Over a period of time, you'll see the longer trail thin out and disappear as all the ants start taking the shorter trail.

What's really remarkable about this is that ants cannot see and cannot talk. That is to say, they lack the sensory organs for sight and sound. So how do they do this job of finding a short route around obstacles to their target food source?

The secret, as with many other things observed in nature, is elegant in its simplicity and inventiveness. Ants have a very good sense of smell, and lay out a class of chemicals called Pheromones to mark their trail. Each ant lays out roughly the same amount of pheromone for a particular food source, and the pheromone evaporates at a constant rate. When an ant moves towards a marked food source, it tends to move in a direction in which it senses a relatively higher concentration of pheromone.

That's all. If you're puzzled as to why I cut the explanation short, take a minute to think about how this would work and then read on. Now, when the obstacle is placed in the path, the ants can no longer sense the correct direction to travel, and they choose at random among the available directions. Ultimately, they would rejoin the trail, but each ant has now laid pheromone along the path it has taken. Initially, there is little to choose from between the two options, but over a period of time, the shorter path, simply by virtue of being shorter, would have a greater number of ants traversing the path back and forth. This means that the evaporating pheromone on the shorter route gets regenerated more often than on the longer route, causing more ants to choose that direction over the other. This further increases the disparity in pheromone level between the two choices, until there is insufficient pheromone on the longer trail to tempt even a single ant to go in that direction.

This kind of a process is called Auto-Catalytic, meaning it speeds itself up. Some time around 1990, an Italian scientist called Marco Dorigo got a brainwave on how to use this ant behaviour to solve combinatorial optimization problems, which are notoriously difficult to solve with conventional mathematical techniques.

What he did was to represent an optimization problem as a set of obstacles in the path of an ant trail. The decision of which direction to take at an obstacle represented the decision of what value to assign to a variable. In other words, if a variable can take four different values, it is like placing an obstacle giving an ant four different paths around it. Each variable in the problem is equivalent to an obstacle on the trail. "Pheromone concentrations" are maintained virtually for each decision at each obstacle.

The generation of a solution is done randomly, with the probability of taking a particular decision being proportional to the virtual pheromone concentration. Then, the "goodness" of the solution is quantified by the objective function, and this is analogous to the distance traveled to reach the food source by that route. If it is a poor solution, it is a long route and the pheromone concentrations along that route are increased by a small value. If it is a good solution, the pheromone concentrations are increased by a higher value; and all the time, pheromone is "evaporated" at a constant rate at every decision point.

It worked. In a way similar to how ants find paths, an algorithm known as Ant Colony Optimization (ACO) was developed and refined, and although it does not always find the globally best solution, it tends to find a good solution for even large problems in reasonably quick time.

In spite of all the nature-defying technological marvels scientists and engineers come up with, there seems to be an infinite depth to the workings of nature from which we learn continually. The ant optimization algorithm is but one in a wide class of algorithms under the banner of Swarm Intelligence, for ants, bees, birds and many other social denizens of nature have unique and innovative ways of doing a plethora of tasks. Computer scientists observe, and come up with smarter algorithms. Engineers observe, and come up with better control system schemes.

How wrong we are, in talking about "humble ants". It's we who are the "humble scientists".

Links for further reading:
Marco Dorigo's page on the behaviour of real ants that inspired ACO
Wikipedia
Marco Dorigo's landmark paper on Ant Systems

About us

TGFI has always been fascinated by two areas of science the most, astronomy and biology. Over time, the double helix has won her over and she decided to get a Ph.D in molecular biology way back when she was in high school. Fundamental processes in which genes operate, regulate and interact within a system fascinate her the most, which also happens to be the area in which she is getting her Ph.D. Five years at it and she realises how hard it has become for her to think outside her little world. Which is why she think this collaborative blog is a great idea, will make her get out of her world a bit, and talk about science in layman's words. She hopes to contribute on here once a month.

Sakshi is a grad studnet in UK and work on splicing and spliceosome assembly in the budding yeast. Her project focuses on how a cell targets misformed or locked spliceosome and activates turn over. Her interests in science lies in molecular / genetic underpinnings of life. Though she makes frequent forays into other aspects of biology, she is interested in regulation of gene expression (she believes RNA to be the coolest thing, ever). To keep her sanity in the ever crazed world of an underpaid, over-worked graduate student, she indulges in photography, reading (she prides herself on reading everything she can lay her hands on) and listening to music or watching movies.

Prashanth is an incurable romantic and an incorrigible geek from Chennai who tries to convince himself that they are both desirable qualities. He can be bribed into doing almost anything by lending him a good book. He is currently doing his Ph.D. in Industrial Engineering at the Pennsylvania State University.

Murthy is a grad student (and an engineer by birth) who is interested in many things some of them being: Control systems application to biological systems and control systems in nature(They are more interesting and far more elegant than the crude imitations scientists try to build and feel proud about). Algorithms and coding, Fluid mechanics and ofcourse Biology, specifically immune system of mammals and behavior of microorganisms as influenced by evolution are all part of his scientific interests. He is an Agricultural engineer(Specifically Dairy, food Engineer by training) now working in biofuels and control systems.

Back in high-school, BioPondit was aiming to be an engineer, loved physics, but ended up with a undergraduate degree in chemistry. Continuing in this vein, he trained as a Biophysicist in graduate school and is currently engaged in post-doctoral research in Cell Biology, shedding light on cellular signaling mechanisms. Love of books, cinema and an esoteric taste in music is what drives him - that and an ice-cold martini (stirred not shaken). A desire to communicate the beauty of science is what brings him here.

BaL is a grad student in the Chemistry department at the U of I. Broadly, he works on Computational Biophysics, Bioinformatics, and Evolution and these interdisciplinary fields keep him on his toes most of the time. He is also fascinated by cricket, movies (both Hollywood and Bollywood), and photography. He also tends to listen to a lot of desi music (especially Rehman), and classic rock.

Sonya is and always will be a science dork. Since a very young age, she has been fascinated by animal behavior of all aspects. She has long been amazed with how interactions within species, as well as, across species are affected by evolution and how they often eventually lead to speciation events. Although she believes that studying behavior in a natural environment is important, she is now more interested in how neuronal control (neuromodulators) may affect behavior. It is with this goal in mind, that she is working on her Ph.D in a Neurophysiology lab at University of Kentucky and currently working on understanding physiological mechanisms that may influence behavior in invertebrates. Since she is very engrossed in the early stages of her projects, she does not have things to keep her sane. You may on any given day find her talking to her crayfish..luckily they haven’t started talking back yet.

Curious Cat is a theoretical physicist. Her chosen tools of trade include non equilibrium statistical mechanics (the pen and paper kind) and her focus is the dynamics of fluids and fluid-like systems. She is currently post-doc-ing in the New York and Boston area. But this is just the way she earns her bread so to speak. She likes to read widely outside her area of specialization and likes to reduce all that information into minimum models that are easier to understand but still contains the essential features of the true science of the system. And that is why she is a physicist, becuase it gives her the training to be able to do that. She blogs about this and that at http://virtualcuriosityshop.blogspot.com. You can see the few physics posts she has written so far on this blog as well.

About this Blog

This blog is an attempt to answer any questions we have in mind such that an high school graduate should be able to understand the fundamentals behind the scientific ideas. It could also serve as a portal for us to broaden our knowledge on a subject. The topics that will be discussed will be from a wide range of topics dealing with basic science, and technology. This blog will hopefully be a learning experience for us and it will also be a medium through which we can share our knowledge with each other and with any person who is interested in these topics.