LOSSLESS DIGITAL DATA COMPRESSION SYSTEM
An Overview of the Technical and Marketing Aspects
Data Compression System
By Jean Simmons
President and CEO of Premier America Corporation
(The contents of this document are Proprietary and
Copyrighted Material of Premier America Corporation)
TABLE OF CONTENTS
DEFINITION OF MINCÔ
- No Limit Concept
- An Iterative Algorithm
- Compressing Random Data
- MINCä is Lossless
- "No Limit"
- Compresses the Compressed
- Unlimited Bandwidth
- Unlimited Storage Capacity
- Unlimited Rates of Data Transmission
A BRIEF HISTORY OF DATA COMPRESSION
THE BUSINESS BEHIND MINCÔ
ABOUT THE AUTHOR
MINCÔ LOSSLESS DIGITAL DATA
An Overview of the Technical
and Marketing Aspects
Data Compression System
By Jean Simmons
(The contents of this document are Proprietary and Copyrighted
Material of Premier America Corporation)
In 1996 over 200,000 people converged upon a desert
oasis. The visitors had cellular phones; as did the residents. At
some point during the world’s largest computer convention, held in
Las Vegas, a technological nightmare occurred. Despite the best
efforts of cellular service providers, the wireless network could
not cope. The most modern and sophisticated equipment in the world
failed to prevent the airwaves from clogging. Many people could not
get through at all. Those who did, found their cellular phone
transmissions were lost, dropped, or interrupted.
- For most attendees the Comdex incident of 1996 highlighted the
urgent need for a breakthrough to provide more bandwidth for
cellular phones and other wireless products. Consumers’ ever
growing desire for faster access to better communications and more
information continues to place enormous demands on technology
manufacturers to keep pace.
- The solution to this urgent need comes in the form of a newly
developed "no-limit" lossless digital data compression method that
its developer, Premier Research, LLC, calls "a quantum leap in
- Its name is MINCä Lossless Digital
Data Compression System. MINCä is an
acronym for Minimizing INformation Coder.
Before proceeding, please note that a compilation of
relevant terminology has been provided as an aid in understanding the
concepts behind MINCä compression system. See
Definition of MINCä
- MINCä is a lossless digital data
compression system that uses an iterative process to achieve "no
limit" data compression.
"No limit" lossless digital data compression means:
- "No limit" on the amount of compression which can be
- "No limit" on the type of information which can be
An Iterative Algorithm
The MINCä algorithm is designed to be
used iteratively. That is, the input data is compressed slightly in
a single step which is called a "cycle". The compressed output
produced by this first step or first cycle is then compressed again
in a second cycle using exactly the same procedure followed in the
first. The effect of this iterative process is to progressively
build an ever-increasing ratio of compression relative to the
original input, one small step at a time. On average, the MINCä
algorithm achieves about 25% compression at each cycle (iteration).
Therefore, 8 cycles yield 10:1 compression. But each 8 cycles
thereafter yields another power of 10 as shown below:
Number of Cycles
- If the application requires another order of magnitude of
compression, just add another 8 cycles. It is because of this we say
that there is "no limit" on the amount of compression, which MINCä
- If at any cycle the size of the compressed data becomes too
small to continue through further cycles, it can simply be batched
with other compressed data and the process continued. The practical
constraint on how far this process continues becomes one of time
and is, therefore, application dependent.
- When the MINCä algorithm is
implemented in a silicon chip, using a parallel architecture known
as a "systolic pipe", the time issue will be a moot point because
most of the processing time will be overlapped.
Compressing Randomized Data
It will be apparent from the foregoing discussion that as data
proceeds downward through the various cycles (iterations), the data
is becoming increasingly randomized. That is, the entropy of the
data is increasing. Any patterns present in the source data are
- The MINCä algorithm is unaffected by
this increasing randomness. Most lossless compression algorithms
depend upon the fact that the source symbols occur with a definite
frequency distribution that permits shorter codes to be assigned to
more frequently occurring symbols, and longer codes to less
frequently occurring symbols. The net result is compression. Hence,
such methods cannot compress random data. In contrast, the MINCä
algorithm is not concerned with the frequency of occurrence of
source symbols. In fact, MINCä requires
no definition of the source data whatsoever and, therefore, has no
knowledge of the meaning attached to the source symbols. The MINCä
algorithm is designed to uncover order within chaos and structure
underlying randomness. Because of this, MINCä
is unaffected by the increasing randomness of the data as we proceed
downward in the cycles. Therefore, the number of iterations can
continue virtually indefinitely. This is the basis for MINCä
’s claim of "no limit" on the amount of compression.
The data compression world is divided into 2 camps: lossy and
Lossy techniques are constructed so as to
allow decompressed data to differ from the original data
so long as the decompressed data satisfies some fidelity
criteria. For example, with image or audio compression (such as
found in MPEG and JPEG) it may suffice to have an image that
looks as good to the human eye or sounds as good to the human
ear as the original. In other words, much of the original
information can be discarded before the eye or ear detects it.
As a result, lossy compression methods, although they can
achieve large ratios, can be used only with certain data types
such as video and audio.
In contrast, with lossless techniques
the decompressed data is identical to the original data
bit-for-bit. Nothing is altered and the source data is restored
with 100% integrity. Most kinds of data compression (excluding
video and audio information) require lossless techniques.
Historically, lossless methods have only achieved an average of
about 2:1 compression. This is not enough to meet the growing
demands for more storage capacity, bandwidth and faster
- MINCä solves all this. It is
lossless and achieves very large compression ratios as we have
discussed previously. This means that MINCä
can be used with all types of information.
- Here are the main features of MINCä
Lossless Digital Data Compression System which taken together set
MINCä apart from all current and prior
art in this field.
- There is no limit on the amount of compression MINCä
- MINCä is a lossless
digital compression method.
- MINCä is universally
applicable to all data types.
- MINCä can compress data
already compressed by other methods.
The iterative procedures utilized by MINCä
mean that there is virtually no limit on the amount of compression
that can be achieved. If additional compression is required, then
more iterations are performed. The increasing randomization of the
data which results from these iterations in no way diminishes MINCä
’s ability to achieve further compression.
- Because MINCä employs "lossless"
rather than "lossy" techniques, it is able to recover all compressed
data bit-by-bit with no loss of the original information.
Decompressed data exactly equals the original.
The methods employed by MINCä to
achieve compression are applicable to all digital information. There
is no need to tailor or adapt the MINCä
algorithm for different data types. There is also no need for prior
definition of data types. This facilitates the use of MINCä
in a real-time communications environment.
Compresses the Compressed
MINCä is able to compress data
already compressed by other methods. This means MINCä
is compatible with existing compression methods and standards.
Present methods and standards cannot make this claim.
- The MINCä features described in the
previous section provide the following advantages for applications
and products in which MINCä shall be
- Unlimited Bandwidth
- Unlimited Storage Capacity
- Unlimited Rates of Data Transmission
When MINCä is used in a
telecommunications environment, its very large compression ratios
effectively provide unlimited bandwidth. Whatever bandwidth has been
assigned to an application, with MINCä
that same bandwidth will allow a vast increase in the amount of
information transmitted. The effect is the same as if there had been
a substantial increase in bandwidth. Thus, MINCä
ends forever bandwidth and frequency congestion issues.
Unlimited Storage Capacity
The large compression ratios provided by MINCä
effectively multiply the capacity of any storage system with which
it is used. Since the space required to store any item of data
compressed with MINCä is a small fraction
of what it would otherwise occupy, the effect is equivalent to
increasing the capacity of the storage device. This applies to all
types of storage devices and media (e.g. hard disk, floppy disk,
magnetic tape, CD-ROM, CD, videotape, etc.).
Unlimited Rates of Data
- Since data is compressed prior to transmission, the amount of
time to send compressed data over a communication channel (e.g.
phone line, cellular channel, etc.) is reduced by the same
proportion as the compression ratio. For example, if the compression
ratio is 100:1, then the transmission time is 1/100th of
what it would be without MINCä . Since
MINCä provides practically unlimited
compression ratios as discussed above, this translates into
virtually unlimited transmission rates.
- The MINCä features and advantages
discussed in the preceding sections provide many benefits to
equipment manufacturers, service providers and end users. We have
grouped the benefits into two categories:
- Technical benefits
- Economic benefits
- MINCä is compatible with all existing
digital technology for both data storage and telecommunications. Yet
MINCä effectively removes all of the
inherent limitations of these technologies today: limitations of (1)
storage capacity (2) bandwidth (3) data transmission rates.
Therefore, MINCä permits a giant "leapfrog"
in technical benefits and cost-performance improvements to existing
products. The demand for increased data storage capacity accelerates
constantly, particularly with multimedia applications. MINCä
solves this problem since it has the ability to increase compression
ratios as needed to meet future demands. The same applies to
the ever-increasing demand for bandwidth. MINCä
satisfies this demand by the ability to increase compression ratios
to allow available bandwidth to carry the required volume of traffic.
It does this without the need for laying another foot of copper wire,
coaxial cable, or fiber optics. In fact, MINCä
raises the question of whether in the future we will need wire-based
communication at all. MINCä endows
available frequency spectrum with the capacity to carry all present
and future telecommunication traffic in wireless form. This includes
TV and radio broadcasting, mobile radio, cellular, paging, fax,
voice and data.
- All the technical benefits outlined above are available to
manufacturers and service providers without any extra allocation of
resources for research and development. This will cost-justify the
modest royalty for the use of MINCä , in
exchange for which, they receive a complete technology ready to be
interfaced with their systems. Since MINCä
expects to receive a digital bit stream as its input and produces
the same as its output, interfacing existing systems to MINCä
is trivial. To facilitate integration with existing products, a
family of semiconductor chips will be available from licensed MINCä
manufacturers for incorporating MINCä
into current and future products. Preliminary technical
specifications will be available to licensees shortly. Obviously,
the benefits of unlimited bandwidth, unlimited storage capacity and
accelerated data transmission rates will bring huge cost performance
gains to all systems into which MINCä is
incorporated. The fact is that MINCä will
facilitate all telecommunications being in wireless form and will
save trillions of dollars in capital investment in expanding and
converting wire networks (e.g. installing fiber optics).
A Brief History of Data
- In 1952, David Huffman of Bell Labs published a method for
lossless data compression. His method guaranteed optimum codes
for any data set with a known frequency distribution. Those
symbols in the data set (the source alphabet) which occurred
most often were assigned the shortest codes. Those symbols,
which occurred less often, were assigned longer codes. This
method on average achieved 2:1 compression. In the last 45
years, most lossless methods have been based on Huffman’s work.
Even today, most lossless compression methods achieve an
average of only 2:1.
- In recent years, much work has been done in the area of
lossy compression for video and audio information. This has
given rise, for example, to the MPEG standard for video. Lossy
compression based on the use of fractals has also appeared.
These methods are inherently limited by the fact that they
degrade the quality of the restored data.
- MINCä represents a new chapter in
the history of data compression. It is lossless and yet provides
no limit on the compression which can be achieved. MINCä
is universally applicable to all digital information. It needs
no prior definition of the data to be compressed and is
therefore ideal for real-time environments. There is no longer
any need for using lossy methods.
THE BUSINESS BEHIND MINCÔ
Premier Research, LLC, a privately held Nevada company based
in Las Vegas, is the owner and developer of the technology known
as MINCä . Patents pending covering
MINCä have been assigned to Premier
- Premier America Corporation, a Nevada corporation, also
based in Las Vegas, is the exclusive worldwide marketing agent
of MINCä . An agency agreement exists
between Premier America Corporation and Premier Research, LLC.
Jean Simmons, the author of this paper, is the President and CEO
of Premier America Corporation.
- The MINCä technology will be made
available on the basis of non-exclusive technology licenses.
Licensees will pay a modest royalty for the use of the
technology to Premier Research, LLC. These licenses will be
marketed and the terms negotiated on behalf of Premier Research,
LLC by Premier America Corporation.
- It is intended the MINCä
technology will be implemented as a family of semiconductor
chips to be manufactured by MINCä
licensees. These chips will be available for purchase and
incorporation into products of suitably licensed manufacturers.
To meet the demands of the dramatic growth in the quantity
of digital information that must be transmitted, stored, and
managed in the information age, new technology is required.
, by providing:
Unlimited storage capacity;
Unlimited rates of data compression;
It is here NOW.
For more information contact:
President and CEO
Premier America Corporation
2860 East Flamingo Rd. Suite A
Las Vegas, NV 89121
ABOUT THE AUTHOR
, President and CEO of Premier America
Corporation, has nineteen years of management-level experience in
sales, marketing, and operations while working for various
high-technology companies. Her career as a management-level
executive included positions at NYNEX Corporation, Toshiba America,
TRW, Inc. and Xerox Corporation. Currently, Simmons lives in Las
Vegas and, as President of Premier America Corporation, is actively
initiating a national marketing/sales program for MINC™ Data
- Premier America Corporation based on currently available
information believed to be reliable has prepared this document.
Premier America Corporation, nor any of its respective directors,
officers, employees or representatives, make any representation or
warranty, expressed or implied, as to the accuracy or completeness
of this document or any of its contents, nor shall any of the
foregoing have any liability resulting from the use of the
information contained herein or otherwise supplied.
APPENDIX I - TERMINOLOGY
- ALGORITHM – a procedure for solving a mathematical problem, in a
finite number of steps that frequently involves repetition of an
- BANDWIDTH – refers to a range of frequencies which have been
assigned a particular use for communication purposes.
- BINARY NUMBERS – numbers expressed in (base-2) notation, a
system that uses only two digits, 0 and 1.
- CYCLE – as used here, a single iteration in the process of
compressing or decompressing information.
- DATA COMPRESSION – refers to the process of transforming a body
of data to a smaller representation from which the original or some
approximation to the original can be computed at a later time.
- DECODING – the act of converting coded information into its
- DIGITAL DATA – representing information (data) as electrical "on-off"
signals that correspond to binary digits and can be stored in
- ENCODING – the act of converting information into a code.
- ENTROPY – is a measure of the information content in a message.
- ITERATION – repetition of a sequence of computer instructions a
specified number of times or until a condition is met.
- LOSSLESS DATA COMPRESSION – requires all data compressed and
subsequently decompressed to always be identical to the original.
- LOSSY DATA COMPRESSION – allows the decompressed data to differ
from the original data so long as the decompressed data satisfies
some fidelity criteria.
- RANDOM NUMBERS – numbers distributed as though by chance,
usually with equal probability of occurrence.
- REDUNDANCY – the part of a message that can be eliminated
without loss of essential information.
- STORAGE DEVICE – a memory device in which data is stored for use
by a computer system.
APPENDIX II - DEBUNKING THE
- MINCä appears to contradict the
conventional wisdom of data compression. There are good reasons for
this. Traditional methods of lossless data compression, practiced
for over 45 years, have given rise to this conventional wisdom. It
must be understood that MINCä departs
radically from these methods and, therefore, runs counter to widely
- In this section, we seek to debunk the old myths and show that
MINCä requires new ways of thinking about
MYTH # 1 - A Universal Lossless Data Compressor
(i.e. a compressor of everything) is mathematically impossible.
It is maintained by some that a universal lossless data
compression algorithm is not possible on mathematical grounds. By "universal"
we mean an algorithm, which can compress anything including
random numbers. These critics also state that if such claims are
made for any algorithm, it is not necessary to know the details of
the algorithm in order to reject it on mathematical grounds.
- The simplest basis for mathematical objections to universal
lossless data compression is the "counting" argument. The essence of
this argument is that one cannot represent Sn things
uniquely with n-l bits. Of course, as it stands, this statement is
true and any assertion to the contrary is nonsensical. However, this
statement overlooks a very simple and very important mathematical
Namely that Sn = Sn-1
+ Sn-2 +. . .+Sn-n +1.
We give an example of the above
equation as proof.
Let S = 2 and n = 8.
28 = 256
Summing all powers of 2 less than
8 + 1 gives:
2 7 = 128
2 6 = 64
2 5 = 32
2 4 = 16
2 3 = 8
2 2 = 4
2 1 = 2
2 0 = 1
- The importance of the above is that it shows that Sn
things can be uniquely represented by a set of variable length
binary strings each of which is shorter than n bits by at least 1
- The MINCä universal lossless data
compression system is based on this simple foundation. Of course,
this leaves open the question of how to accommodate the
representation of the length of these strings and still
realize net compression.
- The MINCä algorithm has solved this
problem. Once again this contradicts the pundits, since to evaluate
this issue one must know just how MINCä
MYTH # 2 - There is not sufficient redundancy
in most real-world data to get the large compression ratios MINCÔ
- When considering the issue of redundancy in a data set versus
its true information content, it is customary to overlook the
staggering amount of redundancy introduced by the process of
- Consider the example of English text. In digital form each
letter in a block of text occupies 1 byte (8 bits). An average
length word (e.g. 5 letters), therefore, occupies 5 bytes (40 bits).
A code of this length can represent 240 things
(1,000,000,000,000). However, Oxford’s Unabridged Dictionary
contains only about 219 words (500,000). Typically, an
average word of English text when digitized is about 2 million:1
redundant. The absurdity of this becomes even more apparent if we
consider sentences. A 7-word sentence could occupy perhaps 280 bits
(7 words at 40 bits per word). A code of this size (2280
can represent a number of things greater than there are protons in
- There are other examples of such digital absurdities. Consider
digital speech. The digitization of speech with pulse code
modulation (PCM) and 8-bit samples with 8,000 samples per second
produces 64,000 bits to represent 1 second of human speech. This
means we are assigning a code capable of representing 264,000
things to the information content in 1 second of human speech.
- Formal methods do not exist for removing this kind of
redundancy. However, the MINCä algorithm
does eliminate this redundancy using its iterative procedures. It
should not be surprising, therefore, that MINCä
achieves the very large compression ratios that it does.
MYTH # 3 - Randomized data cannot be compressed.
Traditionally, lossless data compression methods have relied on
the fact that for most real-world data sources, the frequency of
occurrence of the source symbols varies widely. Such methods exploit
this fact by assigning shorter codes to the most frequently
occurring symbols and longer codes to those that occur less often.
However, randomized data symbols tend to occur with approximately
equal frequency. Therefore, traditional methods are unable to
compress such information.
- By contrast, MINCä is not concerned
with the probable frequency of occurrence of the source symbols.
MINCä does not examine the source data in
fixed-length increments (e.g. bytes). Such bit groups for random
data will, of course, occur with approximately equal frequency.
Instead, MINCä treats the source data as
composed of variable-length bit sequences, which by
definition have certain properties that will always occur
with absolute certainty. Predictability means compressibility. In
this manner, MINCä uncovers order within
chaos and structure underlying randomness. For proprietary reasons,
the nature of these sequences and their properties cannot be
MYTH # 4 - Data that has already been compressed
cannot be compressed further.
- The reason for this is closely related to MYTH # 3. Compressed
data has been at least partially randomized and cannot be further
compressed by conventional methods. Once again, the randomizing of
the data has no effect on MINCä so it can
continue to compress data which has been previously compressed by
other methods. Because of this, MINCä is
compatible with existing compression methods and standards.