Tedd,
Sorry for the floppy language. You are quite correct, the name is
discrete cosine. I get too relaxed sometimes!
As to the visual impact of a degree of compression, I don't think that
you can automate this. The issue surrounds the way the brain processes
information. When you see something, you brain processes the visual
field and looks for patters that it recognizes and then your conscious
mind becomes aware of the patterns, not actually the thing you are
looking at. Optical illusions can illustrate this point. For example
where you see a bunch of blobs on a white background and then someone
tells you it is a dog and you see the dog. Once you see the dog you can
no longer 'not see it'. This is because of the way the brain processes
patterns.
The trick to DCT is that in most 'organic' images - people, trees etc -
the patterns for which your brain is looking actually occupy low
frequencies. However, the majority of the information which is encoded
into the image is in high frequencies. Consequently, by selectively
removing the high frequencies, the image appears to the conscious mind
to be the same whilst in reality it is degraded.
The snag come when the pattern your brain is looking to match to
requires high frequencies. The classic is a edge. If one has an
infinitely large white background with a single infinitely sharp line on
it, you require infinite frequencies to encode it correctly (ten years
ago I knew the proof for this, time and good wine has put a stop to
that). This is much like the side band problem in radio transmission.
If you encode an image in dimensional space rather than in frequency
space you don't get this problem (hence PNG permitting perfectly sharp
lines).
So - back on topic. If you take an image with sharp lines in it, then
pass it through DCT twice (the process in symmetrical) but loose some of
the high frequency data in the process (compression) then the result is
that the very high frequency components that encode the edge are
stripped off. Rather than (as one might like) this making the edge
fussy, it produces what is called mosquito noise around the edges.
Because mosquito noise is nothing like what you are 'expecting' to see,
the brain is very sensitive to it.
Thus, the amount you notice the compression of JPEG depends on the
nature of the image you compress.
Now it gets nasty. DCT scales as a power of n (where n is the size of
image) - there is a fast DCT process like the F in FFT. But it is still
non linear. This means that to make the encoding and decoding of JPEG
reasonably quick the image is split into blocks and each block is
separately passed through the DCT process. This is fine except that it
produces errors from one block to the next as to where the edges are in
HSV space. Thus, as the compression is turned up, the edges of the
block can become visible due to discontinuities in the color, huge and
saturation at the borders. This again is sensitive to the sort of image
you are compressing. For example, if it has a very flat (say black or
white) background, then you will not notice. Alternatively, if the image
is tonally rich, like someone's face, you will notice it a lot.
Again, this effect means that it is not really possible to automate the
process of figuring out what compression setting is optimum.
As for PNG: As far as I know, the only issue with any realistic browser
(other than very old ones like IE2 or something) is that the alpha
channel is not supported. As there is no alpha channel in JPEG, so
there is no difference. Though I do not profess to be absolutely sure
that all browsers you might encounter manage PNG ok.
Side Issues:
DCT is integer. This means that if you have zero compression in the DCT
process, then you get out what you put in (except if you get overflow,
which can be avoided as far as I know). This is not the case in FFT
where floating point errors mean you always loose something. Thus
JPEG/100% should be at or near perfect (lossless) but does not actually
compress.
Another area where FFT and DCT become very interesting is in moving
picture processing. You can filter video using FFT or DCT in ways that
are hard or impossible using spacing filters. This can be good for
improving noisy or fussy 'avi' files etc.
Best wishes
AJ
PS - I'll stick the above on my nerd blog nerds-central.blogspot.com, if
you have any good links to suggest to expand the subject, please let me
know and I shall add them.
tedd wrote:
Alex:
Excuse for top posting:
You said: Clear as mud?
Well actually, it's simperer than I thought. After your reply, I did
some reading on jpeg and found it's simply a transform, not unlike FFT
where two-dimensional temporal data is transformed from the time domain
to the frequency domain -- very interesting reading.
The reverse cosine matrix you mention is probably the discrete cosine
transform (DCT) matrix where the x, y pixels of an image file have a z
component representing color. From that you can translate the data into
the frequency domain, which actually generates more data than the original.
However, the quality setting is where you make it back up in compression
ratio's by trimming off higher frequencies which don't add much to the
data. Unlike the FFT, the algorithm does not address phasing, which I
found interesting.
However, the answer to my question deals with the quality statement. In
the statement:
imagejpeg($image_p, null, 100);
I should have used something less than 100.
I've change the figure to 25 and don't see any noticeable difference in
quality of the thumbnail.
It seems to me there should be a table (or algorithm) somewhere that
would recommend what quality to use when reducing the size of an image
via this method. In this case, I reduced an image 62 percent (38% of the
original) with a quality setting of 25 and "see" no difference. I think
this (the quality factor) is programmable.
As for png images, I would probably agree (if I saw comparisons), but
not all browsers accept them. I belive that at least one IE has problems
with png's, right?
tedd
At 4:45 PM +0100 8/23/06, Alex Turner wrote:
M Sokolewice got it nearly correct. However, the situation is a
little more complex than he has discussed.
The % compression figure for jpeg is translated into the amount of
information stored in the reverse cosine matrix. The size of the
compressed file is not proportional to the % you set in the
compressor. Thus 100% actually means store all the information in the
reverse cosine matrix. This is like storing the image in a 24 bit
png, but with the compressor turned off. So at 100% jpeg is quite
inefficient.
The other issue is the amount of high frequency information in your
images. If you have a 2000x2000 image with most of the image dynamics
at a 10 pixel frequency, and you reduce this to 200x200 then the JPEG
compression algorithm will 'see' approximately the same amount of
information in the image :-( The reality is not quite as simple as
this because of the way JPEG uses blocks etc, but it is an easy way of
thinking about it.
What all this means is that as you reduce the size of an image, if you
want it to retain some of the detail of the original but at a smaller
size, there will be a point at which 8 or 24 bit PNG will become a
better bet.
Clear as mud?
AJ
M. Sokolewicz wrote:
I'm not quite sure, but consider the following:
Considering the fact that most JPEG images are stored with some form
of compression usually ~75% that would mean the original image, in
actual size, is about 1.33x bigger than it appears in filesize. When
you make a thumbnail, you limit the amount of pixels, but you are
setting compression to 100% (besides that, you also use a truecolor
pallete which adds to its size). So, for images which are scaled down
less than 25% (actually this will prob. be more around 30-ish, due to
palette differences) you'll actually see the thumbnail being bigger
in *filesize* than the original (though smaller in memory-size)
- tul
P.S. isn't error_reporting( FATAL | ERROR | WARNING ); supposed to be
error_reporting( E_FATAL | E_ERROR | E_WARNING ); ??
tedd wrote:
Hi gang:
I have a thumbnail script, which does what it is supposed to do.
However, the thumbnail image generated is larger than the original
image, how can that be?
Here's the script working:
http://xn--ovg.com/thickbox
And, here's the script:
<?php /* thumb from file */
/* some settings */
ignore_user_abort();
set_time_limit( 0 );
error_reporting( FATAL | ERROR | WARNING );
/* security check */
ini_set( 'register_globals', '0' );
/* start buffered output */
ob_start();
/* some checks */
if ( ! isset( $_GET['s'] ) ) die( 'Source image not specified' );
$filename = $_GET['s'];
// Set a maximum height and width
$width = 200;
$height = 200;
// Get new dimensions
list($width_orig, $height_orig) = getimagesize($filename);
if ($width && ($width_orig < $height_orig))
{
$width = ($height / $height_orig) * $width_orig;
}
else
{
$height = ($width / $width_orig) * $height_orig;
}
// Resample
$image_p = imagecreatetruecolor($width, $height);
$image = imagecreatefromjpeg($filename);
imagecopyresampled($image_p, $image, 0, 0, 0, 0, $width, $height,
$width_orig, $height_orig);
// Output & Content type
header('Content-type: image/jpeg');
imagejpeg($image_p, null, 100);
/* end buffered output */
ob_end_flush();
?>
---
Thanks in advance for any comments, suggestions or answers.
tedd
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