Abstract: A technique for identifying digital object using a digital watermark. The technique includes the steps of encrypting a message derived from source data on the digital object, to obtain an encrypted message digest (S); deriving a watermark from the encrypted message digest (S); and incorporating the watermark into the source data. The encryption is preferably done with a public key encryption system. The message to be encrypted can be obtained via performing a hash function on the source data on the digital object to obtain a message digest (M). The message digest (M) is the message encrypted with the signature encryption key to obtain the encrypted message digest (S). The watermark is resistant to cropping, scaling, and truncation.

Abstract: A technique for identifying digital object using a digital watermark. The technique includes the steps of encrypting a message derived from source data on the digital object, to obtain an encrypted message digest (S); deriving a watermark from the encrypted message digest (S); and incorporating the watermark into the source data. The encryption is preferably done with a public key encryption system. The message to be encrypted can be obtained via performing a hash function on the source data on the digital object to obtain a message digest (M). The message digest (M) is the message encrypted with the signature encryption key to obtain the encrypted message digest (S). The watermark is resistant to cropping, scaling, and truncation.

Abstract: A technique for identifying digital object using a digital watermark. The technique includes the steps of encrypting a message derived from source data on the digital object, to obtain an encrypted message digest (S); deriving a watermark from the encrypted message digest (S); and incorporating the watermark into the source data. The encryption is preferably done with a public key encryption system. The message to be encrypted can be obtained via performing a hash function on the source data on the digital object to obtain a message digest (M). The message digest (M) is the message encrypted with the signature encryption key to obtain the encrypted message digest (S). The watermark is resistant to cropping, scaling, and truncation.

Abstract: A method of filtering an image to reduce noise. The image is divided into subimages or "blocks". An optimum value of a threshold parameter is determined by repeatedly applying a Singular Value Decomposition ("SVD") transform to one of the blocks for various values of the threshold parameter and then using a lossless compression algorithm to determine the compressed size of the various transforms. The optimum threshold parameter is determined by finding the maximum of the second derivative of the compressed size with respect to the log of the threshold parameter. The SVD transform is then applied to all the blocks using the optimum value of the threshold parameter, and the transformed blocks are then recombined to provide the filtered image.

Abstract: A method of compressing color source image data includes forming a quantization table with a "supra-threshold" term. This method includes a step of selecting a set of target images, where each target image includes one or more image elements such as text. These image elements are then analyzed to identify those that are more important for visual quality. These "supra-threshold" terms are then selected that gives a larger weight to the quantization table elements that correspond to important image elements and a smaller weight to the table elements that correspond to less important image elements. This process selectively weights the characteristics of each DCT basis vectors. By giving larger weights to the table elements that correspond to the "up-downness" of the image, i.e., the vertical attributes of the image elements, and the "left-rightness" of the image, i.e.

Abstract: An efficient method and apparatus that operates directly upon compressed blocks of image data to transform them into compressed blocks of scaled image data. Decompression of the compressed blocks of scaled image data yields blocks of scaled image data, which are then used to display a scaled down version of an original image. The present invention is more computationally efficient than a previously known scaling method, which required that compressed blocks of image data be uncompressed prior to any scaling operations.

Abstract: This disclosure provides a method of block-based motion estimation used in video compression. The compression process, derives change data for a new frame of data (with respect to a reference frame) by first dividing the frame structure into data tiles (or data blocks) of identical size. Each tile in the new frame is compared to a localized window (about the tile's expected position) in the reference frame to search for a best fit, and thereby provide motion data for the particular tile. Once the best fit is determined, motion-compensated difference data is determined, and stored with the motion data for each tile to complete the process. To achieve computation efficiency, each tile under analysis is preferably converted to single-bit value data and searching and comparisons are performed based on such transformed single-bit data. The single-bit data is computed by convolving the original image data with a low-pass filter to obtain a threshold matrix.

Abstract: A method of filtering an image to reduce noise. The image is divided into subimages or "blocks". An optimum value of a threshold parameter is determined by repeatedly applying a Singular Value Decomposition ("SVD") transform to one of the blocks for various values of the threshold parameter and then using a lossless compression algorithm to determine the compressed size of the various transforms. The optimum threshold parameter is determined by finding the maximum of the second derivative of the compressed size with respect to the log of the threshold parameter. The SVD transform is then applied to all the blocks using the optimum value of the threshold parameter, and the transformed blocks are then recombined to provide the filtered image.

Abstract: A method and apparatus that provide topologically consistent visual displays of an internal characteristic of a physical object. A measured signal pattern is generated, which describes how the characteristic varies spatially within the physical object. The signal pattern and a threshold value are used to generate a piecewise polygonal approximation, descriptive of the internal characteristic, that is topologically consistent with the internal characteristic. The polygonal approximation is used to generate and display an image representative of the internal characteristic.

Abstract: In a system in which a non-random, noise-free signal is disturbed by random noise, one first determines a way to measure the difference between two signals, and one also selects a measure of complexity for signals. Based on a series of discrete values of the noisy signal, a compression processor generates a series of compressed signals representing the noisy signal, each within a corresponding error or loss tolerance of the discrete values of the noisy signal. An optimization processor applies the various loss tolerance values to the compression processor and then evaluates the relative complexity of the corresponding compressed signals. The optimization processor then determines an optimal knee point loss tolerance, below which the complexity of the compressed signals rises rapidly. For continued filtering of the noisy signal, the compression processor compresses the noisy signal using the optimal knee point loss tolerance.

Abstract: Although data compression methods exist in the prior art, some of which would result in an optimal number of piecewise linear approximation segments for the compressed output signal, these compression methods require complicated calculations and data processing that inhibit real time signal processing and portability in hardware implementation. A novel method for signal compression with a known level of loss by selecting a level of acceptable error is described. With this new compression method, the number of additional approximation segments to its compressed output signal is generally 50% more than the optimal number obtained in the prior art. However, its processing simplicity allows for improved real time signal processing and simplified practical hardware implementation.