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Hello community,

I am thinking about implementing a real-time compression scheme on an FPGA
working at about 500 Mhz. Facing the fact that there is no "universal
compression" algorithm that can compress data regardless of its structure
and statistics I assume compressing grayscale image data. The image data is
delivered line-wise, meaning that one horizontal line is processed, than
the next one, a.s.o.
Because of the high data rate I cannot spend much time on DFT or DCT and on
data modelling. What I am looking for is a way to compress the pixel data in
spatial not spectral domain because of latency aspects, processing
complexity, etc. Because of the sequential data transmission line by line a
block matching is also not possible in my opinion. The compression ratio is
not so important, factor 2:1 would be sufficient. What really matters is the
real time capability. The algorithm should be pipelineable and fast. The
memory requirements should not exceed 1 kb.
What "standard" compression schemes would you recommend? Are there
potentialities for a non-standard "own solution"?

Thank you for your comments.

Regards, Melanie

"Brannon" <brannonking@yahoo.com> wrote in message
news:1135097484.768840.95060@g44g2000cwa.googlegroups.com...

JPEGs are lossy because of the quantization step. You can do it without
the quantization step and still notice a significant compression. If
you preload your quantization constants and Huffman codes into lookup
tables, you can easily process one pixel per clock cycle in a 1500 gate
FPGA. I wrote a fully piplelined version that queued up the first eight
rows of an incoming image into block ram before starting on the DCTs.
It worked great. Ideally you would do DCTs on blocks larger than 8x8,
but the advantage to 8x8 is that you can easily do 64 8bit operations
in parallel which is nice for the Z-ordering, etc. Bigger squares
require bigger chips and external memory, and as soon as you have to go
to external memory you lose your pipelining.

Is the OP not getting pixels in raster-scan order though?
That, and a half-line memory limit means there's not enough storage
for a 2-d DCT.

"Melanie Nasic" <quinn_the_esquimo@freenet.de> wrote in message
news:do95r4$4hh$1@mamenchi.zrz.TU-Berlin.DE...

Hi Pete,

I want the compression to be lossless and not based on perceptional
irrelevancy reductions. By stating 1 kb I meant 1024 bits and that's just
about half the line data. Your recommendation "1d predictor, non-linear
quantizer and entropy coder" sound interesting. COuld you please elaborate
on that? How is it done? Where can I find some exemplary codes? How can it
be achieved with hardware (VHDL sources?)

I think you first need to play around with software and a few sample images.
The 1 d predictor means that you predict the next pixel in the sequence
by examining pixels on the left. A simple example would be to encode the
first pixel on the line, then use that as the prediction for the next pixel.
In that way you send only the difference between the predicted value
and what the pixel actually is. If you had enough memory to store a line you
could use a 2 d predictor, where you predict from pixels to the left and
pixels above.

Unfortunately, you can't use the non-linear quantizer as it's lossy.

I find Khalid Sayood's book "Introduction to Data Compression" quite good.
It comes with a link to a bunch of simple C code that has a variety of
predictors and entropy coders. You could try it on some sample images,
see how good compression you get, then go to hardware when you have
something acceptable.

JPEGs are lossy because of the quantization step. You can do it without
the quantization step and still notice a significant compression. If
you preload your quantization constants and Huffman codes into lookup
tables, you can easily process one pixel per clock cycle in a 1500 gate
FPGA. I wrote a fully piplelined version that queued up the first eight
rows of an incoming image into block ram before starting on the DCTs.
It worked great. Ideally you would do DCTs on blocks larger than 8x8,
but the advantage to 8x8 is that you can easily do 64 8bit operations
in parallel which is nice for the Z-ordering, etc. Bigger squares
require bigger chips and external memory, and as soon as you have to go
to external memory you lose your pipelining.

You don't want to do a dictionary method for an image. In fact, I'm not
sure you want to do a dictionary method in FPGA. That sounds scary to
me. Use frequency domain (DCT or Wavelet) Z-ordering for photos and use
raw RLE for screen shots and similar images.

I attended Altera's DSP showcase/seminar in Rochester, there was a
large presence for interest in implementing MPEG4 (section 10 or 11, I
can't remember) for HDTV applications. You didn't say if the advice
you are searching for is for a commercial product, R&D science project
or a student project but even implementing something real time for DVD
quality (720x480) is worth considering.

I think a while back somebody announced the availability of JPEG
library on the www.opencores.org,
http://www.opencores.org/projects.cgi/web/jpeg/overview I haven't
tried it but critiquing it could be a good place to start. You could
incorporate parts of its implementation into yours, omit the parts you
don't agree with, and foster new not present in it. There is also a
section Video Compress Systems,
http://www.opencores.org/projects.cgi/w ... s/overview that
would be worth taking a look at.

A slightly different approach is using a tool like ImpulseC
(www.impulsec.com). It isn't really C but it is close. It allows you
to efficiently manage parallel systems of functions and integrate them
into VHDL and Verilog designs.

Maybe it is the bubble I have been living in, but your clock speed
seems high, what device families are you considering? I have seen 80
Mhz designs outperform similar applications on gigahertz PC. Don't
let PC marketing skew your objectivity (or maybe it is there choice in
operating systems).

Could you tell us more about the purpose of your project and you end
application?

Derek

Melanie Nasic wrote:

Hello community,

...
What "standard" compression schemes would you recommend? Are there
potentialities for a non-standard "own solution"?

I would think of something like:
- run length encoding
- arithmetic coding
- maybe a dictionary encoding like zip
- if you know some statistics about the values you want to compress you could
also try if huffman coding is sufficent

Regards
Stefan

Thank you for your comments.

Regards, Melanie

"Melanie Nasic" <quinn_the_esquimo@freenet.de> writes:

Because of the high data rate I cannot spend much time on DFT or DCT
and on data modelling. What I am looking for is a way to compress
the pixel data in spatial not spectral domain because of latency
aspects, processing complexity, etc. Because of the sequential data
transmission line by line a block matching is also not possible in
my opinion. The compression ratio is not so important, factor 2:1
would be sufficient. What really matters is the real time
capability. The algorithm should be pipelineable and fast. The
memory requirements should not exceed 1 kb.

You don't say anything about quality.

Here's C code for a lossy compressor / decompressor which consistently
achieves a 2:1 ratio for 8 bpp grayscale images:

#include <stdint.h>
#include <stdio.h>

int
compress(FILE *fin, FILE *fout)
{
uint8_t pin[2], pout;

for (;;) {
if (fread(&pin, sizeof pin, 1, fin) != 1)
return (ferror(fin) ? -1 : 0);
pout = (pin[0] + pin[1]) / 2;
if (fwrite(&pout, sizeof pout, 1, fout) != 1)
return -1;
}
}

int
decompress(FILE *fin, FILE *fout)
{
uint8_t pin, pout[2];

for (;;) {
if (fread(&pin, sizeof pin, 1, fin) != 1)
return (ferror(fin) ? -1 : 0);
pout[0] = pout[1] = pin;
if (fwrite(&pout, sizeof pout, 1, fout) != 1)
return -1;
}
}

(note that the code assumes that the size of the input stream is an
even number)

DES
--
Dag-Erling Smørgrav - des@des.no

"Melanie Nasic" <quinn_the_esquimo@freenet.de> wrote in message
news:do9206$1m4$1@mamenchi.zrz.TU-Berlin.DE...

Hello community,

I am thinking about implementing a real-time compression scheme on an FPGA
working at about 500 Mhz. Facing the fact that there is no "universal
compression" algorithm that can compress data regardless of its structure
and statistics I assume compressing grayscale image data. The image data
is delivered line-wise, meaning that one horizontal line is processed,
than the next one, a.s.o.
Because of the high data rate I cannot spend much time on DFT or DCT and
on data modelling. What I am looking for is a way to compress the pixel
data in spatial not spectral domain because of latency aspects, processing
complexity, etc. Because of the sequential data transmission line by line
a block matching is also not possible in my opinion. The compression ratio
is not so important, factor 2:1 would be sufficient. What really matters
is the real time capability. The algorithm should be pipelineable and
fast. The memory requirements should not exceed 1 kb.
What "standard" compression schemes would you recommend? Are there
potentialities for a non-standard "own solution"?

Thank you for your comments.

Regards, Melanie

The answer, as always, is it all depends ...

Lossless compression (something like run length encoding) might work for
some kinds of image data (computer screens, rendered images), but will fail
for others (natural images etc).

Lossy compression will of course lose something from the image. The simplest
form is probably to average two adjacent pixels, giving you 2 to 1. I
suspect that anything more complex will exceed your space/speed/complexity
budget.

You need to spell out what type of images you are processing, what level of
'loss' is acceptable and why you need compression anyway (it will cause you
much pain !)

Dave

"Melanie Nasic" <quinn_the_esquimo@freenet.de> wrote in message
news:do9206$1m4$1@mamenchi.zrz.TU-Berlin.DE...

Hello community,

I am thinking about implementing a real-time compression scheme on an FPGA
working at about 500 Mhz. Facing the fact that there is no "universal
compression" algorithm that can compress data regardless of its structure
and statistics I assume compressing grayscale image data. The image data
is delivered line-wise, meaning that one horizontal line is processed,
than the next one, a.s.o.
Because of the high data rate I cannot spend much time on DFT or DCT and
on data modelling. What I am looking for is a way to compress the pixel
data in spatial not spectral domain because of latency aspects, processing
complexity, etc.

Are you hoping for lossless, or is lossy OK?

Because of the sequential data transmission line by line a block matching
is also not possible in my opinion. The compression ratio is not so
important, factor 2:1 would be sufficient. What really matters is the real
time capability. The algorithm should be pipelineable and fast. The memory
requirements should not exceed 1 kb.

That's 1024 bits, or bytes?
Is it enough for one line?
You don't say what your resolution and frame rate are.

What "standard" compression schemes would you recommend? Are there
potentialities for a non-standard "own solution"?

If you don't have a line of storage available, that restricts you a lot.
I don't understand this though. If you're using a 500MHz FPGA, it's
presumably recent, and presumably has a decent amount of storage.

How about a 1d predictor, non-linear quantizer and entropy coder?
If you have more memory available, look at JPEG-LS.
It can do lossless, or variable mild degrees of loss.

Thank you for your comments.

Regards, Melanie

Hi Pete,

I want the compression to be lossless and not based on perceptional
irrelevancy reductions. By stating 1 kb I meant 1024 bits and that's just
about half the line data. Your recommendation "1d predictor, non-linear
quantizer and entropy coder" sound interesting. COuld you please elaborate
on that? How is it done? Where can I find some exemplary codes? How can it
be achieved with hardware (VHDL sources?)

Thank you a lot.

Bye, Mel.



"Pete Fraser" <pfraser@covad.net> schrieb im Newsbeitrag
news:11qg5v3q9plph0a@news.supernews.com...

"Melanie Nasic" <quinn_the_esquimo@freenet.de> wrote in message
news:do9206$1m4$1@mamenchi.zrz.TU-Berlin.DE...
Hello community,

I am thinking about implementing a real-time compression scheme on an
FPGA working at about 500 Mhz. Facing the fact that there is no
"universal compression" algorithm that can compress data regardless of
its structure and statistics I assume compressing grayscale image data.
The image data is delivered line-wise, meaning that one horizontal line
is processed, than the next one, a.s.o.
Because of the high data rate I cannot spend much time on DFT or DCT and
on data modelling. What I am looking for is a way to compress the pixel
data in spatial not spectral domain because of latency aspects,
processing complexity, etc.

Are you hoping for lossless, or is lossy OK?

Because of the sequential data transmission line by line a block matching
is also not possible in my opinion. The compression ratio is not so
important, factor 2:1 would be sufficient. What really matters is the
real time capability. The algorithm should be pipelineable and fast. The
memory requirements should not exceed 1 kb.

That's 1024 bits, or bytes?
Is it enough for one line?
You don't say what your resolution and frame rate are.

What "standard" compression schemes would you recommend? Are there
potentialities for a non-standard "own solution"?

If you don't have a line of storage available, that restricts you a lot.
I don't understand this though. If you're using a 500MHz FPGA, it's
presumably recent, and presumably has a decent amount of storage.

How about a 1d predictor, non-linear quantizer and entropy coder?
If you have more memory available, look at JPEG-LS.
It can do lossless, or variable mild degrees of loss.

Thank you for your comments.

Regards, Melanie

Stefan,
The huffman coding sounds interesting. From what I remember, most real
image data is spatially correlated. That would lead me to guess that
the most likely difference between two adjacent pixel values is zero,
then one, etc. This would seem to be suitable to use huffman coding
on, by just coding the difference It would be interesting to run
sample scenes through the filter. It is not obvious that a lossless
2:1 reduction can be guaranteed because one could synthesize a scene
that would not compress. Also, the transformed image may not be robust
in the presence of noise. Just my two cents.

-Newman

In comp.arch.fpga Melanie Nasic <quinn_the_esquimo@freenet.de> wrote:
: I want the compression to be lossless and not based on perceptional
: irrelevancy reductions.

If it has to be lossless there's no way you can guarantee to
get 2:1 compression (or indeed any compression at all!). You
may do, with certain kinds of input, but it's all down to the
statistics of the data. The smaller your storage the less
you can benefit from statistical variation across the image,
and 1 Kbyte is very small!

Given that a lossless system is inevitably 'variable bit rate'
(VBR) the concept of "real time capability" is somewhat vague;
the latency is bound to be variable. In real-world applications
the output bit-rate is often constrained so a guaranteed minimum
degree of compression must be achieved; such systems cannot be
(always) lossless.

From my experience I would say you will need at least a 4-line
buffer to get near to 2:1 compression on a wide range of input
material. For a constant-bit-rate (CBR) system based on a 4x4
integer transform see:

http://www.bbc.co.uk/rd/pubs/whp/whp119.shtml

This is designed for ease of hardware implementation rather than
ultimate performance, and is necessarily lossy.

Richard.
http://www.rtrussell.co.uk/
To reply by email change 'news' to my forename.

If you can store a buffer of at least one extra scanline, you could try the
Paeth predictor + RLE. This will give reasonable prediction of the next
pixel's grayscale value, and if the prediction is OK, the result will often
contain a string of zeroes, and the RLE will do a good job.

If you can "afford it" (in other words, the FPGA is fast enough), you could
use arithmetic coding on the resulting predicted values, with a simple order
0 model, instead of RLE.

Paeth + RLE will do OK on computer generated images, but not on natural
images. Paeth + AC will do OK on both.

Both will fit in 1kb of code for sure.

Nils

On 20/12/2005 the venerable Melanie Nasic etched in runes:

Hello community,

I am thinking about implementing a real-time compression scheme on an
FPGA working at about 500 Mhz. Facing the fact that there is no
"universal compression" algorithm that can compress data regardless
of its structure and statistics I assume compressing grayscale image
data. The image data is delivered line-wise, meaning that one
horizontal line is processed, than the next one, a.s.o. Because of
the high data rate I cannot spend much time on DFT or DCT and on data
modelling. What I am looking for is a way to compress the pixel data
in spatial not spectral domain because of latency aspects, processing
complexity, etc. Because of the sequential data transmission line by
line a block matching is also not possible in my opinion. The
compression ratio is not so important, factor 2:1 would be
sufficient. What really matters is the real time capability. The
algorithm should be pipelineable and fast. The memory requirements
should not exceed 1 kb. What "standard" compression schemes would
you recommend? Are there potentialities for a non-standard "own
solution"?

Thank you for your comments.

Regards, Melanie

Have a look at Graphics File Formats by Kay & Levine (ISBN
0-07-034025-0). It will give you some ideas.

--
John B

Melanie Nasic wrote:

Hello community,

I am thinking about implementing a real-time compression scheme on an FPGA
working at about 500 Mhz. Facing the fact that there is no "universal
compression" algorithm that can compress data regardless of its structure
and statistics I assume compressing grayscale image data. The image data is
delivered line-wise, meaning that one horizontal line is processed, than
the next one, a.s.o.
Because of the high data rate I cannot spend much time on DFT or DCT and on
data modelling. What I am looking for is a way to compress the pixel data in
spatial not spectral domain because of latency aspects, processing
complexity, etc. Because of the sequential data transmission line by line a
block matching is also not possible in my opinion. The compression ratio is
not so important, factor 2:1 would be sufficient. What really matters is the
real time capability. The algorithm should be pipelineable and fast. The
memory requirements should not exceed 1 kb.
What "standard" compression schemes would you recommend?

Though it's only rarely used, there's a lossless version of JPEG
encoding. It's almost completely different from normal JPEG encoding.
This can be done within your constraints, but would be improved if you
can relax them minutely. Instead of only ever using the current scan
line, you can improve things if you're willing to place the limit at
only ever storing one scan line. The difference is that when you're in
the middle of a scan line (for example) you're storing the second half
of the previous scan line, and the first half of the current scan line,
rather than having half of the buffer sitting empty. If you're storing
the data in normal RAM, this makes little real difference -- the data
from the previous scan line will remain in memory until you overwrite
it, so it's only really a question of whether you use it or ignore it.

Are there potentialities for a non-standard "own solution"?

Yes. In the JPEG 2000 standard, they added JPEG LS, which is another
lossless encoder. A full-blown JPEG LS encoder needs to store roughly
two full scan lines if memory serves, which is outside your
constraints. Nonetheless, if you're not worried about following the
standard, you could create more or less a hybrid between lossless JPEG
and JPEG LS, that would incorporate some advantages of the latter
without the increased storage requirements.

I suspect you could improve the prediction a bit as well. In essence,
you're creating a (rather crude) low-pass filter by averaging a number
of pixels together. That's equivalent to a FIR with all the
coefficients set to one. I haven't put it to the test, but I'd guess
that by turning it into a full-blown FIR with carefully selected
coefficients (and possibly using more of the data you have in the
buffer anyway) you could probably improve the predictions. Better
predictions mean smaller errors, and tighter compression.