A first international data compression standard became available when the CCITT adopted the V.42bis. standard in 1990. The standard is now used by more than 50 modem manufacturers and is rapidly,spreading into Local and Remote Area Networks (LANs, WANs). Follow-on standards now being developed (such as V.34 or V.fast) use the same data compression method in different communications protocols. British Telecom developed the V.42bis standard by combining a variety of inventions and ideas into a standard called BLTZ (British Telecom Lempel Ziv). Data compression is based on tree libraries invented by Holtz in 1974, which Welch modified in 1984 into the LZW codes. British Telecom added "Delayed Innovation" and a limited recycling library. A built-in microprocessor monitors the compression ratio, switching compression on or off using IBM's "Escape Character" sequences. The output codes are stuffed into 8 bit octets and combined into packets which include error checking codes. Defective packets are re-transmitted automatically, ensuring error free data transmission. >

Data compression in the V.42bis modems

Three recently introduced technologies: a new Autosophy information theory; fast Content Addressable Memories; and Packet Switching protocols, are combined in data compression chipsets to improve network throughput with real-time lossless text and image compression. Compared with the cost of putting new cables into the ground or launching new satellites, data compression chipsets offer a much less expensive alternative for improving the bandwidth of broadband communication networks. Data compression is based on a new Autosophy information theory in which, in contrast to Shannon's theory, communication is determined only by the data content. In addition to high lossless data compression, this also provides virtually unbreakable 'codebook' encryption and easier communication via packet switching networks. The Autosophy theories provide eight known classes of self-learning Omni Dimensional Networks. Only the serial network is explained here for use in compressed text and video communications. A new Content Addressable Read Only Memory (CAROM) may increase data compression speed to more than 20 Million symbols per second, fast enough for virtually any network speed.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Advanced data compression promises the next big leap in network performance

A new autosophy information theory provides an alternative to the classical Shannon information theory. Using the new theory in communication networks provides both a high degree of lossless compression and virtually unbreakable encryption codes for network security. The bandwidth in a conventional Shannon communication is determined only by the data volume and the hardware parameters, such as image size; resolution; or frame rates in television. The data content, or what is shown on the screen, is irrelevant. In contrast, the bandwidth in autosophy communication is determined only by data content, such as novelty and movement in television images. It is the data volume and hardware parameters that become irrelevant. Basically, the new communication methods use prior 'knowledge' of the data, stored in a library, to encode subsequent transmissions. The more 'knowledge' stored in the libraries, the higher the potential compression ratio. 'Information' is redefined as that which is not already known by the receiver. Everything already known is redundant and need not be re-transmitted. In a perfect communication each transmission code, called a 'tip,' creates a new 'engram' of knowledge in the library in which each tip transmission can represent any amount of data. Autosophy theories provide six separate learning modes, or omni dimensional networks, all of which can be used for data compression. The new information theory reveals the theoretical flaws of other data compression methods, including: the Huffman; Ziv Lempel; LZW codes and commercial compression codes such as V.42bis and MPEG-2.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Autosophy information theory provides lossless data and video compression based on the data content

Transmitting graphics or live television on the Internet or Information Superhighway will be very slow and expensive unless video compression is used to reduce the transmission bandwidth. There are three basic ways to transmit images and video. Conventional bit stream transmissions depend on very high speed channels, such as fiber optics. Lossy cosine transforms and motion compensation in the JPEG or MPEG-2 standards will reduce the bandwidth but at the expense of the image quality. The more the images are compressed, the worse the distortions become until the image quality becomes unacceptable. A new Autosophy information theory may now provide a third alternative. In the new information theory, “Information” is dependent only on image content or ”Novelty” and ”Movement” within the images. Information is redefined as something not already known to the receiver. Everything the receiver already knows is redundant and need not be communicated. ”Knowledge” is accumulated in tree networks that grow by themselves like data crystals. The result is a new kind of television in which the bandwidth is determined only by the image content or what is to be shown on the screen. Hardware parameters, such as image size, resolution or frame rates become irrelevant. Very high lossless image compression is achieved without sacrificing image quality. The transmission method is especially suitable for packet switching networks and provides optional encryption for network security. It can also be used to transmit still image graphics on the Internet or to store Multimedia moving images in PCs.

http://ieeexplore.ieee.org/iel2/3501/10332/00485413.pdf

Autosophy image compression for packet network television

Autosophy is a new science which tries to explain self-assembling structures, such as crystals or living trees, in mathematical terms. This research provides algorithms for growing self-learning data networks in memories, similar to the growing of data crystals or data trees, without programming or conventional data processing. The new learning algorithms provide an entirely new technology for building brain-like robot vision systems. Television images from a camera are learned and stored in a peculiar omni dimensional hyperspace library. This will result in enormous data compression because no portion of the image is stored twice. The more images have already been learned the fewer storage locations will be required to store additional images. The omni dimensional hyperspace library allows for near instant search access which is independent of the library size. The resulting brain-like robot vision systems are educated very much like a human child by showing objects to a television camera and associating them with descriptive text or robot arm motions. Autosophy may succeed where conventional data processing has failed in providing intelligent self-organizing robot vision. Text and image learning has been simulated in software to show learning behavior which is strikingly similar to our own brain.

Autosophy networks yield self-learning robot vision

Autosophy, an emerging new science, explains `self-assembling structures,' such as crystals or living trees, in mathematical terms. This research provides a new mathematical theory of `learning' and a new `information theory' which permits the growing of self-assembling data network in a computer memory similar to the growing of `data crystals' or `data trees' without data processing or programming. Autosophy databases are educated very much like a human child to organize their own internal data storage. Input patterns, such as written questions or images, are converted to points in a mathematical omni dimensional hyperspace. The input patterns are then associated with output patterns, such as written answers or images. Omni dimensional information storage will result in enormous data compression because each pattern fragment is only stored once. Pattern recognition in the text or image files is greatly simplified by the peculiar omni dimensional storage method. Video databases will absorb input images from a TV camera and associate them with textual information. The `black box' operations are totally self-aligning where the input data will determine their own hyperspace storage locations. Self-aligning autosophy databases may lead to a new generation of brain-like devices.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Klaus E. Holtz

Papers

Self-aligning and compressed autosophy video databases