A watermark system includes an embedder, detector, and reader. The watermark embedder encodes a watermark signal in a host signal to create a combined signal. The detector looks for the watermark signal in a potentially corrupted version of the combined signal, and computes its orientation. Finally, a reader extracts a message in the watermark signal from the combined signal using the orientation to approximate the original state of the combined signal. While adapted for images, video and audio, the watermark system applies to other electronic and physical media. For example, it can be applied to mark graphical models, blank paper, film and other substrates, texturing objects for ID purposes, etc.

Watermark embedder and reader

Various improvements to steganographic systems, and applications therefore, are disclosed. The improvements include facilitating scale and rotation registration for steganographic decoding by use of rotationally symmetric steganographically embedded patterns and subliminal digital graticules; improved techniques for decoding without access to unencoded originals; improving robustness of steganographic coding in motion pictures and/or in the presence of lossy compression/decompression; and representing data by patterned bit cells whose energy in the spatial domain facilitates decoding registration. Applications include enhanced-security financial transactions, counterfeit resistant identification cards, fraud deterrent systems for cellular telephony, covert modem channels in video transmissions, photo duplication kiosks with automatic copyright detection, and hotlinked image objects (e.g. with embedded URLs) for use on the internet.

Computer system linked by using information in data objects

With the continuing growth of modern communications technology, demand for image transmission and storage is increasing rapidly. Advances in computer technology for mass storage and digital processing have paved the way for implementing advanced data compression techniques to improve the efficiency of transmission and storage of images. In this paper a large variety of algorithms for image data compression are considered. Starting with simple techniques of sampling and pulse code modulation (PCM), state of the art algorithms for two-dimensional data transmission are reviewed. Topics covered include differential PCM (DPCM) and predictive coding, transform coding, hybrid coding, interframe coding, adaptive techniques, and applications. Effects of channel errors and other miscellaneous related topics are also considered. While most of the examples and image models have been specialized for visual images, the techniques discussed here could be easily adapted more generally for multidimensional data compression. Our emphasis here is on fundamentals of the various techniques. A comprehensive bibliography with comments is included for a reader interested in further details of the theoretical and experimental results discussed here.

Image data compression: A review

An automated system checks networked computers, such as computers on the internet, for watermarked audio, video, or image data. A report listing the location of such audio, video or image data is generated, and provided to the proprietor(s) thereof identified by the watermark information.

Methods for surveying dissemination of proprietary empirical data

A systematic method of sparse matrix factorization is developed for all four versions of the discrete W transform, the discrete cosine transform, and the discrete sine transform, as well as for the discrete Fourier transform. The factorization leads to fast algorithms in which only real arithmetic is involved. A scheme for reducing multiplications and a convenient index system are introduced. This makes new algorithms more efficient than conventional algorithms for the discrete Fourier transform, the discrete cosine transform, and the discrete sine transform.

Fast algorithms for the discrete W transform and for the discrete Fourier transform

The Karhunen-Loeve transform for a class of signals is proven to be a set of periodic sine functions and this Karhunen-Loeve series expansion can be obtained via an FFT algorithm. This fast algorithm obtained could be useful in data compression and other mean-square signal processing applications.

A Fast Karhunen-Loeve Transform for a Class of Random Processes

A fast Karhunen-Loeve transform for a class of random processes

A two-dimensional interpolative model for coding of images is presented. It is shown that this model leads to three different coding algorithms, each algorithm defining a specific transmitter-receiver architecture. Simulation examples on a 255 × 255 image are given. Computational aspects and possible extensions are discussed.

A. Jain

Papers

A Fast Karhunen-Loeve Transform for a Class of Random Processes

A fast Karhunen-Loeve transform for a class of random processes

Image Coding Via a Nearest Neighbors Image Model