Computer vision-based methods for enhanced JBIG2 and generic bitonal compression
First Claim
1. A computer-implemented image compression method comprising:
- determining, in a first determining step and by a computer processor, that (a) a respective instance of each of a pair of models occurs within a respective word and (b) a same single neighboring model neighbors the instances of both models of the pair in each of the respective words of the respective instances at a same side of the respective instances;
determining, in a second determining step, by the processor, and based on the determining of the first determining step, to use a particular one of a plurality of matching algorithms for comparing the pair of models;
responsive to determining in the second determining step that the particular matching algorithm should be used, determining, in a third determining step and by the processor, whether the pair of models should be merged into a single model; and
storing, by the processor, a representation of the image, the representation including a plurality of models and being based on the determination of the third determining step.
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Accused Products
Abstract
A system and method of symbol matching may include a processor configured to determine which pixels of a first symbol are tangent pixels; for each of the determined tangent pixels, determine whether a second symbol includes a pixel corresponding to the tangent pixel that includes at least one same tangent constraint as that of the tangent pixel; accept the first and second symbols as a match based on shared tangent constraints conditional upon a determination that the second symbol includes for each of at least a subset of the tangent pixels of the first symbol a corresponding pixel that includes the at least one same tangent constraint as that of the tangent pixel; and generate a document including a single symbol that is mapped to the first and second symbols.
33 Citations
32 Claims
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1. A computer-implemented image compression method comprising:
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determining, in a first determining step and by a computer processor, that (a) a respective instance of each of a pair of models occurs within a respective word and (b) a same single neighboring model neighbors the instances of both models of the pair in each of the respective words of the respective instances at a same side of the respective instances; determining, in a second determining step, by the processor, and based on the determining of the first determining step, to use a particular one of a plurality of matching algorithms for comparing the pair of models; responsive to determining in the second determining step that the particular matching algorithm should be used, determining, in a third determining step and by the processor, whether the pair of models should be merged into a single model; and storing, by the processor, a representation of the image, the representation including a plurality of models and being based on the determination of the third determining step. - View Dependent Claims (2, 3, 4, 5)
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6. A computer-implemented image compression method comprising:
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comparing, in a first comparing step, using a first matching algorithm, and by a computer processor, each of at least a subset of a plurality of glyphs of an image to one or more other glyphs of the plurality of glyphs; for each pair of glyphs compared in the comparing step, determining, by the processor and based on a result of the comparison, whether to group the pair of glyphs into a single one of a plurality of models; comparing, in a second comparing step, using a second matching algorithm that is looser than the first matching algorithm, and by the processor, a first combination of a first pair of glyphs that are consecutively positioned in the image, to a second combination of a second pair of glyphs that are consecutively positioned in the image, wherein at least one of (a) the glyphs of the first position of the respective combinations are represented by different ones of the models based on the first matching algorithm and (b) the glyphs of the second position of the respective combinations are represented by different ones of the models based on the first matching algorithm; and for each of the first and second positions for which the respective glyphs are represented by different ones of the models, merging, by the processor, the different models into a single model based on a determination in the second comparing step that the first and second combinations match.
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7. A computer-implemented image encoding method comprising:
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for each of at least a subset of a plurality of glyphs of an image, assigning, by a computer processor and to the glyph, a respective ‘
x’
value characterizing a horizontal position of the respective glyph and a respective ‘
y’
value characterizing a vertical position of the respective glyph;in a first sorting step, sorting, by the processor, the at least the subset of glyphs by their respective ‘
x’
values;in a second sorting step, sorting, by the processor, the at least the subset of glyphs by their respective ‘
y’
values;assigning, by the processor, each of the at least the subset of glyphs to one of a plurality of ‘
x’
bins to evenly distribute the at least the subset of bins over all of the ‘
x’
bins, the distribution over the ‘
x’
bins being according to the sorted order of the at least the subset of glyphs of the first sorting step, so that a first group of the at least the subset of glyphs of the sorted order of the first sorting step are assigned to a first of the ‘
x’
bins, and each of a plurality of following groups of the at least the subset of glyphs of the sorted order of the first sorting step are assigned to respective following ones of the ‘
x’
bins;assigning, by the processor, each of the at least the subset of glyphs to one of a plurality of ‘
y’
bins to evenly distribute the at least the subset of bins over all of the ‘
y’
bins, the distribution over the ‘
y’
bins being according to the sorted order of the at least the subset of glyphs of the first sorting step, so that a first group of the at least the subset of glyphs of the sorted order of the first sorting step are assigned to a first of the ‘
y’
bins, and each of a plurality of following groups of the at least the subset of glyphs of the sorted order of the first sorting step are assigned to respective following ones of the ‘
y’
bins;for each of the at least the subset of glyphs for which a number of others of the at least the subset of glyphs that are each assigned to both the ‘
x’
bin and the ‘
y’
bin to which the respective glyph is assigned is at least a minimum threshold, comparing, by the processor, characteristics of the respective glyph to only those others of the at least the subset;for each of the at least the subset of glyphs for which a number of others of the at least the subset of glyphs that are each assigned to both the ‘
x’
bin and the ‘
y’
bin to which the respective glyph is assigned is less than the minimum threshold;comparing, by the processor, the respective glyph to other glyphs assigned to both the ‘
x’
bin and the ‘
y’
bin to which the respective glyph is assigned; anditeratively moving outward, by the processor, one level at a time to all next-level combinations of the ‘
x’
bins and ‘
y’
bins that neighbor the bin combination to which the respective glyph is assigned to compare the characteristics of the respective glyph to others of the at least the subset of glyphs included in those other neighboring bin combinations until the number of others of the at least the subset of glyphs to which the respective glyph is compared satisfies the minimum threshold; andstoring a representation of the image based on the comparisons; wherein the levels of the bin combinations are defined by an arrangement of the ‘
x’
bins along one of a horizontal axis and a vertical axis and an arrangement of the ‘
y’
bins along the other of the horizontal axis and the vertical axis, thereby forming cells that each corresponds to a respective bin combination of one of the ‘
x’
bins and one of the ‘
y’
bins, with eight neighbors one level out surrounding each one of the cells that is not an edge cell, each next level outward including eight more cells than the immediately preceding level prior to reaching an edge of the table of cells. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14)
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15. A computer-implemented image encoding method comprising:
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grouping, by a computer processor, glyphs of the image into bins based on respective second order ‘
x’
moment values of the glyphs and respective second order ‘
y’
moment values of the glyphs, the bins including ‘
x’
bins populated with respective sub-sets of the glyphs based on the second order ‘
x’
moment values of the glyphs and ‘
y’
bins populated with respective sub-sets of the glyphs based on the second order ‘
y’
moment values of the glyphs, and combinatory bins that are each formed by a respective intersection of a respective one of the ‘
x’
bins and a respective one of the ‘
y’
bins when the ‘
x’
bins are arranged along one of a horizontal axis and a vertical axis and the ‘
y’
bins are arranged along the other of the horizontal axis and the vertical axis, wherein boundaries of the ‘
x’
bins are selected to provide a uniform distribution of the glyphs over the ‘
x’
bins and boundaries of the ‘
y’
bins are selected to provide a uniform distribution of the glyphs over the ‘
y’
bins;for each of the grouped glyphs; calculating, by the processor, an XOR distance of the respective glyph to all other glyphs sharing the same combinatory bin as that of the respective glyph; at least one of; responsive to determining that the number of all other glyphs that share the same combinatory bin as that of the respective glyph is less than a predetermined threshold, calculating, by the processor, an XOR distance of the respective glyph to all other glyphs that are within those 3×
3 combinatory bins that immediately surround the combinatory bin to which the respective glyph has been assigned; andresponsive to determining that the number of all other glyphs that share the same combinatory bin as that of the respective glyph in addition to the number of all other glyphs in the 3×
3 surrounding combinatory bins is less than the predetermined threshold, calculating, by the processor, an XOR distance of the respective glyph to all other glyphs that are within those 5×
5 combinatory bins that immediately surround the 3×
3 surrounding combinatory bins; andstoring, by the processor, a list of no more than a predetermined maximum number of those other glyphs for which the shortest XOR distances to which from the respective glyph are calculated in the calculating step; and generating, by the processor, a representation of the image based on the stored lists. - View Dependent Claims (16, 17, 18, 19, 20, 21)
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22. A computer-implemented image compression method comprising:
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for each of at least a subset of a plurality of models that each represents a respective symbol formed of on pixels, adding, by a computer processor, at least one of blank rows and blank columns of off pixels to the respective model; and generating, by the processor, a representation of an image including pointers to the plurality of models with respective image location information. - View Dependent Claims (23, 24)
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25. A computer-implemented image compression method comprising:
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determining, in a first determining step and by a computer processor, a difference between a first glyph and second glyph; comparing, by the processor, the determined difference to an area within a bounding box of the first glyph; based on the comparison, determining, in a second determining step and by the processor, that the first glyph is to be encoded by referencing the second glyph; and encoding the first glyph based on the determination of the second determining step. - View Dependent Claims (26, 27, 28, 29, 30)
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31. A computer-implemented image compression method comprising:
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grouping glyphs of an image into a plurality of representative models; (b) for each distinct pair of the plurality of models, counting a number of co-occurrences in the image of glyphs represented by the pair of models neighboring each other in a same order, with a same vertical difference between the glyphs, and a same horizontal difference between the glyphs; (c) subsequently, determining for which of all pairs of models a greatest number of co-occurrences are counted in the counting step; (d) determining whether the number of co-occurrences, counted for the pair determined to be that for which the greatest number of co-occurrences were counted, is at least a predetermined threshold minimum; (e) responsive to determining that the number of occurrences is at least the predetermined threshold minimum, generating a new composite model, for inclusion as one of the plurality of representative models, the new composite model corresponding to the pair of models, for which the greatest number of co-occurrences were determined to have been counted, as a whole; (f) iteratively repeating steps (b)-(e) until an iteration in which there is no pair of models for which a counted number of co-occurrences is at least the predetermined threshold minimum; and (g) encoding the image using the plurality of representative models. - View Dependent Claims (32)
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Specification