Re: [PHP] Re: Why small > big?
> [SNIP] > Considering in this thread where I left the quality at 100% and > reduced the image to less 40 percent of the original, and the end > result was that I actually made a larger file. So, I belive that at > least this example shows that 100% is not a good quality value > setting for reducing images -- thus we know the high end is less than > 100. I know from experience that reducing the quality from 100% to 95% sometimes reduces the file size to 50%; without showing much noticeable change in quality... HTH -- PHP General Mailing List (http://www.php.net/) To unsubscribe, visit: http://www.php.net/unsub.php
Re: [PHP] Re: Why small > big?
> [SNIP] > 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. I personally use PNG all the time for smaller images. Only for high color, larger images, I use JPEG. Besides the unsupported Alpha transparency that you've already mentioned, I've never had any form of problem (or heard anyone complain) about unsupported PNG images. And that's indexed and RGB PNGs. -- PHP General Mailing List (http://www.php.net/) To unsubscribe, visit: http://www.php.net/unsub.php
Re: [PHP] Re: Why small > big?
As I promised, here is the writeup with examples:
http://nerds-central.blogspot.com/2006/08/choosing-file-format-for-small-web.html
Cheers
AJ
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:
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
--
PHP General Mailing List (http://www.php.net/)
To unsubscribe, visit: http://www.php.net/unsub.php
--
www.deployview.com
www.nerds-central.com
www.project-network.com
--
PHP General Mailing List (http://www.php.net/)
To unsubscribe, visit: http://www.php.net/unsub.php
Re: [PHP] Re: Why small > big?
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 t
