25 MP sensor of Sony

Hi
In article , "David J.
Littleboy" wrote:


This is where 3-color perpixel sensors are problematic: the raw files are
three times larger.

harr be ye meaning the Foveon thinggy?

Thought they had seperate per pixel bits internally I did.


See Ya
(when bandwidth gets better ;-)

Chris Eastwood
Photographer, Programmer
Motorcyclist and dingbat

please remove undies for reply

Floyd L. Davidson wrote:
"David J Taylor" wrote:
Chris Malcolm wrote:
[]
You're quite right, the non-linearity of gamma correction does
increase the resolutions required. The important point remains that
the number of bits in the encoding doesn't affect the dynamic range
between darkest and lightest, just the number of steps in between.

Chris,

It affects things by how you define dynamic range, whether the lowest bit
is just on-off, or whether there is a finite signal-to-noise ratio at the
bottom end of the "dynamic range". Some people use the latter definition,
and it may relate to how film dynamic range is defined.

By either definition, his statement is not true.

Were I making your assumptions about range representation, which I'm
not. I suspect that the most important thing we disagree about is
whether we disagree :-)

--
Chris Malcolm DoD #205
IPAB, Informatics, JCMB, King's Buildings, Edinburgh, EH9 3JZ, UK
[http://www.dai.ed.ac.uk/homes/cam/]

Chris Malcolm wrote:
Floyd L. Davidson wrote:
"David J Taylor" wrote:
Chris Malcolm wrote:
[]
You're quite right, the non-linearity of gamma correction does
increase the resolutions required. The important point remains that
the number of bits in the encoding doesn't affect the dynamic range
between darkest and lightest, just the number of steps in between.

Chris,

It affects things by how you define dynamic range, whether the lowest bit
is just on-off, or whether there is a finite signal-to-noise ratio at the
bottom end of the "dynamic range". Some people use the latter definition,
and it may relate to how film dynamic range is defined.

By either definition, his statement is not true.

Were I making your assumptions about range representation, which I'm
not. I suspect that the most important thing we disagree about is
whether we disagree :-)

To you perhaps. But what you said was:

"The important point remains that the number
of bits in the encoding doesn't affect the
dynamic range between darkest and lightest,
just the number of steps in between."

And that, as virtually *any* tutorial or text on the
subject will explain the math for you, is wrong.

The standard formula is:

Dynamic_Range_in_dB = (n * 6.02) + 1.72

Where 'n' is the number of bits. The number of bits of
course does in fact set the maximum number of steps, and
therefore the minimum size of those steps. And *that*
is what sets the dynamic range that can be encoded.

You can claim all you like that any range can be
endocded with one bit, but the *fact* is that 1 bit is
limited to 6.7 dB of dynamic range.

Digital encoding wil *never* make sense until the reason
for that limit is clearly understood.

--
Floyd L. Davidson http://www.apaflo.com/floyd_davidson
Ukpeagvik (Barrow, Alaska)

In article ,
(Floyd L. Davidson) wrote:

Chris Malcolm wrote:
Floyd L. Davidson wrote:
"David J Taylor"
wrote:
Chris Malcolm wrote:
[]
You're quite right, the non-linearity of gamma correction does
increase the resolutions required. The important point remains that
the number of bits in the encoding doesn't affect the dynamic range
between darkest and lightest, just the number of steps in between.

Chris,

It affects things by how you define dynamic range, whether the lowest bit
is just on-off, or whether there is a finite signal-to-noise ratio at the
bottom end of the "dynamic range". Some people use the latter definition,
and it may relate to how film dynamic range is defined.

By either definition, his statement is not true.

Were I making your assumptions about range representation, which I'm
not. I suspect that the most important thing we disagree about is
whether we disagree :-)

To you perhaps. But what you said was:

"The important point remains that the number
of bits in the encoding doesn't affect the
dynamic range between darkest and lightest,
just the number of steps in between."

And that, as virtually *any* tutorial or text on the
subject will explain the math for you, is wrong.

The standard formula is:

Dynamic_Range_in_dB = (n * 6.02) + 1.72


That formula calculates the theoretical dynamic range for a single
sample of data. It is not correct to apply this to a set of data, such
as an audio signal, an image, or a video signal. It doesn't make any
more sense than it would to say that a camera's performance is pi*r^2 of
the front lens.



Where 'n' is the number of bits. The number of bits of
course does in fact set the maximum number of steps, and
therefore the minimum size of those steps. And *that*
is what sets the dynamic range that can be encoded.

You can claim all you like that any range can be
endocded with one bit, but the *fact* is that 1 bit is
limited to 6.7 dB of dynamic range.

Digital encoding wil *never* make sense until the reason
for that limit is clearly understood.

--
I don't read Google's spam. Reply with another service.

Kevin McMurtrie wrote:
In article ,
(Floyd L. Davidson) wrote:

To you perhaps. But what you said was:

"The important point remains that the number
of bits in the encoding doesn't affect the
dynamic range between darkest and lightest,
just the number of steps in between."

And that, as virtually *any* tutorial or text on the
subject will explain the math for you, is wrong.

The standard formula is:

Dynamic_Range_in_dB = (n * 6.02) + 1.72

That formula calculates the theoretical dynamic range for a single
sample of data.

That is not correct. It calculates the dynamic range of
the resulting digital data set, not from 1 sample but
over the average of many (i.e., all possible) samples.
The quantization distortion is determined by the
integral of the error signal spread across the entire
range of each step (as opposed to one sample that has
only a single value of error).

It is not correct to apply this to a set of data, such
as an audio signal, an image, or a video signal.

As *any* half decent tutorial or text on the subject will
explain, that is *exactly* what it applies to. Here are
three examples, two books on audio signals and one URL
that discusses digital imaging.

"Digital Telephony", 3rd Ed., John C. Bellamy, 2000,
published by John Wiley & Sons. pp. 99-101

The above text shows the mathematical derivation, as well
as examples of its use.

"Telecommunications System Engineering", 3rd Ed.,
Roger L. Freeman, 1996, published by John Wiley & Sons.
p 353.

This text does not show the derivation, but states the formula
and give examples.

http://www.cis.rit.edu/class/simg712...antization.pdf

See pages 12 and 13.

It doesn't make any
more sense than it would to say that a camera's performance is pi*r^2 of
the front lens.
....
Digital encoding wil *never* make sense until the reason
for that limit is clearly understood.

That is still a very valid statement, obviously.

Until people understand the theory involved in digitizing
analog data with a linear ADC... the rest of this discussion
is meaningless wandering in the dark.

--
Floyd L. Davidson http://www.apaflo.com/floyd_davidson
Ukpeagvik (Barrow, Alaska)

Floyd L. Davidson wrote:
Chris Malcolm wrote:
Floyd L. Davidson wrote:
"David J Taylor" wrote:
Chris Malcolm wrote:
[]
You're quite right, the non-linearity of gamma correction does
increase the resolutions required. The important point remains that
the number of bits in the encoding doesn't affect the dynamic range
between darkest and lightest, just the number of steps in between.

Chris,

It affects things by how you define dynamic range, whether the lowest bit
is just on-off, or whether there is a finite signal-to-noise ratio at the
bottom end of the "dynamic range". Some people use the latter definition,
and it may relate to how film dynamic range is defined.

By either definition, his statement is not true.

Were I making your assumptions about range representation, which I'm
not. I suspect that the most important thing we disagree about is
whether we disagree :-)

To you perhaps. But what you said was:

"The important point remains that the number
of bits in the encoding doesn't affect the
dynamic range between darkest and lightest,
just the number of steps in between."

And that, as virtually *any* tutorial or text on the
subject will explain the math for you, is wrong.

The standard formula is:

Dynamic_Range_in_dB = (n * 6.02) + 1.72

Where 'n' is the number of bits. The number of bits of
course does in fact set the maximum number of steps, and
therefore the minimum size of those steps. And *that*
is what sets the dynamic range that can be encoded.

You can claim all you like that any range can be
endocded with one bit, but the *fact* is that 1 bit is
limited to 6.7 dB of dynamic range.

If you can't see the assumption in that formula even though you have
gone so far as to describe it there's no point in continuing this
argument.

(There's nothing wrong with it being based on assumptions -- nearly
all standard engineering formulas are. But it matters when you're
trying to discuss the principles behind the engineering
assumptions. And why should we be doing that? Because we were
discsussing the differences between two different kinds of engineering
implementation, which raised the question of the relevance of the
usual assumptions which are based on the usual technology.

At least, that's what I was trying to do :-)

--
Chris Malcolm DoD #205
IPAB, Informatics, JCMB, King's Buildings, Edinburgh, EH9 3JZ, UK
[http://www.dai.ed.ac.uk/homes/cam/]

Chris Malcolm wrote:
Floyd L. Davidson wrote:
Chris Malcolm wrote:
Floyd L. Davidson wrote:
"David J Taylor" wrote:
Chris Malcolm wrote:
[]
You're quite right, the non-linearity of gamma correction does
increase the resolutions required. The important point remains that
the number of bits in the encoding doesn't affect the dynamic range
between darkest and lightest, just the number of steps in between.

Chris,

It affects things by how you define dynamic range, whether the lowest bit
is just on-off, or whether there is a finite signal-to-noise ratio at the
bottom end of the "dynamic range". Some people use the latter definition,
and it may relate to how film dynamic range is defined.

By either definition, his statement is not true.

Were I making your assumptions about range representation, which I'm
not. I suspect that the most important thing we disagree about is
whether we disagree :-)

To you perhaps. But what you said was:

"The important point remains that the number
of bits in the encoding doesn't affect the
dynamic range between darkest and lightest,
just the number of steps in between."

And that, as virtually *any* tutorial or text on the
subject will explain the math for you, is wrong.

The standard formula is:

Dynamic_Range_in_dB = (n * 6.02) + 1.72

Where 'n' is the number of bits. The number of bits of
course does in fact set the maximum number of steps, and
therefore the minimum size of those steps. And *that*
is what sets the dynamic range that can be encoded.

You can claim all you like that any range can be
endocded with one bit, but the *fact* is that 1 bit is
limited to 6.7 dB of dynamic range.

If you can't see the assumption in that formula even though you have
gone so far as to describe it there's no point in continuing this
argument.

There are a number of assumptions. Your vague reference
to such doesn't make much sense. (It is assumed, for
example, that the error signal is evenly distributed
across the entire range of a single step. That may not
be true for any given signal, but it has been
demonstrated to be a close enough approximation.)

(There's nothing wrong with it being based on assumptions -- nearly
all standard engineering formulas are. But it matters when you're
trying to discuss the principles behind the engineering
assumptions. And why should we be doing that?

Because we want *accurate* answers that allow further
understanding of that and other processes.

Because we were
discsussing the differences between two different kinds of engineering
implementation, which raised the question of the relevance of the
usual assumptions which are based on the usual technology.

Trying to baffle someone with bull****? Appeal to foggy
confusion? (The "two different" implementations are the
figment of someone's imagination to start with, but it
would make no difference because they are both based on
precisely the same engineering "implementation".)

At least, that's what I was trying to do :-)

You've make a very basic error, and repeated is several
times, in relations to bit depth and dynamic range. As
I've said repeatedly, until *that* part is understood,
the rest of it is going to remain a confused mystery.

Let me repeat, the dynamic range which can be
represented by a linear ADC when an analog signal is
converted to digital data, is *well* *known* to be
represented by the formula:

Dynamic_Range_in_dB = (Bit_Depth * 6.02) + 1.76

You can, as noted, find the derivation of that formula
in almost any good engineering text. You can find the
formula stated in everything from simple tutorials to
the manufacturer's data sheets and application notes for
ADC's. For example, from

http://datasheets.maxim-ic.com/en/ds/MAX1183.pdf


"Signal-to-Noise Ratio (SNR)

For a waveform perfectly reconstructed from digital
samples, the theoretical maximum SNR is the ratio of
the fullscale analog input (RMS value) to the RMS
quantization error (residual error). The ideal,
theoretical minimum analog-to-digital noise is
caused by quantization error only and results directly
from the ADC's resolution (N-Bits):

SNRdB[max] = 6.02 N + 1.76

In reality, there are other noise sources besides
quantization noise (thermal noise, reference noise,
clock jitter, etc.). SNR is computed by taking the
ratio of the RMS signal to the RMS noise, which
includes all spectral components minus the
fundamental, the first five harmonics, and the DC
offset.


What you *cannot* find is *anything* which will support
your repeated statements that bit depth does not
restrict the dynamic range. And until you understand why,
all of this is just going to continue to confuse you.

--
Floyd L. Davidson http://www.apaflo.com/floyd_davidson
Ukpeagvik (Barrow, Alaska)

In article ,
(Floyd L. Davidson) wrote:

Kevin McMurtrie wrote:
In article ,
(Floyd L. Davidson) wrote:

To you perhaps. But what you said was:

"The important point remains that the number
of bits in the encoding doesn't affect the
dynamic range between darkest and lightest,
just the number of steps in between."

And that, as virtually *any* tutorial or text on the
subject will explain the math for you, is wrong.

The standard formula is:

Dynamic_Range_in_dB = (n * 6.02) + 1.72

That formula calculates the theoretical dynamic range for a single
sample of data.

That is not correct. It calculates the dynamic range of
the resulting digital data set, not from 1 sample but
over the average of many (i.e., all possible) samples.
The quantization distortion is determined by the
integral of the error signal spread across the entire
range of each step (as opposed to one sample that has
only a single value of error).

It is not correct to apply this to a set of data, such
as an audio signal, an image, or a video signal.

As *any* half decent tutorial or text on the subject will
explain, that is *exactly* what it applies to. Here are
three examples, two books on audio signals and one URL
that discusses digital imaging.

"Digital Telephony", 3rd Ed., John C. Bellamy, 2000,
published by John Wiley & Sons. pp. 99-101

The above text shows the mathematical derivation, as well
as examples of its use.

"Telecommunications System Engineering", 3rd Ed.,
Roger L. Freeman, 1996, published by John Wiley & Sons.
p 353.

This text does not show the derivation, but states the formula
and give examples.

http://www.cis.rit.edu/class/simg712...antization.pdf

See pages 12 and 13.

Excellent. Please read the rest of it.


It doesn't make any
more sense than it would to say that a camera's performance is pi*r^2 of
the front lens.
...
Digital encoding wil *never* make sense until the reason
for that limit is clearly understood.

That is still a very valid statement, obviously.

Until people understand the theory involved in digitizing
analog data with a linear ADC... the rest of this discussion
is meaningless wandering in the dark.

--
I don't read Google's spam. Reply with another service.

Kevin McMurtrie wrote:
(Floyd L. Davidson) wrote:
Kevin McMurtrie wrote:
(Floyd L. Davidson) wrote:

The standard formula is:

Dynamic_Range_in_dB = (n * 6.02) + 1.72

That formula calculates the theoretical dynamic range for a single
sample of data.

That is not correct. It calculates the dynamic range of
the resulting digital data set, not from 1 sample but
....

It is not correct to apply this to a set of data, such
as an audio signal, an image, or a video signal.

As *any* half decent tutorial or text on the subject will
explain, that is *exactly* what it applies to. Here are
....

This text does not show the derivation, but states the formula
and give examples.

http://www.cis.rit.edu/class/simg712...antization.pdf

See pages 12 and 13.

Excellent. Please read the rest of it.

It says that virtually everything you stated is not
true, as noted in the quoted text above. It is *not*
from 1 sample. It is used for audio data, image data,
and video signals.

And I'll grant that you are probably confusing the
issue with dithering... :-)

--
Floyd L. Davidson http://www.apaflo.com/floyd_davidson
Ukpeagvik (Barrow, Alaska)

Floyd L. Davidson wrote:

What you *cannot* find is *anything* which will support
your repeated statements that bit depth does not
restrict the dynamic range. And until you understand why,
all of this is just going to continue to confuse you.

It doesn't confuse me at all. You've given a number of reasons why bit
depth has to restrict dynamic range because the current accepted
relationships between bit depth and dynamic range can't be changed
without making other important things a lot worse. I don't disagree
with any of that. What I'm pointing out is that it's the optimal point
in an engineering compromise, not a fundamental scientific
principle. It's the difference between practical and theoretical
possibilities.

If the technology changes, the optimal compromise might change.

--
Chris Malcolm DoD #205
IPAB, Informatics, JCMB, King's Buildings, Edinburgh, EH9 3JZ, UK
[http://www.dai.ed.ac.uk/homes/cam/]