Fractals and Benoit
Mandelbrot
A Computer Science Discovery
often considered kin to Prime Numbers.
Fractals are a very important discovery of an astute
mathematician at IBM, one Benoit Mandelbrot.
In 1977, Mandelbrot
found that certain mathematical formula can define a
shape which when displayed, appears to contain
smaller representations of itself similar in nature
(geometry and shape) within itself. His analysis of
why this occurred lead to a
series of calculations that allow images to be
created that have virtually infinite selfdefinition, the more
you zoom in on them, the more detail is exposed
bearing strong
similarity
to the image that contains them.
Fractals themselves, are visually
stimulating to look at. The picture to the right is
called a "Mandelbrot".
However, their ability to carry or modify data is
extremely useful
to computer science in encoding, decoding, and
ciphers. For example, by using
Fractal Geometry Mathematics, one can reduce a
fairly complex image shape down to a very small,
compressed object which if later is decompressed,
sufficiently resembles the original shape, that it
is recognizable by the human mind with considerable
accuracy.
If that process is
done to a series of still images in a video stream,
when recomposed and played back in the same
sequences as the original video, will resemble the
original video they were composed of sufficiently, to allow
the human mind to grasp the content despite the
distortion of the image that ultimately takes place
during the process.
One company, Fractal Geometries, in the mid 90's,
teamed up with what today is known as Real Networks,
to adapt this technology so as to create video that
could play back along a single 33.6 to 56K bit per
second dial up stream, when such were popular on the
Internet, and DSL and Cable were not yet caught on.
While the playback was not very high quality when
viewed at the low speeds of a 56K or slower modem
line, combining it with progressive scan compression and motion
detection compression, a realistic representation of an original
60 FPS NTSV Television Video was able to be
transmitted at approximately 53KBPS to a user of
Real Player, admittedly at about half speed and poor
quality (but not much worse than early TV back in
the 30's and 40's).
Early Bell Laboratories studies were
able to transmit video at 1MHz across three pairs of
copper wire, however it required over 3 seconds to
accumulate a single still frame at a time. Later
studies in the Video Compression Lab at AT&T Bell Labs
in Crawford Corners, NJ, found that using two 56K (128KBPS) Digital ISDN
lines, a video signal could be sent at 512.5 frames
per second, using the early Bell Picturephone type
device as a
source as well as other video devices and advanced
data compression technology. Not having this 128K
Digital throughput in
the average 4853KBPS Trellis Coded LZW compressed Dial
Up telephone line to the Internet, the adaptation of fractal
compression (which searches for similarities within
video images) made it possible to watch a somewhat
distorted image at as low as 28KBPS, leading to eventual adoption of
video as a medium that could be watched across the
Internet, even using slow modem driven lines.
The presentation below, a lecture by Benoit Mandelbrot
at MIT, uses an adaptation of Mendelbrot's Fractals to
display MPEG2 Video, particularly visible in the Low
Speed 56K Transmission link.
It was this breakthrough which led
Real Networks into the marketplace with a commercially
viable player for low speed line use when viewing
Television, in the mid 90's. Thus, the competition to dominate the Desktop
Internet Video Marketplace was born, in a
competition between Real Player, Microsoft Media
Player, Apple Quicktime Player and
various other companies.
Discovering this ability in Fractals and incorporating the
method into a
compression algorithm, gave Real an early lead by
enabling snail like dial up telephone lines to play
back digital video which ordinarily might require
440KBPS or faster signals.. But, as in all things,
Mandelbrot's discovery was not purely an invention, just an
observation based on the prior work of someone in
the uses of fractal
mathematics. That exercise, of all things, was
strongly brought to bear by Code Breakers trying
to decode messages, used by the early Decoders group
at the famed Cryptology Lab at Bletchley Park in England during WWII.
Scientists who migrated to America from Germany
after the war who came to work for IBM, stumped by
how Allies had managed to decode German radio
transmissions successfully, leading to Nazi
Germany's defeat, had commissioned studies at IBM in
its Yorktown Heights research center to examine
various methods for encoding, decoding, encrypting
and decrypting information. Byproduct to that
secret study, Benoit Mandelbrot stumbled on to the
use of certain equations such as the series invented by
Gaston Julia, called the "Julia Set" which is based
on the form: z^2 + c, where c is a
complex constant with real and imaginary numbers
in representing shapes. This, after lengthy
experimentation, led to the mathematical exposure of
Fractals at IBM for the very first time since same
was first explored by the Code Breakers in the 1940's.
This occurred decades after it had surfaced in
England for use examining German radio transmissions
looking for details that would escape normal
filtering methods, using the famed British COLOSSUS
computers, invented by Sir Thomas Flowers, the
inventor of the Programmable Electronic Computer.
It is ironic that some have suggested that those who
commissioned the study at IBM were trying to figure
out how it was all done, since methods like that don't
become obsolete, particularly if kept a secret long
enough.
Ironic because during WWII, IBM
was, covertly, Nazi Germany's primary provider of
computation and tabulation technology. Germany was
defeated, in part, by the discoveries made by the
British Mathematicians and Codebreakers at Bletchley
Park and the Colossus Computers invented by Sir
Thomas Flowers that made it possible to perform such
calculations thousands of times faster than the
human mind was able. While the discovery of Fractals
helped illuminate one of the many angles that led to
Nazi Germany's defeat, science today still hasn't
figured out the vast majority of the secrets, which
have been kept a secret since 1941. Some have
breathed a sigh of relief that IBM has not made much
progress in that area.
Was IBM's Mandelbrot the inventor of Fractals?
Answer: No, not really.
No, not exactly. British cartographers
trying to map the English Coastline, noted in early
King's history, that there was a peculiar phenomenon
that occurred as they tried to create more and more
detailed maps for the King. As they charted
the coastline, they noted in their records that the
closer in they zoomed to include more and more
details in minutia on larger maps of each coastal
segment, when they measured the length of the
British coast, it got longer and longer, as a result
of the inclusion of more irregular details.
This phenomenon was unknown to mankind, and became
the object of many mathematical theories of the era.
For, how could the coastline some 5000+ miles long,
measure 8000 miles or even longer, the more they
zoomed in on it?
Unknowingly, British
Mathematicians had discovered one of the key
aspects of a fractal: the more you zoom in on its
detail, the more details emerge. This is
unlike the average photograph, which tends
to loose detail as one zooms in on it, due to its
fixed resolution nature. You can see
an example of this loss of detail by using any of
the Aerial Photo View Mapping systems on the
Internet, that let you zoom in on any address.
The closer you get, the less detail, unlike in the
case of a Fractal, and unlike in the case of the
King's cartographers and their own notation
regarding the byproduct of more detailed, larger
maps.
Later on, the French
mathematician Gaston Julia discovered even more
characteristics regarding what have since become
known as Fractal Mathematical Polynomial Equation
Sets (or Julia and Mandelbrot Sets). He wondered
what a complex polynomial function would look like
visually,
such as the ones named after him (z^2 + c, where c
is a complex constant with real and imaginary
numbers) if recursed upon itself until it achieved a
certain level, if colored and plotted in space.
His idea translated as follows.
One takes the x and y coordinates of a point in
space, and assign them to z as in x + y*i, where i
is the square root of negative one (a nonreal
number). Then: squaring
this number, and add c as a constant. Then
insert the resulting pair of real and imaginary
numbers recursively back into z, performing the
whole equation over and over, again and again.
If you keep doing that until the result is greater
than a certain magnitude number: the number of times
one must execute the formula to get it to leave it's
own "orbit" can be assigned a color. Turn the
point (x, y) that was calculated that assigned color,
unless the resulting coordinates are not yet out of
their own orbit, in which case make it black. The
results: a very visually interesting shape which, as
you expand it, displays more and more details that
resemble the original shape.
Mandelbrot studied Julia Sets and
notes of the King's Cartographers, as a byproduct of
research that was being driven by notes that were
released when Britain declassified Project MKUltra,
at Bletchley Park, in the mid 70's, and even though
only scant bits of information were released to the
public, these two led Mandelbrot to discover the
very interesting effects of Fractals, later more
fully exemplified by Mathematicians and Computer
Graphics Artists all around the world.
For information about Mandelbrot, kindly watch this 1
hour 20 minute lecture:
220K/Second Broadband version:
BENOIT MANDELBROT Lecturing on Self Similarity and
Fractals and Science, Engineering and Finance (roughness
and beauty) at MITWORLD in November 2001
56K (Dial up Version):
http://mfile.akamai.com/12800/rm/mitworld.download.akamai.com/12800/mitwmandelbrot27nov0156k.ram
< Requires REAL PLAYER (which, of course, uses
Mandelbrot's discovery!)
download Real by clicking on
the link above. You will need
to register with REAL NETWORKS
Examples of Fractals.
The Julia Set. Gaston Julia created a very
interesting phenomenon with his unique recursive
polynomial evaluation of real and imaginary numbers.
Here is an interesting video zoom in on a Julia Set,
which continues to expose similarity details as it
drills down inside of the visual.
CLICK HERE TO
VIEW IT >
Spanky. Noel Griffin, on the Canadian
Laboratory of Particle Physics has collected an
enormous library of Fractal Related Images and
trivia.
CLICK HERE TO GO THERE
>
DataCompression. DataCompression has compiled a study
of Fractals and their relationship to different
disciplines in compression of data streams in computer
science.
CLICK HERE TO GO THERE
>
SummerSoft's LavendarC  Data Encryption using
Fractal Mathematics
CLICK
HERE TO GO THERE >
Fractal Merkel Trees as apply to Data Encryption from "RSAC"
CLICK HERE TO GO THERE
>
Fractals and Physics by Volkhard Normeier
CLICK HERE TO GO THERE
>
Fractals used in Video Compression
Acceleration.
Subject: [77] Introduction to Fractal
compression (long)
SOURCE:
http://www.faqs.org/faqs/compressionfaq/part2/
Written by John Kominek <kominek@links.uwaterloo.ca>
Seven things you should know about
Fractal Image Compression
(assuming that you want to know about it).
1. It is a promising new technology, arguably superior to JPEG 
but only with an argument.
2. It is a lossy compression method.
3. The fractals in Fractal Image Compression are Iterated Function
Systems.
4. It is a form of Vector Quantization, one that employs a virtual
codebook.
5. Resolution enhancement is a powerful feature but is not some
magical way of achieving 1000:1 compression.
6. Compression is slow, decompression is fast.
7. The technology is patented.
That's the scoop in condensed form. Now to elaborate,
beginning with a little
background.
A Brief History of Fractal Image Compression

The birth of fractal geometry (or rebirth, rather) is
usually traced to IBM
mathematician Benoit B. Mandelbrot and the 1977
publication of his seminal
book The Fractal Geometry of Nature. The book put forth
a powerful thesis:
traditional geometry with its straight lines and smooth
surfaces does not
resemble the geometry of trees and clouds and mountains.
Fractal geometry,
with its convoluted coastlines and detail ad infinitum,
does.
This insight opened vast possibilities. Computer
scientists, for one, found a
mathematics capable of generating artificial and yet
realistic looking landscapes,
and the trees that sprout from the soil. And
mathematicians had at
their disposal a new world of geometric entities.
It was not long before mathematicians asked if there was
a unity among this
diversity. There is, as John Hutchinson demonstrated in
1981, it is the branch
of mathematics now known as Iterated Function Theory.
Later in the decade
Michael Barnsley, a leading researcher from Georgia
Tech, wrote the popular
book Fractals Everywhere. The book presents the
mathematics of Iterated Functions
Systems (IFS), and proves a result known as the Collage
Theorem. The
Collage Theorem states what an Iterated Function System
must be like in order
to represent an image.
This presented an intriguing possibility. If, in the
forward direction, fractal
mathematics is good for generating natural looking
images, then, in the
reverse direction, could it not serve to compress
images? Going from a given
image to an Iterated Function System that can generate
the original (or at
least closely resemble it), is known as the inverse
problem. This problem
remains unsolved.
Barnsley, however, armed with his Collage Theorem,
thought he had it solved.
He applied for and was granted a software patent and
left academia to found
Iterated Systems Incorporated (US patent 4,941,193. Alan
Sloan is the co
grantee of the patent and cofounder of Iterated
Systems.) Barnsley announced
his success to the world in the January 1988 issue of
BYTE magazine. This
article did not address the inverse problem but it did
exhibit several images
purportedly compressed in excess of 10,000:1. Alas, it
was not a breakthrough.
The images were given suggestive names such as "Black
Forest" and "Monterey
Coast" and "Bolivian Girl" but they were all manually
constructed. Barnsley's
patent has come to be derisively referred to as the
"graduate student algorithm."
Graduate Student Algorithm
o Acquire a graduate student.
o Give the student a picture.
o And a room with a graphics workstation.
o Lock the door.
o Wait until the student has reverse engineered the picture.
o Open the door.
Attempts to automate this process have met little
success. As Barnsley admitted
in 1988: "Complex color images require about 100 hours
each to encode and
30 minutes to decode on the Masscomp [dual processor
workstation]." That's 100
hours with a _person_ guiding the process.
Ironically, it was one of Barnsley's PhD students that
made the graduate
student algorithm obsolete. In March 1988, according to
Barnsley, he arrived
at a modified scheme for representing images called
Partitioned Iterated
Function Systems (PIFS). Barnsley applied for and was
granted a second patent
on an algorithm that can automatically convert an image
into a Partitioned
Iterated Function System, compressing the image in the
process. (US patent
5,065,447. Granted on Nov. 12 1991.) For his PhD thesis,
Arnaud Jacquin imple
mented the algorithm in software, a description of which
appears in his land
mark paper "Image Coding Based on a Fractal Theory of
Iterated Contractive
Image Transformations." The algorithm was not
sophisticated, and not speedy,
but it was fully automatic. This came at price: gone was
the promise of
10,000:1 compression. A 24bit color image could
typically be compressed from
8:1 to 50:1 while still looking "pretty good."
Nonetheless, all contemporary
fractal image compression programs are based upon
Jacquin's paper.
That is not to say there are many fractal compression
programs available.
There are not. Iterated Systems sell the only commercial
compressor/decompressor,
an MSWindows program called "Images Incorporated."
There are also an
increasing number of academic programs being made freely
available. Unfortunately,
these programs are  how should I put it?  of merely
academic quality.
This scarcity has much to do with Iterated Systems'
tight lipped policy about
their compression technology. They do, however, sell a
Windows DLL for programmers.
In conjunction with independent development by
researchers else
where, therefore, fractal compression will gradually
become more pervasive.
Whether it becomes allpervasive remains to be seen.
Historical Highlights:
1977  Benoit Mandelbrot finishes the first edition of The Fractal
Geometry of Nature.
1981  John Hutchinson publishes "Fractals and SelfSimilarity."
1983  Revised edition of The Fractal Geometry of Nature is
published.
1985  Michael Barnsley and Stephen Demko introduce Iterated
Function Theory in
"Iterated Function Systems and the Global
Construction of
Fractals."
1987  Iterated Systems Incorporated is founded.
1988  Barnsley publishes the book Fractals Everywhere.
1990  Barnsley's first patent is granted.
1991  Barnsley's second patent is granted.
1992  Arnaud Jacquin publishes an article that describes the
first
practical fractal
image compression method.
1993  The book Fractal Image Compression by Michael Barnsley and
Lyman
Hurd is published.
 The Iterated Systems' product line
matures.
1994  Put your name here.
On the Inside

The fractals that lurk within fractal image compression
are not those of the
complex plane (Mandelbrot Set, Julia sets), but of
Iterated Function Theory.
When lecturing to lay audiences, the mathematician
HeinzOtto Peitgen introduces
the notion of Iterated Function Systems with the
alluring metaphor of a
Multiple Reduction Copying Machine. A MRCM is imagined
to be a regular copying
machine except that:
1. There are multiple lens arrangements to create multiple overlapping
copies of the original.
2. Each lens arrangement reduces the size of the original.
3. The copier operates in a feedback loop, with the output of one
stage the input to the next. The initial input may be
anything.
The first point is what makes an IFS a system. The third
is what makes it
iterative. As for the second, it is implicitly
understood that the functions
of an Iterated Function Systems are contractive.
An IFS, then, is a set of contractive transformations
that map from a defined
rectangle of the real plane to smaller portions of that
rectangle. Almost
invariably, affine transformations are used. Affine
transformations act to
translate, scale, shear, and rotate points in the plane.
Here is a simple
example:


x

1 


 




 2
3 

 
 


Before
After
Figure 1. IFS for generating Sierpinski's Triangle.
This IFS contains three component transformations (three
separate lens ar
rangements in the MRCM metaphor). Each one shrinks the
original by a factor of
2, and then translates the result to a new location. It
may optionally scale
and shift the luminance values of the rectangle, in a
manner similar to the
contrast and brightness knobs on a TV.
The amazing property of an IFS is that when the set is
evaluated by iteration,
(i.e. when the copy machine is run), a unique image
emerges. This latent image
is called the fixed point or attractor of the IFS. As
guaranteed by a result
known as the Contraction Theorem, it is completely
independent of the initial
image. Two famous examples are Sierpinski's Triangle and
Barnsley's Fern.
Because these IFSs are contractive, selfsimilar detail
is created at every
resolution down to the infinitesimal. That is why the
images are fractal.
The promise of using fractals for image encoding rests
on two suppositions: 1.
many natural scenes possess this detail within detail
structure (e.g. clouds),
and 2. an IFS can be found that generates a close
approximation of a scene
using only a few transformations. Barnsley's fern, for
example, needs but
four. Because only a few numbers are required to
describe each transformation,
an image can be represented very compactly. Given an
image to encode, finding
the optimal IFS from all those possible is known as the
inverse problem.
The inverse problem  as mentioned above  remains
unsolved. Even if it
were, it may be to no avail. Everyday scenes are very
diverse in subject
matter; on whole, they do not obey fractal geometry.
Real ferns do not branch
down to infinity. They are distorted, discolored,
perforated and torn. And the
ground on which they grow looks very much different.
To capture the diversity of real images, then,
Partitioned IFSs are employed.
In a PIFS, the transformations do not map from the whole
image to the parts,
but from larger parts to smaller parts. An image may
vary qualitatively from
one area to the next (e.g. clouds then sky then clouds
again). A PIFS relates
those areas of the original image that are similar in
appearance. Using Jac
quin's notation, the big areas are called domain blocks
and the small areas
are called range blocks. It is necessary that every
pixel of the original
image belong to (at least) one range block. The pattern
of range blocks is
called the partitioning of an image.
Because this system of mappings is still contractive,
when iterated it will
quickly converge to its latent fixed point image.
Constructing a PIFS amounts
to pairing each range block to the domain block that it
most closely resembles
under some tobedetermined affine transformation. Done
properly, the PIFS
encoding of an image will be much smaller than the
original, while still
resembling it closely.
Therefore, a fractal compressed image is an encoding
that describes:
1. The grid partitioning (the range blocks).
2. The affine transforms (one per range block).
The decompression process begins with a flat gray
background. Then the set of
transformations is repeatedly applied. After about four
iterations the attractor
stabilizes. The result will not (usually) be an exact
replica of the original, but reasonably close.
Scalelessness and Resolution Enhancement

When an image is captured by an acquisition device, such
as a camera or scanner,
it acquires a scale determined by the sampling
resolution of that device.
If software is used to zoom in on the image, beyond a
certain point you don't
see additional detail, just bigger pixels.
A fractal image is different. Because the affine
transformations are spatially
contractive, detail is created at finer and finer
resolutions with each iteration.
In the limit, selfsimilar detail is created at all
levels of resolution,
down the infinitesimal. Because there is no level that
'bottoms out'
fractal images are considered to be scaleless.
What this means in practice is that as you zoom in on a
fractal image, it will
still look 'as it should' without the staircase effect
of pixel replication.
The significance of this is cause of some misconception,
so here is the right
spot for a public service announcement.
/ READER BEWARE \
Iterated Systems is fond of the following argument. Take
a portrait that is,
let us say, a grayscale image 250x250 pixels in size, 1
byte per pixel. You
run it through their software and get a 2500 byte file
(compression ratio =
25:1). Now zoom in on the person's hair at 4x
magnification. What do you see?
A texture that still looks like hair. Well then, it's as
if you had an image
1000x1000 pixels in size. So your _effective_
compression ratio is 25x16=400.
But there is a catch. Detail has not been retained, but
generated. With a
little luck it will look as it should, but don't count
on it. Zooming in on a
person's face will not reveal the pores.
Objectively, what fractal image compression offers is an
advanced form of
interpolation. This is a useful and attractive property.
Useful to graphic
artists, for example, or for printing on a high
resolution device. But it does
not bestow fantastically high compression ratios.
\ READER BEWARE /
That said, what is resolution enhancement? It is the
process of compressing an
image, expanding it to a higher resolution, saving it,
then discarding the
iterated function system. In other words, the compressed
fractal image is the
means to an end, not the end itself.
The Speed Problem

The essence of the compression process is the pairing of
each range block to a
domain block such that the difference between the two,
under an affine trans
formation, is minimal. This involves a lot of searching.
In fact, there is nothing that says the blocks have to
be squares or even
rectangles. That is just an imposition made to keep the
problem tractable.
More generally, the method of finding a good PIFS for
any given image involves
five main issues:
1. Partitioning the image into range blocks.
2. Forming the set of domain blocks.
3. Choosing type of transformations that will be considered.
4. Selecting a distance metric between blocks.
5. Specifying a method for pairing range blocks to domain blocks.
Many possibilities exist for each of these. The choices
that Jacquin offered
in his paper are:
1. A twolevel regular square grid with 8x8 pixels for the large
range blocks and 4x4 for the small ones.
2. Domain blocks are 16x16 and 8x8 pixels in size with a
subsampling
step size of four. The 8 isometric symmetries
(four rotations,
four mirror flips) expand the domain pool to a
virtual domain
pool eight times larger.
3. The choices in the last point imply a shrinkage by two in each
direction, with a possible rotation or flip, and
then a translation
in the image plane.
4. Mean squared error is used.
5. The blocks are categorized as of type smooth, midrange, simple
edge, and complex edge. For a given range block
the respective
category is searched for the best match.
The importance of categorization can be seen by
calculating the size of the
total domain pool. Suppose the image is partitioned into
4x4 range blocks. A
256x256 image contains a total of (2568+1)^2 =
62,001 different 8x8 domain
blocks. Including the 8 isometric symmetries increases
this total to 496,008.
There are (2564+1)^2 = 64,009 4x4 range blocks, which
makes for a maximum of
31,748,976,072 possible pairings to test. Even on a fast
workstation an exhaustive
search is prohibitively slow. You can start the program
before departing
work Friday afternoon; Monday morning, it will still be
churning away.
Increasing the search speed is the main challenge facing
fractal image compression.
Similarity to Vector Quantization

To the VQ community, a "vector" is a small rectangular
block of pixels. The
premise of vector quantization is that some patterns
occur much more frequently
than others. So the clever idea is to store only a few
of these common
patterns in a separate file called the codebook. Some
codebook vectors are
flat, some are sloping, some contain tight texture, some
sharp edges, and so
on  there is a whole corpus on how to construct a
codebook. Each codebook
entry (each domain block) is assigned an index number. A
given image, then, is
partitioned into a regular grid array. Each grid element
(each range block) is
represented by an index into the codebook. Decompressing
a VQ file involves
assembling an image out of the codebook entries. Brick
by brick, so to speak.
The similarity to fractal image compression is apparent,
with some notable
differences.
1. In VQ the range blocks and domain blocks are the same size; in
an
IFS the domain blocks are always larger.
2. In VQ the domain blocks are copied straight; in an IFS each
domain
block undergoes a luminance scaling and offset.
3. In VQ the codebook is stored apart from the image being coded;
in
an IFS the codebook is not explicitly stored. It
is comprised of
portions of the attractor as it emerges during
iteration. For that
reason it is called a "virtual codebook." It has
no existence
independent of the affine transformations that
define an IFS.
4. In VQ the codebook is shared among many images; in an IFS the
virtual codebook is specific to each image.
There is a more refined version of VQ called gainshape
vector quantization in
which a luminance scaling and offset is also allowed.
This makes the similari
ty to fractal image compression as close as can be.
Compression Ratios

Exaggerated claims not withstanding, compression ratios
typically range from
4:1 to 100:1. All other things equal, color images can
be compressed to a
greater extent than grayscale images.
The size of a fractal image file is largely determined
by the number of trans
formations of the PIFS. For the sake of simplicity, and
for the sake of comparison
to JPEG, assume that a 256x256x8 image is partitioned
into a regular
partitioning of 8x8 blocks. There are 1024 range blocks
and thus 1024 trans
formations to store. How many bits are required for
each?
In most implementations the domain blocks are twice the
size of the range
blocks. So the spatial contraction is constant and can
be hard coded into the
decompression program. What needs to be stored are:
x position of domain block
8 6
y position of domain block
8 6
luminance scaling
8 5
luminance offset
8 6
symmetry indicator
3 3
 
35 26 bits
In the first scheme, a byte is allocated to each number
except for the symmetry
indicator. The upper bound on the compression ratio is
thus (8x8x8)/35 =
14.63. In the second scheme, domain blocks are
restricted to coordinates
modulo 4. Plus, experiments have revealed that 5 bits
per scale factor and 6
bits per offset still give good visual results. So the
compression ratio limit
is now 19.69. Respectable but not outstanding.
There are other, more complicated, schemes to reduce the
bit rate further. The
most common is to use a three or four level quad tree
structure for the range
partitioning. That way, smooth areas can be represented
with large range
blocks (high compression), while smaller blocks are used
as necessary to
capture the details. In addition, entropy coding can be
applied as a backend
step to gain an extra 20% or so.
Quality: Fractal vs. JPEG

The greatest irony of the coding community is that great
pains are taken to
precisely measure and quantify the error present in a
compressed image, and
great effort is expended toward minimizing an error
measure that most often is
 let us be gentle  of dubious value. These measure
include signaltonoise
ratio, root mean square error, and mean absolute error.
A simple example is
systematic shift: add a value of 10 to every pixel.
Standard error measures
indicate a large distortion, but the image has merely
been brightened.
With respect to those dubious error measures, and at the
risk of oversimplification,
the results of tests reveal the following: for low
compression
ratios JPEG is better, for high compression ratios
fractal encoding is better.
The crossover point varies but is often around 40:1.
This figure bodes well
for JPEG since beyond the crossover point images are so
severely distorted
that they are seldom worth using.
Proponents of fractal compression counter that
signaltonoise is not a good
error measure and that the distortions present are much
more 'natural looking'
than the blockiness of JPEG, at both low and high bit
rates. This is a valid
point but is by no means universally accepted.
What the coding community desperately needs is an easy
to compute error measure
that accurately captures subjective impression of human
viewers. Until then, your
eyes are the best judge.
The Chairman's Conclusion about
Fractals
By Dr. Jack A. Shulman
In 1979, part way
through the development of a flight control
supercomputer system later used by the Department of
Defense in a classified bomber application, I was heard
to remark that I'd adapted recursive similarities since
1975, to create a self expanding system of adaptive
supercomputers, which could subdivide the control of
flight elements and assign them to iterative self
similar subcomponents, creating a Geodesic like array of
parallel computers within larger arrays of parallel
computers within larger arrays of parallel computers.
It wasn't until Benoit Mandelbrot published on Fractals,
that I realized that neurocomputing and the associative
structure of the human brain was comprised of logic
within logic arranged not unlike my supercomputers, in
an array of Cognition, Abstraction, Inference and
Induction micro engines, in essence, neuroganglial
pills all arranged to create circumlocution and form a
network of knowledge which had associations within it
that were the same as larger associations in its outer
layers that had significant self similarities contained
iteratively within them to a very atomic degree. The
parallels with Mandelbrot's Fractals in 1977 amazed me.
I marveled at the mind's immediate cognitive ability to
instantly recognize those similarities in the wondrous
spires of Fractal that the neoMandelbrotists were
producing on computer graphics screens that zoomed in on
the details within details within details endlessly.
While the Mind is
perhaps not Fractalic per say, Fractals simultaneously
trigger the Cognitive function of Similarity Recognition
and Comparison in a unique, creating an almost physical
stimulus regarding "beauty and roughness" to quote
Mandelbrot. The notion of iterative self
similarity Mandelbrot frequently refers to (clearly, he
is fascinated with the notion) does seem to apply as "a
way to explain" the means that Sleep uses to recompose
the short term memories into long term memories,
holographically sweeping clean redundant copies of
similar logic, and replacing them with references to
longer term individual copies of the basic logic that
applies, with weights and measurements added for good
measure. Even then, Sleep's compression algorithm is
also not Fractalic.
Nonetheless,
Fractals have turned out to be a key to understanding
the Cognitive functions of the mind, and even to study
the patterns of correct and incorrect logical
abstraction and inference, found within it, for many
similarities within similarities demonstrate how the
recursive nature of Human Association often is
sufficiently vague or sufficiently weak, to simply allow
us to draw the wrong conclusions.
Nonetheless, the mere study of Fractals, itself, is mind
opening, particularly when considering how to create
images of infinite selfdefinition, a problem such as
viewing the surface of the Earth from space. One
may be able to see the shoreline, but can one see the
baby horseshoe sand crabs scampering across by zooming
in on the image produced by Aerial photography today?
I have a very
strong belief that eventually, humanity will learn how
to collect nearly infinite detail using sensor based
technology, that science will progress to such a degree
that eventually when we zoom in on a video image from
Space, we will eventually be able to see not only the
shore and the crabs, but the individual grains of sand
and even the atomic structure within a single one.
And I am also of the belief that in order to store this
myriad of imagery in holographic form, we are going to
have to borrow heavily from the exceptional work of
Benoit Mandelbrot, and that which he has inspired in
others. For in my vision of the eventual nearly infinite
resolution of some future orbital camera, and its
metaphor looking OUT into the Universe through some form
of successor to Hubble (or perhaps an upgraded Hubble ST
itself?) we are talking about having to store an
enormous amount of information and then, all of us will
be thankful to Benoit Mandelbrot once again!
 Dr. Jack A. Shulman, Chairman,
ACSA
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