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Part 1: The World of Physics ends here.
Section A: Histogram Composition
Section B: Decomposing Light Absorption for Exposure
Sub I: 3rd Generation Digital Imaging
Sub II: This is where The World of Physics ends.
Section C: Histogram Digital Data Distribution
Section D: Automatic EV (Exposure Value) Compensation
Section E: Reading the Histogram, Exposure Evaluation
This replaces Part 6: Exposure Evaluation using Histograms, the appendage of:
I’m releasing this section in two parts for more space: (This is Part 1)
If you are just joining us, the PRELUDE & SYLLABUS section is the logical starting point for the series.
Welcome to Digital Photography #101
by Virtual Studio Photography (VSPHO)
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Section A: Histogram Composition
The Histogram is a graphical representation of the digital data distribution of light in a digital image. The actual Histogram composition therefore is just an average of Spatial pixel information represented in a qualitative, scalar graph.
Before we breakdown the data distribution in a histogram, let’s simply look at one now for later analysis.
Note: If you have a problem opening any pictures, please let me know in the comment section. Likewise, hitting the refresh icon at the top left of the screen is always helpful.
VEGAS (Once open, hit CNTL + or – to control image size.)
Histograms are simply bar graphs. Our Histogram is based off of the 256 gradient levels of light intensity in the JPG standard, 8 bit grey scale. The vertical bars read from left (dark areas of the image) to right (light/bright areas of the image). Because each vertical bar represents 1 of 3 colors (RGB), there are actually 768 vertical bars overlapping to visually reflect 256 gradient levels. The vertical height of the bars represent the total Spatial pixels used in that bar segment “IN PROPORTION” to the total Spatial pixels of the entire image. The vertical bar height does not represent any posterization when hitting the top. Hitting the top of the graph is just the limitation of the graph, no harm. (examples below)
Trivia: Our DSLR cameras actually use a 12 bit or 14 bit grey scale (color depth) to compose our digital images. Someday, Histograms will also be upgraded. But for now, Histograms are limited to the 8 bit grey scale.
When Histograms were originally designed for digital imaging, 5 Stops of DynamicRange was the limitation of digital imaging. This limitation became the current JPG industry standard for printing. But now, third generation sensors are capable of capturing 6+ Stops of DynamicRange (the JPG format still compresses the DynamicRange to 5 Stops).
Here is an example of a very early digital image. It only needed about 4 Stops of data to graph this image (Stops, digital data and dynamic range explained in Part 2).
Note: If you have a problem opening any pictures, please let me know in the comment section. Likewise, hitting the refresh icon at the top left of the screen is always helpful.
WARSAW1 (Once open, hit CNTL + or – to control image size.)
Reading a Histogram is a bit like watching Television, you don’t need to know how it’s built to utilize the information. But just for fun, let’s break down where the data comes from before we learn how to read it. (You can skip past this if you’d like to get straight to reading a Histogram in Section E of part 2.) *Part 2 will be released in a couple of days.*
The premise of this hypothetical exposure is to outline the process of digital imaging that is displayed in the Histogram.
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Section B: Light Absorption for Exposure
Because a Histogram is simply a bar graph that reflects the frequency of data, let’s see where our imaging data comes from.
Simply put, when photons collide with our digital sensor, their energy dislodges and ejects electrons. It’s called the “Photoelectric Effect.” To stay inline with our fun analogies, we’ll call it the Billiard Ball Effect.
Each photon striking our camera’s sensor knocks one electron (a 1:1 ratio) out of its orbit. Imagine an incredibly fast moving ping pong ball slamming into a billiard ball and knocking it clean off the table.
Note: If you wish to get a comprehensive explanation of this interaction you can Google on: Compton Scattering , Relativistic Momentum, Planck Relationship and Photoelectric Effect.
Actually, our sensor (CCD or CMOS) chip has two electrically charged sides. When photons collide with the front side, electrons are ejected from their orbit (only on that same side).
These collisions cause an imbalance of electrical charge from front to rear of the sensor. The camera’s circuitry meters the difference of both sides and stores the voltage difference in capacitors for each physical pixel (one capacitor = one physical pixel). After the shot, the chip is re-charged to a neutral state for the next shot. (This is why our digital cameras eat batteries so fast.)
Our digital image is composed from the data gathered from the voltage differences between each capacitor (outlined in ISO Sensitivity ). The Histogram shows the data distribution of spatial pixels comprising the image.
I’m doing this breakdown for the first time with you.
We know (from previous section) that Sunlight is measured in a unit called a Mole.
One Mole of Sunlight equals 6.022 times 10 to the 23 power (6.022 x 10^23) photons (602,200,000,000,000,000,000,000 photons) accumulated in a square meter area, over a one second period. (There are other parameters like it being on a clear day at high noon, latitude also being perpendicular to the Sun…)
We are now theoretically metering the light source directly, in this case the Sun. The direct metering of a light source is called “Incident light.”
Trivia: “Incident” refers to the geometry of the Angle of Incidence of direct light, not as in; “They had an incident at the bar.” (I couldn’t resist the pun.)
My analogy is only estimated, but it gives us an idea of the process of digital photography and the advantage of the Histogram.
So, do not take a picture of the Sun without proper filtering (filters covered in a future section), but I metered the Sun directly with proper filtering and then I shot a picture.
This is a great example of extreme DynamicRange. Normally, a bright blue sky at noon is at the top end of the DynamicRange. But contrasted with the intense power of direct Sun light, the sky becomes the darkest part of the image. (We will read the Histogram of this image in Section E.)
Note: A high resolution JPG of this image is available to download, below. You may make a poster of 16×20 or an 8×10 print if you wish.
Note: If you have a problem opening any pictures, please let me know in the comment section. Likewise, hitting the refresh icon at the top left of the screen is always helpful.
BLACKSKY (Once open, hit CNTL + or – to control image size.)
For our hypothetical illustration, we are going to maintain the light intensity level of a Mole reduced proportionally to our lens surface area for our exposure.
As there are one million square millimeters in One Square Meter, each square mm equals one millionth of a Mole. (Each sq mm = 6.022 x 10^17 photons.)
Our lens has a 67mm diameter front element with a surface area of 3526 square millimeters.
Note: In the upcoming lens section, we will summarize the refractive index and the transmission efficiency of a lens. For now, it’s just a lens.
Using the lens surface area as our light regulator, we multiple the surface area by the light intensity to start with 21 quintillion (2.1×10^19) photons of Incident light for our exposure.
We are creating a simple grey scale to evaluate in a Histogram, so we are metering a rough surface with an unknown reflective efficiency. Aperture and Shutter Speed are not yet factored into the photon count, but we need to adjust the light intensity level to reflect a realistic photon count .
Note: If you have a problem opening any pictures, please let me know in the comment section. Likewise, hitting the refresh icon at the top left of the screen is always helpful.
DIFFUSED (Once open, hit CNTL + or – to control image size.)
This example of diffused Incident Sun light is metered at f-stop 11 at 1/500 of a second. This falls perfectly inline with the EV table for a bright sun light exposure of Reflected light, number 16 on the EV table.
Note: The EV (Exposure Value) table gives us a general outline for the Aperture correlated to Shutter Speed at the standard ISO 100. Our digital camera light meters are calibrated to the EV table for exposure. (Google: “Exposure Value Table” for specifics.)
Like our example, this would be a totally white exposure of even light distribution (no DynamicRange), we’ll add black spatial pixels later for our grey scale DynamicRange.
When we meter a real picture, the actual reflection efficiency varies with the surface type (outlined in previous section, Part 4). The reflection efficiency is called “Hemispherical Spectral Reflectivity.”
Likewise, for this breakdown of a color Histogram, we are including the individual color frequencies from our Incident light. This inclusive color measurement of light is called “Spectral Irradiance.”
As we are following the light trail in our digital camera, we have just metered and we’re ready to set our exposure variables. We’ve already established a f-stop 11 with a 1/500 shutter speed, so let’s adjust the theoretical photon count.
F-stop 11 is 7 Stops from the maximum of f-stop 1. F-stop 1.0 means the aperture diameter would be exactly the same as the focal length. F-stop 1.4 is the first stop of half the light. F-stop 11 is the seventh stop that equals 1/128 of the light intensity. (e.g. f-stops 1.4, 2, 2.8, 4, 5.6, 8, 11)
Because we do not know our reflective surface efficiency, we are using the f-stop to adjust our photon count. We are setting the photon count to a realistic 99 quadrillion photons (9.9 x 10^16) for our analogy.
Next we adjust the photon count with the Shutter Speed. 1/500 Shutter Speed equals our partial Mole of one second divided by 500 = 1.98 x 10^14 photons make it to the sensor’s color filters.
Previously, part 3, we outlined the quantum efficiency of 70% of the digital color filters.
Adjusting to the 70% quantum efficiency of the color filters (RGB) leaves us with 139 trillion photons making it past the color filters to convert into electrons for voltage comparisons and physical pixel conversions.
We are outlining the quantity of light because the Digital Data Distribution of a Histogram is unique to every picture. It is a direct correlation to the distribution of light in proportion to the total accumulated light in that image. (Please keep in mind “in proportion.”)
For now, we have 139 trillion photons for our exposure.
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Sub I: 3rd Generation Imaging
Now let’s break down our 3rd generation imaging capabilities.
Digital camera manufactures do not publish their proprietary chip specifications, but I believe 3rd generation sensors utilize 10pF (10 Pico Farad) capacitors. These are very tiny capacitors to accommodate the space limitation on high density sensor chips.
Disclaimer: This is my best guess as to the specific capacitor size, but the analogy is still insightful.
We outlined capacitor storage in ISO Sensitivity . With real capacitor storage utilization, a full charge is approximately 90% of the full capacity, allowing 10% Head-Room for clipping reduction (posterization protection).
In short, each 10pF capacitor can store approximately 5.6 million electrons at the 90% storage level. (e.g. 5.6 million electrons = 6.25 quintillion electrons in One Farad (at one volt), times 10pF, times 90% head-room)
Let’s say our 3rd generation camera has 36 million physical pixels (each physical pixel has one capacitor). Each capacitor can hold 5.6 million electrons. 36 million capacitors equals 202 trillion electrons of total storage capacity. These capacitors are the Physical Pixels that get converted into Spatial Pixels.
So our digital sensor could hold potentially 202 trillion photons of pure light with no DynamicRange. This would be taking advantage of an ISO 100 which uses all of the capacitor’s capacity, each capacitor would be full (no image, just solid white).
Our first sample histogram is a Photoshop generated gradient grey scale as an introduction. We’ll use it as an example of this theoretical exposure we are outlining.
HISTOGRAM (Once open, hit CNTL + or – to control image size.)
Our exposure of 139 trillion photons is within the capacity of our digital sensor (202 trillion), but it is of pure light with no DynamicRange. We are now going to theoretically convert some Spatial pixels to black and grey to create a gradient DynamicRange. Of course, black is the absence of light. So the difference in our count of 139 trillion to the potential of 202 trillion allows for about 1/3 to be considered black.
Trivia: While digital photography is able to store and reproduce the light spectrum accurately, our eyes are tuned best to the middle of the light spectrum. Our eyes have “Cones” that are sensitive to the frequencies of light just like our ears are sensitive to the frequencies of sound. I think of these Cones as ice cream cones scooping up photons for color and sight.
I’ll quickly mention we also have “Rods” in our eyes that are hypersensitive to low intensity light. The Rods do not recognize color, they only discern images in low light situations, like hunting for food in moonlight.
But the human eye is best tuned for color and detail perception in both the middle color frequencies (greens), middle color temperature (5500 Kelvin) and middle intensity of light.
Our Histogram graphs the full color spectrum broken into 8 bits, and only 5 Stops of DynamicRange. Therefore, peaks through the middle of the Histogram represent the most beneficial light intensity for our vision. The middle also possibly represent the least interesting from an artistic point of view (more later).
Here is an example with a previous picture showing the most common mid-range that our eyes see best, the great outdoors.
Unlike the previous example above with an ancient digital image, the dynamic range in this is a full 5 Stops.
Note: If you have a problem opening any pictures, please let me know in the comment section. Likewise, hitting the refresh icon at the top left of the screen is always helpful.
EVANS HISTOGRAM (Once open, hit CNTL + or – to control image size.)
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Sub II: This is where The World of Physics ends.
This is truly where the World of Physics ends in digital imaging and The World of Computer Algorithms begins. This section outlined photon counts converted to capacitor voltages for Physical pixel creation.
Physical pixels are 100% computer generated image data that can only be stored in a non compressed file format like RAW, Photoshop PSD or uncompressed TIFF (these are the most common).
I hope you will join us for part 2 where we will outline a simplified but comprehensive summary of digital imaging data distribution.
2: Physics Can’t Be Beat
(Entering the World of Computer Algorithms)
Section C: Histogram Digital Data Distribution
Section D: Automatic EV (Exposure Value) Compensation.
Section E: Reading the Histogram, Exposure Evaluation
Below is a high resolution JPG digital negative of the above “BLACK SKY” picture for a 16×20 poster at 300 DPI (or an 8×10 print). It has a black border for the proper aspect ratio. It’s my thank you for your support.
Please join us in the next section:
Thanks again,
Virtual Studio Photography
16×20 BLACK SKY (6+ meg JPG download file.)