Eastman-Kodak Company

Other

Scott J Daly

Majid Rabbani

Chris W Honsinger

Cheng-Tie Chen

John R Squilla

Michel J Denber

John Hamilton

Edward M Granger

Allen K Tsaur

Hsien-Che Lee

Richard Lawrence VanMetter

Yushing T Tsai

Hsien Che Lee

A method of embedding digital data in a source image includes the steps of: a) generating a multi-level data image representing the digital data; b) convolving the multi-level data image with an encoding carrier image to produce a frequency dispersed data image; and c) adding the frequency dispersed data image to the source image to produce a source image containing embedded data. The data is recovered from the image by: a) cross correlating the source image containing embedded data with a decoding carrier image to recover the data image; and b) extracting the digital data from the recovered data image.

Method for embedding digital information in an image

A method of hiding a first image in a second image to produce a combined image includes the steps of: a) modifying the first image according to the content of the second image, so that when the modified first image is added to the second image to produce the combined image, no visible differences will exist between the second image and the combined image; and b) adding the modified first image to the second image to produce the combined image.

Method and apparatus for hiding one image or pattern within another

A method for detecting an amount of rotation or magnification in a modified image, includes the steps of: embedding a marker image having a pair of identical features separated by a distance d and oriented at an angle .alpha. in an original image to produce a marked image, the marked image having been rotated and/or magnified to produce the modified image; performing an autocorrelation on the modified image to produce a pair of autocorrelation peaks corresponding to the location of the features of the marker image in the modified image; and comparing the separation d' and orientation .alpha.' of the autocorrelation peaks with the separation d and orientation .alpha. of the features in the marker image to determine the amount of rotation and magnification in the modified image.

Method for detecting rotation and magnification in images

A method for detecting the characteristics of a display, comprises the steps of: creating a model of the display having multiple parameters relating the physical output of the display to an input signal creating the display; employing a model of the human visual system, generating a set of control signals for producing patterns in the display that enable an observer to employ visual selection criteria to identify specific patterns corresponding to specific values of the parameters; applying the set of control signals to the display to produce the patterns; selecting specific patterns from the display; determining the values of the parameters from the patterns selected; and determining the characteristics of the display by applying the values of the parameters to the model.

Visual characterization using display model

A method is disclosed for constructing a tone scale curve. In this curve, equal log exposure differences in an x-ray image of an object produce substantially equal brightness differences in a displayed image.

Constructing tone scale curves

Visual differences between a first and second image are determined by first modifying the first and second image signals to model non-linear response to luminance in cone receptors of an eye, modifying the first and second image signals to compensate for both optical properties of the eyes and neural interaction between cells in a retina, adjusting sensitivity to image differences to model the ability of the human visual system to discern features in an image, and comparing the first and second image signals based on the adjusted sensitivity. An output based on this comparison is provided in the form of either an in-context map having first and second colors superimposed on one of the images to moderate differences or in the form of a free-form map having light and dark pixels thereon indicating differences.

Method and apparatus for determining visually perceptible differences

In accordance with the present invention, the R,G,B color image signals from a single sensor having a color filter array are all transformed to .GAMMA.-space by changing them to R.sup.1/.GAMMA., G.sup.1/.GAMMA., B.sup.1/.GAMMA., respectively, where .GAMMA. is approximately 2.4. In this space, all operations such as color differencing, interpolation of those missing pixels required for color differencing, compression, decompression, edge enhancement and final interpolation of all missing pixels are performed without further transformation of the image signals. For the same final bit rate, noise in the reproduced image is reduced by refraining from interpolating the missing color pixels prior to compression of the image data. In order to avoid over-emphasizing features of the image which are already sufficiently sharp, the combined outputs of horizontal and vertical sharpening processes are subjected to a paring process of the invention which suppresses strong high-spatial frequency components as a function of their amplitude. In the compression-decompression process of the invention, each spatial frequency coefficient of the spatial frequency-transformed image is divided by a normalization factor determined by cascading in the spatial frequency domain the human visual system contrast sensitivity function, the edge enhancement modulation transfer function and the image display modulation transfer function and inversing the resulting matrix elements.

Image compression with color interpolation for a single sensor image

An edge detection mechanism for an imagery signal compression and transmission system processes histogram and variance data to identify the presence of a uniform area with a block of transform signals. The presence of an edge within an image block, for which corresponding transform coefficients are obtained, is identified by initially generating a histogram of the image block. If the maximum value of the histogram exceeds a first threshold value, a uniform area is inferred, and adaptive quantization that would otherwise be carried out prior to executing a minimum redundancy encoding scheme is bypassed. If the maximum value of the histogram does not exceed the first threshold value, a set of variance calculations is carried out for use with a further sequence of edge detection decisions. The total variance and low frequency variance of the block are determined in accordance with the transform coefficients associated with the image block. The results of these variance calculations are then processed with further thresholds and the histogram maximum value. If the absence of an edge is inferred, the transform coefficients associated with the image block are quantized using adaptive quantization.

Histogram/variance mechanism for detecting presence of an edge within

A color imagery compression/decompression mechanism employs a contrast sensitivity function model of the human visual system for color images, to derive normalization values for compressing chromatic components of the color imagery data. The CSF model is defined in terms of an ADT color space transform (15), the chromatic channels of which conform with the band-limited sensitivity of the visual system. The transform for obtaining the ADT components is executable by reduced complexity logic operators. Each of the ADT components is block transform coded using DCT transform coefficients (18) that are normalized (24) in accordance with a `weighted` (46, 45) CSF model (42) of the human visual system, which effectively performs a low pass filtering of the chromatic (DT) channels. This low pass filter-weighting of the normalization values in the compression, transmit end (10) of the system produces a set of normalized coefficients which effectively blur the image and reduce the entropy in the chromatic channels and may be implemented using a Gaussian function. In the decompression process at the receiver (14), a corresponding set of denormalization coefficients (32), which are generated in accordance with the same chromatic CSF model of the human visual system, are not weighted, so that the cascading of the normalization and denormalizing operators in the transmitter and receiver yields a product less than unity, simulating the entropy-reducing effect of subsampling.

DCT transform compression, transmission and recovery of digital color

A system for compressing and transmitting a digital image signal over a limited bandwidth communication channel, transform codes the image values and quantizes the transform coefficients according to a two-dimensional model of the sensitivity of the human visual system. The model of the human visual system is characterized by being less sensitive to diagonally oriented spatial frequencies than to horizontally or vertically oriented spatial frequencies, thereby achieving increased compression of the image.

Digital image compression and transmission system visually weighted

In a method and apparatus for transmitting a digital image over a limited bandwidth communication channel, an image is block transformed to produce blocks of transform coefficients; the transform coefficients are quantized in accordance with a model of the visibility of quantization error in the presence of image detail; the quantized coefficients are encoded with a minimum redundancy code; and the coded, quantized transform coefficients are transmitted.

Adaptive block transform image coding method and apparatus

A still image communication system performs a block cosine transformation on an image of bandwidth compression. A transmitter subsamples a full resolution image, to provide a preview image, and transforms and transmits the preview image prior to transforming and transmitting the full resolution image. A receiver and displays the preview image, and replaces the preview image on the display block-by-block as the full resolution is received.

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Scottsville, NY, United States of America

Scott J Daly