Sensor Resolution

Sensor Resolution

In my previous two posts on this subject HERE and HERE I’ve been looking at pixel resolution as it pertains to digital display and print, and the basics of how we can manipulate it to our benefit.

You should also by aware by now that I’m not the worlds biggest fan of high sensor resolution 35mm format dSLRs – there’s nothing wrong with mega pixels; you can’t have enough of them in my book!

BUT, there’s a limit to how many you can cram into a 36 x 24 millimeter sensor area before things start getting silly and your photographic life gets harder.

So in this post I want to explain the reasoning behind my thoughts.

But before I get into that I want to address something else to do with resolution – the standard by which we judge everything we see around us – the resolution of the eye.

 

Human Eye – How Much Can We See?

In very simple terms, because I’m not an optician, the answer goes like this.

Someone with what some call 20/20/20 vision – 20/20 vision in a 20 year old – has a visual acuity of 5 line pairs per millimeter at a distance of 25 centimeters.

What’s a line pair?

5 line pairs per millimeter. Each line pair is 0.2mm and each line is 0.1mm.

5 line pairs per millimeter. Each line pair is 0.2mm and each line is 0.1mm.

Under ideal viewing conditions in terms of brightness and contrast the human eye can at best resolve 0.1mm detail at a distance of 25 centimeters.

Drop the brightness and the contrast and black will become less black and more grey, and white will become greyer; the contrast between light and dark becomes reduced and therefore that 0.1mm detail becomes less distinct.  until the point comes where the same eye can’t resolve detail any smaller than 0.2mm at 25cms, and so on.

Now if I try and focus on something at 25 cms my eyeballs start to ache,  so we are talking extreme close focus for the eye here.

An interesting side note is that 0.1mm is 100µm (microns) and microns are what we measure the size of sensor photosites in – which brings me nicely to SENSOR resolution.

 

Sensor Resolution – Too Many Megapixels?

As we saw in the post on NOISE we do not give ourselves the best chances by employing sensors with small photosite diameters.  It’s a basic fact of physics and mathematics – the more megapixels on a sensor, then the smaller each photosite has to be in order to fit them all in there;  and the smaller they are then the lower is their individual signal to noise or S/N ratio.

But there is another problem that comes with increased sensor resolution:

Increased diffraction threshold.

Andy Astbury,Wildlife in Pixels,sensor resolution,megapixels,pixel pitch,base noise,signal to noise ratio

Schematic of identical surface areas on lower and higher megapixel sensors.

In the above schematic we are looking at the same sized tiny surface area section on two sensors.

If we say that the sensor resolution on the left is that of a 12Mp Nikon D3, and the ‘area’ contains 3 x 3 photosites which are each 8.4 µm in size, then we can say we are looking at an area of about 25µm square.

On the right we are looking at that same 25µm (25 micron) square, but now it contains 5.2 x 5.2 photosites, each 4.84µm in size – a bit like the sensor resolution of a 36Mp D800.

 

What is Diffraction?

Diffraction is basically the bending or reflecting of waves by objects placed in their path (not to be confused with refraction).  As it pertains to our camera sensor, and overall image quality, it causes an general softening of every single point of sharp detail in the image that is projected onto the sensor during the exposure.

I say during the exposure because diffraction is ‘aperture driven’ and it’s effects only occur when the aperture is ‘stopped down’; which on modern cameras only occurs during the time the shutter is open.

At all other times you are viewing the image with the aperture wide open, and so you can’t see the effect unless you hit the stop down button (if you have one) and even then the image in the viewfinder is so small and dark you can’t see it.

As I said, diffraction is caused by aperture diameter – the size of the hole that lets the light in:

Andy Astbury,Wildlife in Pixels,sensor resolution,megapixels,pixel pitch,base noise,signal to noise ratio

Diffraction has a low presence in the system at wider apertures.

Light enters the lens, passes through the aperture and strikes the focal plane/sensor causing the image to be recorded.

Light waves passing through the center of the aperture and light waves passing through the periphery of the aperture all need to travel the same distance – the focal distance – in order for the image to be sharp.

The potential for the peripheral waves to be bent by the edge of the aperture diaphragm increases as the aperture becomes smaller.

Andy Astbury,Wildlife in Pixels,sensor resolution,megapixels,pixel pitch,base noise,signal to noise ratio

Diffraction has a greater presence in the system at narrower apertures.

If I apply some randomly chosen numbers to this you might understand it a little better:

Let’s say that the focal distance of the lens (not focal length) is 21.25mm.

As long as light passing through all points of the aperture travels 21.25mm and strikes the sensor then the image will be sharp; in other words, the more parallel the central and peripheral light waves are, then the sharper the image.

Making the aperture narrower by ‘stopping down’ increases the divergence between central and peripheral waves.

This means that peripheral waves have to travel further before the strike the sensor; further than 21.25mm – therefore they are no longer in focus, but those central waves still are.  This effect gives a fuzzy halo to every single sharply focused point of light striking our sensor.

Please remember, the numbers I’ve used above are meaningless and random.

The amount of fuzziness varies with aperture – wider aperture =  less fuzzy; narrower aperture = more fuzzy, and the circular image produced by a single point of sharp focus is known as an Airy Disc.

As we ‘stop down’ the aperture the edges of the Airy Disc become softer and more fuzzy.

Say for example, we stick a 24mm lens on our camera and frame up a nice landscape, and we need to use f14 to generate the amount of depth of field we need for the shot.  The particular lens we are using produces an Airy Disc of a very particular size at any given aperture.

Now here is the problem:

Andy Astbury,Wildlife in Pixels,sensor resolution,megapixels,pixel pitch,base noise,signal to noise ratio

Schematic of identical surface areas on lower and higher megapixel sensors and the same diameter Airy Disc projected on both of them.

As you can see, the camera with the lower sensor resolution and larger photosite diameter contains the Airy Disc within the footprint of ONE photosite; but the disc effects NINE photosites on the camera with the higher sensor resolution.

Individual photosites basically record one single flat tone which is the average of what they see; so the net outcome of the above scenario is:

Andy Astbury,Wildlife in Pixels,sensor resolution,megapixels,pixel pitch,base noise,signal to noise ratio

Schematic illustrating the tonal output effect of a particular size Airy Disc on higher and lower resolution sensors

On the higher resolution sensor the Airy Disc has produced what we might think of as ‘response pollution’ in the 8 surrounding photosites – these photosites need to record the values of the own ‘bits of the image jigsaw’ as well – so you end up with a situation where each photosite on the sensor ends up recording somewhat imprecise tonal values – this is diffraction in action.

If we were to stop down to f22 or f32 on the lower resolution sensor then the same thing would occur.

If we used an aperture wide enough on the higher resolution sensor – an aperture that generated an Airy Disc that was the same size or smaller than the diameter of the photosites – then only 1 single photosite would be effected and diffraction would not occur.

But that would leave of with a reduced depth of field – getting around that problem is fairly easy if you are prepared to invest in something like a Tilt-Shift lens.

Andy Astbury,Wildlife in Pixels,sensor resolution,megapixels,pixel pitch,base noise,signal to noise ratio

Both images shot with a 24mm TS lens at f3.5. Left image lens is set to zero and behaves as normal 24mm lens. Right image has 1 degree of down tilt applied.

Above we see two images shot with a 24mm Tilt-Shift lens, and both shots are at f3.5 – a wide open aperture.  In the left hand image the lens controls are set to zero and so it behaves like a standard construction lens of 24mm and gives the shallow depth of field that you’d expect.

The image on the right is again, shot wide open at f3.5, but this time the lens was tilted down by just 1 degree – now we have depth of field reaching all the way through the image.  All we would need to do now is stop the lens down to its sharpest aperture – around f8 – and take the shot;  and no worries about diffraction.

Getting back to sensor resolution in general, if your move into high megapixels counts on 35mm format then you are in a ‘Catch 22’ situation:

  • Greater sensor resolution enables you to theoretically capture greater levels of detail.

but that extra level of detail is somewhat problematic because:

  • Diffraction renders it ‘soft’.
  • Eliminating the diffraction causes you to potentially lose the newly acquired level of, say foreground detail in a landscape, due to lack of depth of field.

All digital sensors are susceptible to diffraction at some point or other – they are ‘diffraction limited’.

Over the years I’ve owned a Nikon D3 I’ve found it diffraction limited to between f16 & f18 – I can see it at f18 but can easily rescue the situation.  When I first used a 24Mp D3X I forgot what I was using and spent a whole afternoon shooting at f16 & f18 – I had to go back the next day for a re-shoot because the sensor is diffraction limited to f11 – the pictures certainly told the story!

Everything in photography is a trade-off – you can’t have more of one thing without having less of another.  Back in the days of film we could get by with one camera and use different films because they had very different performance values, but now we buy a camera and expect its sensor to perform all tasks with equal dexterity – sadly, this is not the case.  All modern consumer sensors are jacks of all trades.

If it’s sensor resolution you want then by far the best way to go about it is to jump to medium format, if you want image quality of the n’th degree – this way you get the ‘pixel resolution’ without many of the incumbent problems I’ve mentioned, simply because the sensors are twice the size; or invest in a TS/PC lens and take the Scheimpflug route to more depth of field at a wider aperture.

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Pixel Resolution – part 2

More on Pixel Resolution

In my previous post on pixel resolution  I mentioned that it had some serious ramifications for print.

The major one is PHYSICAL or LINEAR image dimension.

In that previous post I said:

  • Pixel dimension divided by pixel resolution = linear dimension

Now, as we saw in the previous post, linear dimension has zero effect on ‘digital display’ image size – here’s those two snake jpegs again:

Andy Astbury,wildlife in pixels,pixel,dpi,ppi,pixel resolution,photoshop,lightroom,adobe

European Adder – 900 x 599 pixels with a pixel resolution of 300PPI

Andy Astbury,wildlife in pixels,pixel,dpi,ppi,pixel resolution,photoshop,lightroom,adobe

European Adder – 900 x 599 pixels with a pixel resolution of 72PPI

Digital display size is driven by pixel dimensionNOT linear dimension or pixel resolution.

Print on the other hand is directly driven by image linear dimension – the physical length and width of our image in inches, centimeters or millimeters.

Now I teach this ‘stuff’ all the time at my Calumet workshops and I know it’s hard for some folk to get their heads around print size and printer output, but it really is simple and straightforward if you just think about it logically for minute.

Let’s get away from snakes and consider this image of a cute Red Squirrel:

Andy Astbury,wildlife in pixels,

Red Squirrel with Bushy Tail – what a cutey!
Shot with Nikon D4 – full frame render.

Yeah yeah – he’s a bit big in the frame for my taste but it’s a seller so boo-hoo – what do I know ! !

Shot on a Nikon D4 – the relevance of which is this:

  • The D4 has a sensor with a linear dimension of 36 x 24 millimeters, but more importantly a photosite dimension of 4928 x 3280. (this is the effective imaging area – total photosite area is 4992 x 3292 according to DXO Labs).

Importing this image into Lightroom, ACR, Bridge, CapOne Pro etc will take that photosite dimension as a pixel dimension.

They also attach the default standard pixel resolution of 300 PPI to the image.

So now the image has a set of physical or linear dimensions:

  • 4928/300  x  3280/300 inches  or  16.43″ x 10.93″

or

  • 417.24 x 277.71 mm for those of you with a metric inclination!

So how big CAN we print this image?

 

Pixel Resolution & Image Physical Dimension

Let’s get back to that sensor for a moment and ask ourselves a question:

  • “Does a sensor contain pixels, and can it have a PPI resolution attached to it?
  • Well, the strict answer would be No and No not really.

But because the photosite dimensions end up being ‘converted’ to pixel dimensions then let’s just for a moment pretend that it can.

The ‘effective’ PPI value for the D4 sensor could be easily derived from its long edge ‘pixel’ count of the FX frame divided by the linear length which is just shy of 36mm or 1.4″ – 3520 PPI or thereabouts.

So, if we take this all literally our camera captures and stores a file that has linear dimensions of  1.4″ x 0.9″, pixel dimensions of  4928 x 3280 and a pixel resolution of 3520 PPI.

Import this file into Lightroom for instance, and that pixel resolution is reduced to 300 PPI.  It’s this very act that renders the image on our monitor at a size we can work with.  Otherwise we’d be working on postage stamps!

And what has that pixel resolution done to the linear image dimensions?  Well it’s basically ‘magnified’ the image – but by how much?

 

Magnification & Image Size

Magnification factors are an important part of digital imaging and image reproduction, so you need to understand something – magnification factors are always calculated on the diagonal.

So we need to identify the diagonals of both our sensor, and our 300 PPI image before we can go any further.

Here is a table of typical sensor diagonals:

Andy Astbury

Table of Sensor Diagonals for Digital Cameras.

And here is a table of metric print media sizes:

Andy Astbury

Metric Paper Sizes including diagonals.

To get back to our 300 PPI image derived from our D4 sensor,  Pythagoras tells us that our 16.43″ x 10.93″ image has a diagonal of 19.73″ – or 501.14mm

So with a sensor diagonal of 43.2mm we arrive at a magnification factor of around 11.6x for our 300 PPI native image as displayed on our monitor.

This means that EVERYTHING on the sensor – photosites/pixels, dust bunnies, logs, lumps of coal, circles of confusion, Airy Discs – the lot – are magnified by that factor.

Just to add variety, a D800/800E produces native 300 PPI images at 24.53″ x 16.37″ – a magnification factor of 17.3x over the sensor size.

So you can now begin to see why pixel resolution is so important when we print.

 

How To Blow Up A Squirrel !

Let’s get back to ‘his cuteness’ and open him up in Photoshop:

Our Squirrel at his native 300 PPI open in Photoshop.

Our Squirrel at his native 300 PPI open in Photoshop.

See how I keep you on your toes – I’ve switched to millimeters now!

The image is 417 x 277 mm – in other words it’s basically A3.

What happens if we hit print using A3 paper?

Red Squirrel with Bushy Tail. D4 file at 300 PPI printed to A3 media.

Red Squirrel with Bushy Tail. D4 file at 300 PPI printed to A3 media.

Whoops – that’s not good at all because there is no margin.  We need workable margins for print handling and for mounting in cut mattes for framing.

Do not print borderless – it’s tacky, messy and it screws your printer up!

What happens if we move up a full A size and print A2:

Red Squirrel 300 PPI printed on A2

Red Squirrel D4 300 PPI printed on A2

Now that’s just over kill.

But let’s open him back up in Photoshop and take a look at that image size dialogue again:

Our Squirrel at his native 300 PPI open in Photoshop.

Our Squirrel at his native 300 PPI open in Photoshop.

If we remove the check mark from the resample section of the image size dialogue box (circled red) and make one simple change:

Our Squirrel at a reduced pixel resolution of 240 PPI open in Photoshop.

Our Squirrel at a reduced pixel resolution of 240 PPI open in Photoshop.

All we need to do is to change the pixel resolution figure from 300 PPI to 240 PPI and click OK.

We make NO apparent change to the image on the monitor display because we haven’t changed any physical dimension and we haven’t resampled the image.

All we have done is tell the print pipeline that every 240 pixels of this image must occupy 1 liner inch of paper – instead of 300 pixels per linear inch of paper.

Let’s have a look at the final outcome:

Red Squirrel D4 240 PPI printed on A2.

Red Squirrel D4 240 PPI printed on A2.

Perfick… as Pop Larkin would say!

Now we have workable margins to the print for both handling and mounting purposes.

But here’s the big thing – printed at 2880+ DPI printer output resolution you would see no difference in visual print quality.  Indeed, 240 PPI was the Adobe Lightroom, ACR default pixel resolution until fairly recently.

So there we go, how big can you print?? – Bigger than you might think!

And it’s all down to pixel resolution – learn to understand it and you’ll find a lot of  the “murky stuff” in photography suddenly becomes very simple!

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Pixel Resolution

What do we mean by Pixel Resolution?

Digital images have two sets of dimensions – physical size or linear dimension (inches, centimeters etc) and pixel dimensions (long edge & short edge).

The physical dimensions are simple enough to understand – the image is so many inches long by so many inches wide.

Pixel dimension is straightforward too – ‘x’ pixels long by ‘y’ pixels wide.

If we divide the physical dimensions by the pixel dimensions we arrive at the PIXEL RESOLUTION.

Let’s say, for example, we have an image with pixel dimensions of 3000 x 2400 pixels, and a physical, linear dimension of 10 x 8 inches.

Therefore:

3000 pixels/10 inches = 300 pixels per inch, or 300PPI

and obviously:

2400 pixels/8 inches = 300 pixels per inch, or 300PPI

So our image has a pixel resolution of 300PPI.

 

How Does Pixel Resolution Influence Image Quality?

In order to answer that question let’s look at the following illustration:

Andy Astbury,pixels,resolution,dpi,ppi,wildlife in pixels

The number of pixels contained in an image of a particular physical size has a massive effect on image quality. CLICK to view full size.

All 7 square images are 0.5 x 0.5 inches square.  The image on the left has 128 pixels per 0.5 inch of physical dimension, therefore its PIXEL RESOLUTION is 2 x 128 PPI (pixels per inch), or 256PPI.

As we move from left to right we halve the number of pixels contained in the image whilst maintaining the physical size of the image – 0.5″ x 0.5″ – so the pixels in effect become larger, and the pixel resolution becomes lower.

The fewer the pixels we have then the less detail we can see – all the way down to the image on the right where the pixel resolution is just 4PPI (2 pixels per 0.5 inch of edge dimension).

The thing to remember about a pixel is this – a single pixel can only contain 1 overall value for hue, saturation and brightness, and from a visual point of view it’s as flat as a pancake in terms of colour and tonality.

So, the more pixels we can have between point A and point B in our image the more variation of colour and tonality we can create.

Greater colour and tonal variation means we preserve MORE DETAIL and we have a greater potential for IMAGE SHARPNESS.

REALITY

So we have our 3 variables; image linear dimension, image pixel dimension and pixel resolution.

In our typical digital work flow the pixel dimension is derived from the the photosite dimension of our camera sensor – so this value is fixed.

All RAW file handlers like Lightroom, ACR etc;  all default to a native pixel resolution of 300PPI. * (this 300ppi myth annoys the hell out of me and I’ll explain all in another post).

So basically the pixel dimension and default resolution SET the image linear dimension.

If our image is destined for PRINT then this fact has some serious ramifications; but if our image is destined for digital display then the implications are very different.

 

Pixel Resolution and Web JPEGS.

Consider the two jpegs below, both derived from the same RAW file:

Andy Astbury,pixels,resolution,dpi,ppi,Wildlife in Pixels

European Adder – 900 x 599 pixels with a pixel resolution of 300PPI

European Adder - 900 x 599 pixels with a pixel resolution of 72PPI

European Adder – 900 x 599 pixels with a pixel resolution of 72PPI

In order to illustrate the three values of linear dimension, pixel dimension and pixel resolution of the two images let’s look at them side by side in Photoshop:

Andy Astbury,photoshop,resolution,pixels,ppi,dpi,wildlife in pixels,image size,image resolution

The two images opened in Photoshop – note the image size dialogue contents – CLICK to view full size.

The two images differ in one respect – their pixel resolutions.  The top Adder is 300PPI, the lower one has a resolution of 72PPI.

The simple fact that these two images appear to be exactly the same size on this page means that, for DIGITAL display the pixel resolution is meaningless when it comes to ‘how big the image is’ on the screen – what makes them appear the same size is their identical pixel dimensions of 900 x 599 pixels.

Digital display devices such as monitors, ipads, laptop monitors etc; are all PIXEL DIMENSION dependent.  The do not understand inches or centimeters, and they display images AT THEIR OWN resolution.

Typical displays and their pixel resolutions:

  • 24″ monitor = typically 75 to 95 PPI
  • 27″ iMac display = 109 PPI
  • iPad 3 or 4 = 264 PPI
  • 15″ Retina Display = 220 PPI
  • Nikon D4 LCD = 494 PPI

Just so that you are sure to understand the implication of what I’ve just said – you CAN NOT see your images at their NATIVE 300 PPI resolution when you are working on them.  Typically you’ll work on your images whilst viewing them at about 1/3rd native pixel resolution.

Yes, you can see 2/3rds native on a 15″ MacBook Pro Retina – but who the hell wants to do this – the display area is minuscule and its display gamut is pathetically small. 😉

Getting back to the two Adder images, you’ll notice that the one thing that does change with pixel resolution is the linear dimensions.

Whilst the 300 PPI version is a tiny 3″ x 2″ image, the 72 PPI version is a whopping 12″ x 8″ by comparison – now you can perhaps understand why I said earlier that the implications of pixel resolution for print are fundamental.

Just FYI – when I decide I’m going to create a small jpeg to post on my website, blog, a forum, Flickr or whatever – I NEVER ‘down sample’ to the usual 72 PPI that get’s touted around by idiots and no-nothing fools as “the essential thing to do”.

What a waste of time and effort!

Exporting a small jpeg at ‘full pixel resolution’ misses out the unnecessary step of down sampling and has an added bonus – anyone trying to send the image direct from browser to a printer ends up with a print the size of a matchbox, not a full sheet of A4.

It won’t stop image theft – but it does confuse ’em!

I’ve got a lot more to say on the topic of resolution and I’ll continue in a later post, but there is one thing related to PPI that is my biggest ‘pet peeve’:

 

PPI and DPI – They Are NOT The Same Thing

Nothing makes my blood boil more than the persistent ‘mix up’ between pixels per inch and dots per inch.

Pixels per inch is EXACTLY what we’ve looked at here – PIXEL RESOLUTION; and it has got absolutely NOTHING to do with dots per inch, which is a measure of printer OUTPUT resolution.

Take a look inside your printer driver; here we are inside the driver for an Epson 3000 printer:

Andy Astbury,printer,dots per inch,dpi,pixels per inch,ppi,photoshop,lightroom,pixel resolution,output resoloution

The Printer Driver for the Epson 3000 printer. Inside the print settings we can see the output resolutions in DPI – Dots Per Inch.

Images would be really tiny if those resolutions were anything to do with pixel density.

It surprises a lot of people when they come to the realisation that pixels are huge in comparison to printer dots – yes, it can take nearly 400 printer dots (20 dots square) to print 1 square pixel in an image at 300 PPI native.

See you in my next post!

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Noise and the Camera Sensor

Camera sensors all suffer with two major afflictions; diffraction and noise; and between them these two afflictions cause more consternation amongst photographers than anything else.

In this post I’m going to concentrate on NOISE, that most feared of sensor afflictions, and its biggest influencer – LIGHT, and its properties.

What Is Light?

As humans we perceive light as being a constant continuous stream or flow of electromagnetic energy, but it isn’t!   Instead of flowing like water it behaves more like rain, or indeed, bullets from a machine gun!   Here’s a very basic physics lesson:

Below is a diagram showing the Bohr atomic model.

We have a single positively charged proton (black) forming the nucleus, and a single negatively charged electron (green) orbiting the nucleus.

The orbit distance n1 is defined by the electrostatic balance of the two opposing charges.

Andy Astbury,noise,light,Bohr atomic model

The Bohr Atomic Model

If we apply energy to the system then a ‘tipping point’ is reached and the electron is forced to move away from the nucleus – n2.

Apply even more energy and the system tips again and the electron is forced to move to an even higher energy level – n3.

Now here’s the fun bit – stop applying energy to the system.

As the system is no longer needing to cope with the excess energy it returns to its natural ‘ground’ state and the electron falls back to n1.

In the process the electron sheds the energy it has absorbed – the red squiggly bit – as a quantum, or packet, of electromagnetic energy.

This is basically how a flash gun works.

This ‘packet’ has a start and an end; the start happens as the electron begins its fall back to its ground state; and the end occurs once the electron arrives at n1 – therefore it can perhaps be tentatively thought of as being particulate in nature.

So now you know what Prof. Brian Cox knows – CERN here we come!

Right, so what’s this got to do with photography and camera sensor noise

Camera Sensor Noise

All camera sensors are effected by noise, and this noise comes in various guises:

Firstly, the ‘noise control’ sections of most processing software we use tend to break it down into two components; luminosity, or luminance noise; and colour noise.  Below is a rather crappy image that I’m using to illustrate what we might assume is the reality of noise:

Andy Astbury,noise

This shot shows both Colour & Luminance noise.
The insert shows the shot and the small white rectangle is the area we’re concentrating on.

Now let’s look at the two basic components: Firstly the LUMINANCE component

Andy Astbury,noise

Here we see the LUMINANCE noise component – colour & colour noise components have been removed for clarity.

Next, the COLOUR NOISE bit:

Andy Astbury,noise

The COLOUR NOISE component of the area we’re looking at. All luminance noise has been removed.

I must stress that the majority of colour noise you see in your files inside LR,ACR,CapOne,PS etc: is ‘demosaicing colour noise’, which occurs during the demosaic processes.

But the truth is, it’s not that simple.

Localised random colour errors are generated ‘on sensor’ due to the individual sensor characteristics as we’ll see in a moment, because noise, in truth, comes in various guises that collectively effect luminosity and colour:

Andy Astbury,noise

Shot Noise

This first type of noise is Shot Noise – called so because it’s basically an intrinsic part of the exposure, and is caused by photon flux in the light reflected by the subject/scene.

Remember – we see light in a different way to that of our camera. What we don’t notice is the fact that photon streams rise and fall in intensity – they ‘flux’ – these variations happen far too fast for our eyes to notice, but they do effect the sensor output.

On top of this ‘fluxing’ problem we have something more obvious to consider.

Lighter subjects reflect more light (more photons), darker subjects reflect less light (less photons).

Your exposure is always going to some sort of ‘average’, and so is only going to be ‘accurate’ for certain areas of the scene.

Lighter areas will be leaning towards over exposure; darker areas towards under exposure – your exposure can’t be perfect for all tones contained in the scene.

Tonal areas outside of the ‘average exposure perfection’ – especially the darker ones – may well contain more shot noise.

Shot noise is therefore quite regular in its distribution, but in certain areas it becomes irregular – so its often described as ‘pseudo random’ .

Andy Astbury,noise

Read Noise

Read Noise – now we come to a different category of noise completely.

The image is somewhat exaggerated so that you can see it, but basically this is a ‘zero light’ exposure; take a shot with the lens cap on and this is what happens!

What you can see here is the background sensor noise when you take any shot.

Certain photosites on the sensor are actually generating electrons even in the complete absence of light – seeing as they’re photo-voltaic they shouldn’t be doing this – but they do.

Added to this are AD Converter errors and general ‘system noise’ generated by the camera – so we can regard Read Noise as being like the background hiss, hum and rumble we can hear on a record deck when we turn the Dolby off.

Andy Astbury,noise

Thermal & Pattern Noise

In the same category as Read Noise are two other types of noise – thermal and pattern.

Both again have nothing to do with light falling on the sensor, as this too was shot under a duvet with the lens cap on – a 30 minute exposure at ISO 100 – not beyond stupid when you think of astro photography and star trail shots in particular.

You can see in the example that there are lighter and darker areas especially over towards the right side and top right corner – this is Thermal Noise.

During long exposures the sensor actually heats up, which in turn increases the response of photosites in those areas and causes them to release more electrons.

You can also see distinct vertical and some horizontal banding in the example image – this is pattern noise, yet another sensor noise signature.

Andy Astbury,noise

Under Exposure Noise – pretty much what most photographers think of when they hear the word “noise”.

Read Noise, Pattern Noise, Thermal Noise and to a degree Shot Noise all go together to form a ‘base line noise signature’ for your particular sensor, so when we put them all together and take a shot where we need to tweak the exposure in the shadow areas a little we get an overall Under Exposure Noise characteristic for our camera – which let’s not forget, contains other elements of  both luminance noise and colour noise components derived from the ISO settings we use.

All sensors have a base ISO – this can be thought of as the speed rating which yields the highest Dynamic Range (Dynamic Range falls with increasing ISO values, which is basically under exposure).

At this base ISO the levels of background noise generated by the sensor just being active (Pattern,Read & Thermal) will be at their lowest, and can be thought of as the ‘base noise’ of the sensor.

How visually apparent this base noise level is depends on what is called the Signal to Noise Ratio – the higher the S/N ratio the less you see the noise.

And what is it that gives us a high signal?

MORE Photons – that’s what..!

The more photons each photosite on the sensor can gather during the exposure then the more ‘masked’ will be any internal noise.

And how do we catch more photons?

By using a sensor with BIGGER photosites, a larger pixel pitch – that’s how.  And bigger photosites means LESS MEGAPIXELS – allow me to explain.

Buckets in the Rain A

Here we see a representation of various sized photosites from different sensors.

On the right is the photosite of a Nikon D3s – a massive ‘bucket’ for catching photons in – and 12Mp resolution.

Moving left we have another FX sensor photosite – the D3X at 24Mp, and then the crackpot D800 and it’s mental 36Mp tiny photosite  – can you tell I dislike the D800 yet? 

One the extreme left is the photosite from the 1.5x APS-C D7100 just for comparison.

Now cast your mind back to the start of this post where I said we could tentatively regard photons as particles – well, let’s imagine them as rain drops, and the photosites in the diagram above as different sized buckets.

Let’s put the buckets out in the back yard and let’s make the weather turn to rain:

Andy Astbury,Wildlife in Pixels,sensor resolution,megapixels,pixel pitch,base noise,signal to noise ratio

Various sizes of photosites catching photon rain.

Here it comes…

Andy Astbury,Wildlife in Pixels,sensor resolution,megapixels,pixel pitch,base noise,signal to noise ratio

It’s raining

OK – we’ve had 2 inches of rain in 10 seconds! Make it stop!

Andy Astbury,Wildlife in Pixels,sensor resolution,megapixels,pixel pitch,base noise,signal to noise ratio

All buckets have 2 inches of water in them, but which has caught the biggest volume of rain?

Thank God for that..

If we now get back to reality, we can liken the duration of the rain downpour as shutter speed, the rain drops themselves as photons falling on the sensor, and the consistency of water depth in each ‘bucket’ as a correct level of exposure.

Which bucket has the largest volume of water, or which photosite has captured the most photons – in other words which sensor has the highest S/N Ratio?   That’s right – the 12Mp D3s.

To put this into practical terms let’s consider the next diagram:

Andy Astbury,Wildlife in Pixels,sensor resolution,megapixels,pixel pitch,base noise,signal to noise ratio

Increased pixel pitch = Increased Signal to Noise Ratio

The importance of S/N ratio and its relevance to camera sensor noise can be seen clearly in the diagram above – but we are talking about base noise at native or base ISO.

If we now look at increasing the ISO speed we have a potential problem.

As I mentioned before, increasing ISO is basically UNDER EXPOSURE followed by in-camera “push processing” – now I’m showing my age..

Andy Astbury,noise,iso

The effect of increased ISO – in camera “push processing” automatically lift the exposure value to where the camera thinks it is supposed to be.

By under exposing the image we reduce the overall Signal to Noise Ratio, then the camera internals lift all the levels by a process of amplification – and this includes amplifying  the original level of base noise.

So now you know WHY and HOW your images look noisy at higher ISO’s – or so you’d think – again,  it’s not that simple; take the next two image crops for instance:

Andy Astbury, iso,noise,sensor noise

Kingfisher – ISO 3200 Nikon D4 – POOR LIGHT – Click for bigger view

Andy Astbury, iso,noise,sensor noise

Kingfisher – ISO 3200 Nikon D4 – GOOD LIGHT – CLICK for bigger view

If you click on the images (they’ll open up in new browser tabs) you’ll see that the noise from 3200 ISO on the D4 is a lot more apparent on the image taken in poor light than it is on the image taken in full sun.

You’ll also notice that in both cases the noise is less apparent in the high frequency detail (sharp high detail areas) and more apparent in areas of low frequency detail (blurred background).

So here’s “The Andy Approach” to noise and high ISO.

1. It’s not a good idea to use higher ISO settings just to combat poor light – in poor light everything looks like crap, and if it looks crap then the image will look even crappier.When I get in a poor light situation and I’m not faced with a “shot in a million” then I don’t take the shot.

2. There’s a big difference between poor light and low light that looks good – if that’s the case shoot as close to base ISO as you can get away with in terms of shutter speed.

3. I you shoot landscapes then shoot at base ISO at all times and use a tripod and remote release – make full use of your sensors dynamic range.

4. The Important One – don’t get hooked on megapixels and so-called sensor resolution – I’ve made thousands of landscape sales shot on a 12Mp D3 at 100 ISO. If you are compelled to have more megapixels buy a medium format camera which will generate a higher S/N Ratio because the photosites are larger.

5. If you shoot wildlife you’ll find that the necessity for full dynamic range decreases with angle of view/increasing focal length – using a 500mm lens you are looking at a very small section of what your eye can see, and tones contained within that small window will rarely occupy anywhere near the full camera dynamic range.

Under good light this will allow you to use a higher ISO in order to gain that crucial bit of extra shutter speed – remember, wildlife images tend to be at least 30 to 35% high frequency detail – noise will not be as apparent in these areas as it is in the background; hence to ubiquitous saying of  wildlife photographers “Watch your background at all times”.

Well, I think that’s enough to be going on with – but there’s oh so much more!

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Bit Depth

Bit Depth – What is a Bit?

Good question – from a layman’s point of view it’s the smallest USEFUL unit of computer/digital information; useful in the fact that it can have two values – 0 or 1.

Think of it as a light switch; it has two positions – ON and OFF, 1 or 0.

bit, Andy Astbury, bit depth

A bit is like a light switch.

We have 1 switch (bit) with 2 potential positions (bit value 0 or 1) so we have a bit depth of 1. We can arrive at this by simple maths – number of switch positions to the power of the number of switches; in other words 2 to the 1st power.

How Does Bit Depth Impact Our Images:

So what would this bit depth of 1 mean in image terms:

Andy Astbury,bit depth,

An Image with a Bit Depth of 1 bit.

Well, it’s not going to win Wildlife Photographer of the Year is it!

Because each pixel in the image can only be black or white, on or off, 0 or 1 then we only have two tones we can use to describe the entire image.

Now if we were to add another bit to the overall bit depth of the image we would have 2 switches (bits) each with 2 potential values so the total number of potential values, so 2 to the 2nd, or 4 potential output values/tones.

Andy Astbury,bits,bit depth

An image with a bit depth of 2 bits.

Not brilliant – but it’s getting there!

If we now double the bit depth again, this time to 4 bit, then we have 2 to the 4th, or 16 potential tones or output values per image pixel:

Andy Astbury,bits,bit depth

A bit depth of 4 bits gives us 16 tonal values.

And if we double the bit depth again, up to 8 bit we will end up with 2 to the 8th power, or 256 tonal values for each image pixel:

Andy Astbury,bits,bit depth

A bit depth of 8 bits yields what the eye perceives to be continuous unbroken tone.

This range of 256 tones (0 to 255) is the smallest number of tonal values that the human eye can perceive as being continuous in nature; therefore we see an unbroken range of greys from black to white.

More Bits is GOOD

Why do we need to use bit depths HIGHER than 8 bit?

Our modern digital cameras capture and store RAW images to a bit depth of 12 bit, and now in most cases 14 bit – 4096 & 16,384 tonal values respectively.

Just as we use the ProPhotoRGB colour space to preserve as many CAPTURED COLOURS as we can, we need to apply a bit depth to our pixel-based images that is higher than the capture depth in order to preserve the CAPTURED TONAL RANGE.

It’s the “bigger bucket” or “more stairs on the staircase” scenario all over again – more information about a pixels brightness and colour is GOOD.

Andy Astbury,bits,bit depth,tonal range,tonality,tonal graduation

How Tonal Graduation Increases with Bit Depth.

Black is black, and white is white, but increased bit depth gives us a higher number of steps/tones; tonal graduations, to get from black to white and vice versa.

So, if our camera captures at 14 bit we need a 15 bit or 16 bit “bucket” to keep it in.  And for those who want to know why a 14 bit bucket ISN’T a good idea then try carrying 2 gallons of water in a 2 gallon bucket without spillage!

The 8 bit Image Killer

Below we have two identical grey scale images open in Photoshop – simple graduations from black to white; one is a 16 bit image, the other 8 bit:

Andy Astbury,bits,bit depth,tone,tonal graduation

16 bit greyscale at the top. 8 bit greyscale below – CLICK Image to view full size.

Now everything looks OK at this “fit to screen” magnification; and it doesn’t look so bad at 1:1 either, but let’s increase the magnification to 1600% so we can see every pixel:

 

Andy Astbury,bits,bit depth,tone,tonal range,tonal graduation

CLICK Image to view full size. At 1600% magnification we can see that the 8 bit file is degraded.

At this degree of magnification we can see a huge amount of image degradation in the lower, 8 bit image whereas the upper, 16 bit image looks tonally smooth in its graduation.

The degradation in the 8 bit image is simply due to the fact that the total number of tones is “capped” at 256. and 256 steps to get from the black to the white values of the image are not sufficient – this leaves gaps in the image that Photoshop has to fill with “invented” tonal information based on its own internal “logic”….mmmmmm….

There was a time when I thought “girlies” were the most illogical things on the planet; but since Photoshop, now I’m not so sure…!

The image is a GREYSCALE – RGB ratios are supposedly equal in every pixel, but as you can see, Photoshop begins to skew the ratios where it has to do its “inventing” so we not only have luminosity artifacts, but we have colour artifacts being generated too.

You might look upon this as “pixel peeping” and “geekey”, but when it comes to image quality, being a pixel-peeping Geek is never a bad thing.

Of course, we all know 8bit as being “jpeg”, and these artifacts won’t show up on a web-based jpeg for your website; but if you are in the business of large scale gallery prints, then printing from an 8 bit image file is never going to be a good idea as these artifacts WILL show on the final print.

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Lightroom Tutorials #2

 

Lightroom Tutorials,video,lessoneagle,golden eagle,snow,winter,Norway,wildlife

Image Processing in Lightroom & Photoshop

 

In this Lightroom tutorial preview I take a close look at the newly evolved Clone/Heal tool and dust spot removal in Lightroom 5.

This newly improved tool is simple to use and highly effective – a vast improvement over the great tool that it was already in Lightroom 4.

 

Lightroom Tutorials  Sample Video Link below: Video will open in a new window

 

https://vimeo.com/64399887

 

This 4 disc Lightroom Tutorials DVD set is available from my website at http://wildlifeinpixels.net/dvd.html

How White is Paper White?

What is Paper White?

We should all know by now that, in RGB terms, BLACK is 0,0,0 and that WHITE is 255,255,255 when expressed in 8 bit colour values.

White can also be 32,768: 32,768: 32,768 when viewed in Photoshop as part of a 16 bit image (though those values are actually 15 bit – yet another story!).

Either way, WHITE is WHITE; or is it?

paper white,photo paper white,printing paper white,Permajet paper whites, snow, Arctic Fox

Arctic Fox in Deep Snow ©Andy Astbury/Wildlife in Pixels

Take this Arctic Fox image – is anything actually white?  No, far from it! The brightest area of snow is around 238,238,238 which is neutral, but it’s not white but a very light grey.  And we won’t even discuss the “whiteness” of  the fox itself.

paper white,photo paper white,printing paper white,Permajet paper whites, bird, pheasant, snow

Hen Pheasant in Snow ©Andy Astbury/Wildlife in Pixels

The Hen Pheasant above was shot very late on a winters afternoon when the sun was at a very low angle directly behind me – the colour temperature has gone through the roof and everything has taken on a very warm glow which adds to the atmosphere of the image.

paper white,photo paper white,printing paper white,Permajet paper whites, snow, sunset, extreme colour temperature

Extremes of colour temperature – Snow Drift at Sunset ©Andy Astbury/Wildlife in Pixels

We can take the ‘snow at sunset’ idea even further, where the suns rays strike the snow it lights up pink, but the shadows go a deep rich aquamarine blue – what we might call a ‘crossed curves’ scenario, where shadow and lower mid tones are at a low Kelvin temperature, and upper mid tones and highlights are at a much higher Kelvin.

All three of these images might look a little bit ‘too much’ – but try clicking one and viewing it on a darker background without the distractions of the rest of the page – GO ON, TRY IT.

Showing you these three images has a couple of purposes:

Firstly, to show you that “TRUE WHITE” is something you will rarely, if ever, photograph.

Secondly, viewing the same image in a different environment changes the eyes perception of the image.

The secondary purpose is the most important – and it’s all to do with perception; and to put it bluntly, the pack of lies that your eyes and brain lead you to believe is the truth.

Only Mother Nature, wildlife and cameras tell the truth!

So Where’s All This Going Andy, and What’s it got to do with Paper White?

Fair question, but bare with me!

If we go to the camera shop and peruse a selection of printer papers or unprinted paper samplers, our eyes tell us that we are looking at blank sheets of white paper;  but ARE WE?

Each individual sheet of paper appears to be white, but we see very subtle differences which we put down to paper finish.

But if we put a selection of, say Permajet papers together and compare them with ‘true RGB white’ we see the truth of the matter:

paper white,photo paper white,printing paper white,Permajet paper whites

Paper whites of a few Permajet papers in comparison to RGB white – all colour values are 8bit.

Holy Mary Mother of God!!!!!!!!!!!!!!!!

I’ll bet that’s come as a bit of a shocker………

No paper is WHITE; some papers are “warm”; and some are “cool”.

So, if we have a “warmish” toned image it’s going to be a lot easier to “soft proof” that image to a “warm paper” than a cool one – with the result of greater colour reproduction accuracy.

If we were to try and print a “cool” image on to “warm paper” then we’ve got to shift the whole colour balance of the image, in other words warm it up in order for the final print to be perceived as neutral – don’t forget, that sheet of paper looked neutral to you when you stuck it in the printer!

Well, that’s simple enough you might think, but you’d be very, very wrong…

We see colour on a print because the inks allow use to see the paper white through them, but only up to a point.  As colours and tones become darker on our print we see less “paper white” and more reflected colour from the ink surface.

If we shift the colour balance of the entire image – in this case warm it up – we shift the highlight areas so they match the paper white; but we also shift the shadows and darker tones.  These darker areas hide paper white so the colour shift in those areas is most definitely NOT desirable because we want them to be as perceptually neutral as the highlights.

What we need to do in truth is to somehow warm up the higher tonal values while at the same time keep the lowest tonal values the same, and then somehow match all the tones in between the shadows and highlights to the paper.

This is part of the process called SOFT PROOFING – but the job would be a lot easier if we chose to print on a paper whose “paper white” matched the overall image a little more closely.

The Other Kick in the Teeth

Not only are we battling the hue of paper white, or tint if you like, but we also have to take into account the luminance values of the paper – in other words just how “bright” it is.

Those RGB values of paper whites across a spread of Permajet papers – here they are again to save you scrolling back:

paper white,photo paper white,printing paper white,Permajet paper whites

Paper whites of a few Permajet papers in comparrison to RGB white – all colour values are 8bit.

not only tell us that there is a tint to the paper due to the three colour channel values being unequal, but they also tell us the brightest value we can “print” – in other words not lay any ink down!

Take Oyster for example; a cracking all-round general printer paper that has a very large colour gamut and is excellent value for money – Permajet deserve a medal for this paper in my opinion because it’s economical and epic!

Its paper white is on average 240 Red, 245 Green ,244 Blue.  If we have any detail in areas of our image that are above 240, 240, 240 then part of that detail will be lost in the print because the red channel minimum density (d-min) tops out at 240; so anything that is 241 red or higher will just not be printed and will show as 240 Red in the paper white.

Again, this is a problem mitigated in the soft proofing process.

But it’s also one of the reasons why the majority of photographers are disappointed with their prints – they look good on screen because they are being displayed with a tonal range of 0 to 255, but printed they just look dull, flat and generally awful.

Just another reason for adopting a Colour Managed Work Flow!

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Colour Space & Profiles

colour space

From Camera to Print
copyright 2013 Andy Astbury/Wildlife in Pixels

Colour space and device profiles seem to cause a certain degree of confusion for a lot of people; and a feeling of dread, panic and total fear in others!

The reality of colour spaces and device profiles is that they are really simple things, and that how and why we use them in a colour managed work flow is perfectly logical and easy to understand.

Up to a point colour spaces and device profiles are one and the same thing – they define a certain “volume” of colours from red to green to blue, and from black to white – and all the colours that lie in between those five points.

The colour spaces that most photographers are by now familiar with are ProPhotoRGB, AdobeRGB(1998) and sRGB – these are classed as “working colour spaces” and are standards of colour set by the International Color Consortium, or ICC; and they all have one thing in common; where red, green and blue are present in equal amounts the colour produced will be NEUTRAL.

The only real differences between these three working colour spaces is the “distances” between the five set points of red, green, blue, black and white.  The greater the distance between the three primary colours then the greater is the degree of graduation between them, hence the greater the number of potential colours.  In the diagram below we can see the sRGB & ProPhoto working colour spaces displayed on the same axes:

colour space volume

The sRGB & ProPhoto colour spaces. The larger volume of ProPhoto contains more colour variety between red, green & blue than sRGB.

If we were to mark five different points on the surface of a partially inflated balloon,  and then inflate it some more then the points in relation to the balloons surface would NOT change: the points remain the same.  But the spatial distances between the points would change, as would the internal volume.  It’s the same with our five points of colour reference – red, green, blue, black & white – they do NOT change between colour spaces; red is red no matter what the working colour space.  But the range of potential colours between our 5 points of reference increases due to increased colour space volume.

So now we have dealt with the basics of the three main working colour spaces, we need to consider the volume of colour our camera sensor can capture – if you like, its colour space; but I’d rather use the word “gamut”.

Let’s take the Canon 5DMk3 as an example, and look at the volume, or gamut, of colour that its sensor can capture, in direct comparison with our 3 quantifiable working colour spaces:

colour space

The Canon 5DMk3 sensor gamut (black) in comparison to ProPhoto (largest), AdobeRGB1998 & sRGB (smallest) working colour spaces.

In a previous blog article I wrote – see here – I mentioned how to setup the colour settings in Photoshop, and this is why.  If you want to keep the greatest proportion of your camera sensors captured colour then you need to contain the image within the ProPhotoRGB working colour space.  If you don’t, and you use AdobeRGB or sRGB as Photoshops working colour space then you will loose a certain proportion of those captured colours – as I’ve heard it put before, it’s like a sex change operation – certain colours get chopped off, and once that’s happened you can’t get them back!

To keep things really simple just think of the 3 standard working colour spaces as buckets – the bigger the bucket, the more colour it contains; and you can’t tip the colours captured by your camera into a smaller bucket without getting spillage and making a mess on the floor!

As I said before, working colour spaces are neutral; but seldom does our camera ever capture a scene that contains pure neutrals.  Even though an item in the scene may well be neutral in colour, camera sensors quite often skew these colours ever so slightly; most Canon RAW files always look a teeny-weeny ever so slight bit magenta to me when I import them; but there again I’m a Nikon shooter seem to have a minute greenish tinge to them before processing.

Throughout our imaging work flow we have 3 stages:

1. Input (camera or scanner).

2. Working Process (Lightroom, Photoshop etc).

3. Output (printer for example).

And each stage has its representative type of colour space – we have input profiles, working colour spaces and output profiles.

So we have our camera capture gamut (colour space if you like) and we’ve opened our image in Photoshop or Lightroom in the ProPhoto working colour space – there’s NO SPILLAGE!

We now come to the crux of colour management; before we can do anything else we need to profile our “window onto our image” – the monitor.

In order to see the reality of what the camera captured we need to ensure that our monitor is in line with our WORKING COLOUR SPACE in terms of colour neutrality – not that of the camera as some people seem to think.

All 3 working colour spaces posses the same degree of colour neutrality where red, green & blue are present at the same values irrespective of physical size of the colour space.

So as long as our monitor is profiled to be:

1. Accurately COLOUR NEUTRAL

2. Displaying maximum brightness only in the presence true white – which you’ll hardly ever photograph, even snow isn’t white.

then we will see a highly workable representation of image colour neutrality and luminosity on our monitor.  Only by working this way can we actually tell if the camera has captured the image correctly in terms of colour balance and overall exposure.

And the fact that our monitor CANNOT display all the colours contained within our big ProPhoto bucket is, to all intents and purposes,  a fairly mute point; though seeing as many of them as possible is never a bad thing.

And using a monitor that does NOT display the volume of colour approximating or exceeding that of the Adobe working space can be highly detrimental for the reasons discussed in my previous post.

Now that we’ve covered input profiles and working colour spaces we need to move on and outline the basics of output profiles, and printer profiles in particular.

colour space, profile, print profile

Adobe & sRGB working paces in comparison to the colours contained in the Kingfisher image and the profile for Permajet Oyster paper using the Epson 7900 printer. (CLICK image for full sized view).

In the image above we can see both the Adobe and sRGB working spaces and the full distribution of colours contained in the Kingfisher image which is a TIFF file in our big ProPhoto bucket of colour;  and a black trace which is the colour profile (or space if you like) for Permajet Oyster paper using Epson UltraChrome HDR ink on an Epson 7900 printer.

As we can see, some of the colours contained in the image fall outside the gamut of the sRGB working colour space; notably some oranges and “electric blues” which are basically colours of the subject and are most critical to keep in the print.

However, all those ProPhoto colours are capable of being reproduced on the Epson 7900 using Permajet Oyster paper because, as the black trace shows, the printer/ink/paper combination can reproduce colours that lie outside of the Adobe working colour space.

The whole purpose of that particular profile is to ensure that the print matches what we can see on the monitor both in terms of colour and brightness – in other words, what we see is what we get – WYSIWYG!

The beauty of a colour managed workflow is that it’s economical – assuming the image is processed correctly then printing via an accurate printer profile can give you a perfect printed rendition of your screen image using just a single sheet of paper – and only one sheets worth of ink.

colour space, colour profile

The difference between colour profiles for the same printer paper on different printers. Epson 3000 printer profile trace in Red (CLICK image for full size view).

If we were to switch printers to an Epson 3000 using UltraChrome K3 ink on the very same paper, the area circled in white shows us that there are a couple of orange hue colours that are a little problematic – they lie either close to or outside the colour gamut of this printer/ink/paper combination, and so they need to be changed in order to ‘fit’, either by localised adjustment or variation of rendering intent – but that’s a story for later!

Why is it different? Well, it’s not to do with the paper for sure, so it’s down to either the ink change or printer head.  Using the same K3 ink in an Epson 4800 brings the colours back into gamut, so the difference is in the printer head itself, or the printer driver, but as I said, it’s a small problem easily fixed.

When you consider the low cost of achieving an accurate monitor profile – see this previous post – and combine that with an accurate printer output profile or two to match your chosen printer papers, and then deploy these assets correctly you have a proper colour managed workflow.  Add to that the cost savings in ink and paper and it becomes a bit of a “no-brainer” doesn’t it?

In this post I set out to hopefully ‘demystify’ colour spaces and profiles in terms of what they are and how they are used – I hope I’ve succeeded!

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Monitor Calibration with ColorMunki

Monitor Calibration with ColorMunki Photo

Following on from my previous posts on the subject of monitor calibration I thought I’d post a fully detailed set of instructions, just to make sure we’re all “singing from the same hymn sheet” so to speak.

Basic Setup

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Put the ColorMunki spectrophotometer into the cover/holder and attach the USB cable.

_D4R7798

Always keep the sliding dust cover closed when storing the ColorMunki in its holder – this prevents dust ingress which will effect the device performance.

BUT REMEMBER – slide the cover out of the way before you begin the calibration process!

colormunkiSpecCover

Install the ColorMunki software on your machine, register it via the internet, then check for any available updates.

Once the software is fully installed and working you are ready to begin.

Plug the USB cable into an empty USB port on your computer – NOT an external hub port as this can sometimes cause device/system communication problems.

Launch the ColorMunki software.

The VERY FIRST THING YOU NEED TO DO is open the ColorMunki software preferences and ensure that it looks like the following screen:

PC: File > Preferences

Mac: ColorMunki Photo > Preferences

Screen Shot 2013-10-17 at 11.28.32

The value for the Tone Response Curve MUST be set to 2.2 which is the default value.

The ICC Profile Version number MUST be set to v2 for best results – this is NOT the default.

Ensure the two check boxes are “ticked”.**

** These settings can be something of a contentious issue. DDC & LUT check boxes should only be “ticked” if your Monitor/Graphics card combination offers support for these modes.

If you find these settings make your monitor become excessively dark once profiling has been completed, start again ensuring BOTH check boxes are “unticked”.

Untick both boxes if you are working on an iMac or laptop as for the most part these devices support neither function.

For more information on this, a good starting point is a page on the X-Rite website available on the link below:

http://xritephoto.com/ph_product_overview.aspx?ID=1115&Action=Support&SupportID=5561

If you are going to use the ColorMunki to make printer profiles then ensure the ICC Profile Version is set to v2.

By default the ColorMunki writes profiles in ICC v4 – not all computer operating systems can function correctly from a graphics colour aspect; but they can all function perfectly using ICC v2.

You should only need to do this operation once, but any updates from X-Rite, or a re-installation of the software will require you to revisit the preferences panel just to check all is well.

Once this panel is set as above Click OK and you are ready to begin.

 

Monitor Calibration

This is the main ColorMunki GUI, or graphic user interface:

Screen Shot 2013-10-17 at 12.32.58

Click Profile My Display

Screen Shot 2013-10-17 at 11.17.49

Select the display you want to profile.

I use what is called a “double desktop” and have two monitors running side by side; if you have just a single monitor connected then that will be the only display you see listed.

Click Next>.

Screen Shot 2013-10-17 at 11.18.18

Select the type of display – we are talking here about monitor calibration of a screen attached to a PC or Mac so select LCD.

Laptops – it never hurts a laptop to be calibrated for luminance and colour, but in most cases the graphics output LUT (colour Look Up Table) is barely 8 bit to begin with; the calibration process will usually reduce that to less than 8 bit. This will normally result in the laptop screen colour range being reduced in size and you may well see “virtual” colour banding in your images.

Remedy: DON’T PROCESS ON A LAPTOP – otherwise “me and the boys” will be paying you a visit!

Select Advanced.

Deselect the ambient light measurement optionit can be expensive to set yourself up with proper lighting in order to have an ICC standard viewing/processing environment; daylight (D65) bulbs are fairly cheap and do go a long way towards helping, but the correct amount of light and the colour of the walls and ceiling, and the exclusion of extraneous light sources of incorrect colour temperature (eg windows) can prove somewhat more problematic and costly.

Processing in darkened room without light is by far the easiest, cheapest and most cost-effective way of obtaining correct working conditions.

Set the Luminance target Value to 120 (that’s 120 candelas per square meter if you’re interested!).

Set the Target White Point to D65 (that’s 6500 degrees Kelvin – mean average daylight).

Click Next>.

Screen Shot 2013-10-17 at 11.19.44

With the ColorMunki connected to your system this is the screen you will be greeted with.

You need to calibrate the device itself, so follow the illustration and rotate the ColorMunki dial to the indicated position.

Once the device has calibrated itself to its internal calibration tile you will see the displayed GUI change to:

Screen Shot 2013-10-17 at 11.20.26

Follow the illustration and return the ColorMunki dial to its measuring position.

Screen Shot 2013-10-17 at 11.20.49

Click Next>.

Screen Shot 2013-10-17 at 11.21.11

With the ColorMunki in its holder and with the spectrophotometer cover OPEN for measurement, place the ColorMunki on the monitor as indicated on screen and in the image below:

XR-CLRMNK-01

We are now ready to begin the monitor calibration.

Click Next>.

The first thing the ColorMunki does is measure the luminosity of the screen. If you get a manual adjustment prompt such as this (indicates non-support/disabling of DDC preferences option):

ColorMunki-Photo-display-screen-111

Simply turn adjust the monitor brightness slowly until the indicator line is level with the central datum line; you should see a “tick” suddenly appear when the luminance value of 120 is reached by your adjustments.

LCDs are notoriously slow to respond to changes in “backlight brightness” so make an adjustment and give the monitor a few seconds to settle down.

You may have to access your monitor controls via the screen OSD menu, or on Mac via the System Preferences > Display menu.

Once the Brightness/Luminance of the monitor is set correctly then ColorMunki will proceed will proceed with its monitor output colour measurements.

In order for you to understand monitor calibration and what is going on here is a sequence of slides from one of my workshops on colour management:

moncal1

moncal2

moncal3

moncal4

Once the measurements are complete the GUI will return to the screen in this form.

Screen Shot 2013-10-17 at 11.26.29

Either use the default profile name, or one of your own choice and click Save.

NOTE: Under NO CIRCUMSTANCES can you rename the profile after it has been saved, or any other .icc profile for that matter, otherwise the profile will not work.

Click Next>.

Screen Shot 2013-10-17 at 11.27.00

Click Save again to commit the new monitor profile to you operating system as the default monitor profile.

You can set the profile reminder interval from the drop down menu.

Click Next>.

Screen Shot 2013-10-17 at 12.32.58

Monitor calibration is now complete and you are now back to the ColorMunki startup GUI.

Quit or Exit the ColorMunki application – you are done!

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Monitor Calibration Devices

Colour management is the simple process of maintaining colour accuracy and consistency between the ACTUAL COLOURS in your image, in terms of Hue, Saturation and Luminosity; and those reproduced on your RGB devices; in this case, displayed on your monitor. Each and every pixel in your image has its very own individual RGB colour values and it is vital to us as photographers that we “SEE” these values accurately displayed on our monitors.

If we were to visit The National Gallery and gaze upon Turners “Fighting Temeraire” we would see all those sumptuous colours on the canvass just as J.M.W. intended; but could we see the same colours if we had a pair of Ray Bans on?

No, we couldn’t; because the sunglasses behave as colour filters and so they would add a “tint” to every colour of light that passes through them.

What you need to understand about your monitor is that it behaves like a filter between your eyes and the recorded colours in your image; and unless that “filter” is 100% neutral in colour, then it will indeed “tint” your displayed image.

So, the first effect of monitor calibration is that the process NEUTRALIZES any colour tint in the monitor display and so shows us the “real colours” in our images; the correct values of Hue and Saturation.

Now imagine we have an old fashioned Kodak Ektachrome colour slide sitting in a projector. If we have the correct wattage bulb in the projector we will see the correct LUMINOSITY of the slide when it is projected.

But if the bulb wattage is too high then the slide will project too brightly, and if the bulb wattage is too low then the projected image will not be bright enough.

All our monitors behave just like a projector, and as such they all have a brightness adjustment which we can directly correlate to our old fashioned slide projector bulb, and this brightness, or backlight control is another aspect of monitor calibration.

Have you done a print that comes out DARKER than the image displayed on the screen?

If you have then your monitor backlight is too bright!

And so, the second effect of monitor calibration is the setting of the correct level of brightness or back lighting of our monitor in order for us to see the true Luminosity of the pixels in our images.

Without accurate Monitor Calibration your ability to control the accuracy of colour and overall brightness of your images is severely limited.

I get asked all the time “what’s the best monitor calibration device to use” so, above is a short video (no sound) I’ve made showing the 3D and 2D plots of profiles I’ve just made for the same monitor using teo different monitor calibration devices/spectrophotometers from opposite ends of the pricing scale.

The first plot you see in black is the AdobeRGB1998 working colour space – this is only shown as a standard by which you can judge the other two profiles; if you like, monitor working colour spaces.

The yellow plot that shows up as an overlay is a profile done with an Xrite ColourMunki Photo, which usually retails for around £300 – and it clearly shows this particular monitor rendering a greater number of colours in certain areas than are contained in the Adobe1998 reference space.

The cyan plot is the same monitor, but profiled with the i1Photo Pro 2 spectro – not much change out of £1300 thank you very much – and the resulting profile virtually an identical twin of the one obtained with the ColorMunki which retails for a quarter of the price!

Don’t get me wrong, the i1 is a far more efficient monitor calibration device if you want to produce custom PRINTER profiles as well, but if you are happy using OEM profiles and just want perfect monitor calibration then I’d say the ColorMunki Photo is the more sensible purchase; or better still the ColorMunki Display at only around £110.

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