Compression of binary halftones

Claim 1

A method of compressing an image, consisting of a plurality of lines of picture elements with intensities that vary with respect to each other forming patterns having a periodicity of H lines, comprising the steps of: reformatting said image by concatenating the first H lines in sequence to form a single line, p1 concatenating th next H lines in sequence to form a second line, and repeating this process of concatenating the next H lines in sequence until the entire image has been so reformatted; and applying a compression algorithm which makes use of directional correlations to compress said reformatted image.

Claim 2

A method of compressing an image, consisting of a plurality of lines of picture elements with intensities that vary with respect to each other forming patterns having a periodicity of H lines, comprising the steps of: reformatting said image by concatenating the first H lines in sequence to form a single line, p1 concatenating th next H lines in sequence to form a second line, and repeating this process of concatenating the next H lines in sequence until the entire image has been so reformatted; and applying a compression algorithm which makes use of directional correlations to compress said reformatted image.

Claim 3

A system for compressing an image, consisting of a plurality of lines of picture elements with intensities that vary with respect to each other forming pattern having a periodicity of H lines, comprising: means for reformatting said image by concatenating the first H liens in sequence to form a single line, concatenating the next H lines in sequence to form a second line, and repeating the concatenating of successive sets of H lines in this manner until the entire image has been so reformatted; and means for applying a compression algorithm which makes use of directional correlations to compress said reformatted image.

Claim 4

A system for compressing an image, consisting of a plurality of lines of picture elements with intensities that vary with respect to each other forming pattern having a periodicity of H lines, comprising: means for reformatting said image by concatenating the first H liens in sequence to form a single line, concatenating the next H lines in sequence to form a second line, and repeating the concatenating of successive sets of H lines in this manner until the entire image has been so reformatted; and means for applying a compression algorithm which makes use of directional correlations to compress said reformatted image.

Claim 5

A system for determining the pattern frequency H of an original image, consisting of a plurality of lines of picture elements with intensity values that vary with respect to each other forming patterns having a periodicity, comprising: means for selecting a maximum pattern frequency MAXFREQ to be used in searching for said pattern frequency H; means for comparing a given line of said image with each of MAXFREQ preceding lines of said image; means for determining which preceding line of said MAXFREQ preceding lines most closely matches said given line; means for selecting an estimated patter frequency by selecting the number E of lines before said given line at which the most closely match occurs to be the estimated pattern frequency; means for repeatedly actuating said comparing, determining, and selecting means with successive image lines being the given line to compute a plurality of E values for said image; and means for selecting the most frequently occurring E value as the pattern frequency H of the image.

Claim 6

A system for determining the pattern frequency H of an original image, consisting of a plurality of lines of picture elements with intensity values that vary with respect to each other forming patterns having a periodicity, comprising: means for selecting a maximum pattern frequency MAXFREQ to be used in searching for said pattern frequency H; means for comparing a given line of said image with each of MAXFREQ preceding lines of said image; means for determining which preceding line of said MAXFREQ preceding lines most closely matches said given line; means for selecting an estimated patter frequency by selecting the number E of lines before said given line at which the most closely match occurs to be the estimated pattern frequency; means for repeatedly actuating said comparing, determining, and selecting means with successive image lines being the given line to compute a plurality of E values for said image; and means for selecting the most frequently occurring E value as the pattern frequency H of the image.

Claim 9

A method for determining the pattern frequency H of an original image, consisting of a plurality of lines of picture elements with intensity values that vary with respect to each other forming patterns having a periodicity, comprising the steps of: selecting a maximum pattern frequency MAXFREQ to be used in searching for said pattern frequency H; comparing a given line of said image with each of MAXFREQ preceding lines of said image; determining which preceding line of said MAXFREQ preceding lines most closely matches said given line; selecting an estimated halftone pattern frequency by selecting the number E of lines before said given line at which the most closely match occurs to be the estimated pattern frequency; repeating said comparing, determining, and selecting steps with successive image lines being the given line to compute a plurality of E values for said image; and selecting the most frequently occurring E value as the pattern frequency H of the image.

Claim 10

A method for determining the pattern frequency H of an original image, consisting of a plurality of lines of picture elements with intensity values that vary with respect to each other forming patterns having a periodicity, comprising the steps of: selecting a maximum pattern frequency MAXFREQ to be used in searching for said pattern frequency H; comparing a given line of said image with each of MAXFREQ preceding lines of said image; determining which preceding line of said MAXFREQ preceding lines most closely matches said given line; selecting an estimated halftone pattern frequency by selecting the number E of lines before said given line at which the most closely match occurs to be the estimated pattern frequency; repeating said comparing, determining, and selecting steps with successive image lines being the given line to compute a plurality of E values for said image; and selecting the most frequently occurring E value as the pattern frequency H of the image.

Claim 13

A system for reformatting halftone data wherein an original bilevel image, having its pel values represented by successive lines of binary data, is reformatted to get another bilevel image which allows vertical correlations to be recognized comprising: means for exclusive-ORing the pel values in a given line of said original image with the respective pel values in each of a selected number of preceding lines of said image to produce respective resultant lines; means for counting the number of 1-value and 0-value runs in each resultant line; means for counting the number of 1-valued pels in each resultant line; means for determining the respective number of lines E preceding said given line at which the count of runs and the count of 1-valued pels first reach minimum values; means for storing said E values as estimated halftone pattern frequencies; means for repeatedly actuating each of the preceding means with successive lines being the given line to compute a plurality of E values; means for selecting the most frequently occurring E value as the halftone pattern frequency H of the image; and means for reformatting said image by concatenating the first H lines in sequence to form a single line, concatenating the next H lines in sequence to form a second line, and repeating the concatenating in this manner until the entire image has been so reformatted.

Claim 14

A system for reformatting halftone data wherein an original bilevel image, having its pel values represented by successive lines of binary data, is reformatted to get another bilevel image which allows vertical correlations to be recognized comprising: means for exclusive-ORing the pel values in a given line of said original image with the respective pel values in each of a selected number of preceding lines of said image to produce respective resultant lines; means for counting the number of 1-value and 0-value runs in each resultant line; means for counting the number of 1-valued pels in each resultant line; means for determining the respective number of lines E preceding said given line at which the count of runs and the count of 1-valued pels first reach minimum values; means for storing said E values as estimated halftone pattern frequencies; means for repeatedly actuating each of the preceding means with successive lines being the given line to compute a plurality of E values; means for selecting the most frequently occurring E value as the halftone pattern frequency H of the image; and means for reformatting said image by concatenating the first H lines in sequence to form a single line, concatenating the next H lines in sequence to form a second line, and repeating the concatenating in this manner until the entire image has been so reformatted.

Claim 15

A method for reformatting halftone data wherein an original bilevel image, having its pel values represented by successive lines of binary data, is reformatted to get another bilevel image which allows vertical correlations to be recognized comprising the steps of: exclusive-ORing the pel values in a given line of said original image with the respective pel values in each of a selected number of preceding lines of said image to produce respective resultant lines; counting the number of 1-value and 0-value runs in each resultant line; counting the number of 1-valued pels in each resultant line; determining the respective number of lines E preceding said given line at which the count of runs and the count of 1-valued pels first reach minimum values; storing said E values as estimated halftone pattern frequencies; repeating each of the preceding steps with successive lines being the given line to compute a plurality of E values; selecting the most frequently occurring E value as the halftone pattern frequency H of the image; and reformatting said image by concatenating the first H lines in sequence to form a single line, concatenating the next H lines in sequence to form a second line, and repeating the concatenating in this manner until the entire image has been so reformatted.

Claim 16

A method for reformatting halftone data wherein an original bilevel image, having its pel values represented by successive lines of binary data, is reformatted to get another bilevel image which allows vertical correlations to be recognized comprising the steps of: exclusive-ORing the pel values in a given line of said original image with the respective pel values in each of a selected number of preceding lines of said image to produce respective resultant lines; counting the number of 1-value and 0-value runs in each resultant line; counting the number of 1-valued pels in each resultant line; determining the respective number of lines E preceding said given line at which the count of runs and the count of 1-valued pels first reach minimum values; storing said E values as estimated halftone pattern frequencies; repeating each of the preceding steps with successive lines being the given line to compute a plurality of E values; selecting the most frequently occurring E value as the halftone pattern frequency H of the image; and reformatting said image by concatenating the first H lines in sequence to form a single line, concatenating the next H lines in sequence to form a second line, and repeating the concatenating in this manner until the entire image has been so reformatted.