Eastman-kodak Company

Qing Yu

Jiebo Luo

Kevin Edward Spaulding

Rajan L Joshi

Richard Alan Simon

Douglas W Couwenhoven

Chris W Honsinger

A method for multitone processing an N level digital image to produce an M level digital image wherein M&#x3c;N, includes the steps of: determining M reconstruction levels based on the gray level distribution of the N level image; and applying multilevel dithering to the N level digital image using the M reconstruction levels to produce the M level digital image.

Digital image multitoning method

A method for halftoning a multi-channel digital color image having an x,y array of color pixel values, wherein at least two color channels are similar having substantially the same color but with low- and high-densities, includes the steps of: providing a matrix of dither values for each group of similar color channels wherein two or more of the matrices of dither values are designed jointly to minimize a visual cos t function; for at least one group of similar color channels, forming an inverted matrix of dither values by subtracting the value of each element of the matrix of dither values for that group from a predetermined maximum value, associating the inverted matrix of dither values with one of the low- or high-density color channels, and associating the matrix of dither values for that group with the other low- or high-density color channel of that group; for each color channel of the multi-channel digital color image modularly addressing the matrix of dither values associated with that color channel using the location of a pixel in the digital color image to obtain ad addressed dither value; using the addressed dither value for each color channel, together with the pixel value for the corresponding color channel, to determine an output halftone image value for each color channel.

Method for halftoning a multi-channel digital color image having at least one group of similar color channels

Disclosed is a method for converting an input digital color image having a set of possible input colors to an output digital color image having a set of palette colors, the number of palette colors being less than the number of possible input colors, wherein the set of palette colors is determined based on the distribution of colors in the input digital image boosted by a distribution of important colors contained in the input digital image.

Method for representing a digital color image using a set of palette colors based on detected important colors

Disclosed is a method for converting an input digital color image having a set of possible input colors to an output digital color image having a set of palette colors, the number of palette colors being less than the number of possible input colors, wherein the set of palette colors is determined based on the distribution of colors in the input digital image supplemented by a distribution of important colors.

Method for representing a digital color image using a set of palette colors

A digital image processing determining the extent of blocking and contouring artifacts in a digital image includes the steps of: determining the extent of blocking artifacts using the column and row difference arrays; determining the extent of contouring artifacts based on an estimated DC quantization step size; and, determining a composite artifact measure as a function of the extent of blocking artifacts and extent of contouring artifacts. A digital image processing method determining the extent of contouring artifacts in the digital image includes the steps of: forming a column difference image; averaging the values in the columns in the column difference image to produce a column difference array; computing the average of the values in the column difference array that are separated by one block width to produce a block averaged column difference array; locating the peak value in the block averaged column difference array; repeating the above mentioned steps in the row direction; locating block boundaries based on the locations of peak values of column and row difference arrays; calculating DC value for each block; generating a histogram of the block DC values; calculating the Fourier transform of the histogram; locating the first non-DC peak in the Fourier transform domain; calculating a DC quantization step size based on the frequency of the first non-DC peak; and, employing the DC quantization step size as a measure of the extent of the contouring artifacts in the digital image.

Method of determining the extent of blocking and contouring artifacts in a digital image

A digital image processing method for adaptively sub-sampling an image to X % of original pixels, includes the steps of generating an edge map of the image; normalizing the edge map to N-bits; applying a shift to the normalized edge map such that X % of the pixels will be ones when the normalized shifted edge map is halftoned using a blue noise halftoning technique; halftoning the edge map using the blue noise halftoning technique to generate a halftone mask; and sub-sampling the image at the pixel locations represented by ones in the halftone mask.

Image processing method for adaptively sub-sampling an image

A digital image processing method for generating a low resolution, low bit depth digital image from a higher resolution, higher bit depth input color digital image, includes the steps of: generating a gray scale image from the input color digital image; generating a low resolution sketch image from the grayscale image; generating a low resolution halftone image from the gray scale image; and combining the low resolution sketch image and the low resolution halftone image to form the low resolution, low bit depth digital image.

Petite size image processing engine

A method of producing a halftoned digital image having an embedded message includes using a digital message and at least one digital carrier image to produce a scrambled message image, and halftoning the digital image employing an error diffusion process where noise is added to the digital image, the noise including the scrambled message image.

Adaptive message embedding error diffusion method

A digital image processing method determines the extent of blocking artifacts in a digital image by first forming a column difference image and averaging the values in the columns in the column difference image to produce a column difference array. The average of the values in the column difference array that are separated by one block width are computed to produce a block averaged column difference array. Then, the peak value in the block averaged column difference array is located, and the mean value of the block averaged column difference array (excluding the peak value) is calculated to produce a column base value, and the ratio between the peak value and the base value are computed to produce a column ratio. The foregoing steps are repeated in the row direction to produce a row ratio. Finally, the column and row ratios are employed as a measure of the extent of blocking artifacts in the digital image.

Method of determining the extent of blocking artifacts in a digital image

A digital image processing method reduces noise and blocking artifacts in a digital image by first converting the RGB values of the digital image pixels to Y, Cb and Cr components, then detecting the block boundaries in the Y, Cb and Cr image components, and estimating the noise in the Y, Cb and Cr image components. One or more noise tables are constructed for the Y, Cb and Cr image components. An adaptive Huber-Markov-random-field-model-based filter (HMRF) is applied to the Y, Cb and Cr image components, wherein the adaptive feature of the HMRF employs the detected block boundaries and the noise tables to produce filtered Y, Cb and Cr image components. Finally, the filtered Y, Cb and Cr image components are converted to RGB components.

Image processing method for reducing noise and blocking artifact in a digital image

A digital image processing method for generating a low resolution, low bit depth digital image from a higher resolution, higher bit depth input color digital image, includes the steps of: generating a gray scale image from the input color digital image; generating a valley image; filtering the valley image with a rank order filter to produce a filtered valley image; thresholding the filtered valley image to generate a binary valley image; generating a shadow image and a highlight image from the gray scale image; combining the shadow image, the highlight image, and the binary valley image to produce a combined binary image; and sub-sampling the combined binary image to a desired low resolution.

Image processing method and apparatus for generating low resolution, low bit depth images

A new multitoning technique is proposed that combines error diffusion, blue-noise dithering and over-modulation in an adaptive algorithm to achieve high quality multilevel printing with smooth texture transition. A periodic dither signal is first added to an input digital image wherein the amplitude of the periodic dither signal is a function of the input pixel value for each input pixel. The amplitude of the periodic dither signal is larger for input pixel values near the N output levels, and the amplitude of the periodic dither signal is smaller for input pixel values intermediate to the N output levels to produce a modified input image. Then, a multi-level error diffusion halftoning algorithm is applied to the modified input image wherein the error diffusion halftoning algorithm uses a set of error feedback weights which are adjusted according to the original input pixel value for each input pixel. The sum of the error feedback weights is smaller for input pixel values near the N output levels, and the sum of the error feedback weights is larger for input pixel values intermediate to the N output levels to produce an output multi-level digital image.

Growing community of inventors

Rochester, NY, United States of America

Qing Yu