Image processing method and apparatus using a truncated histogram

Claim 2

An image processing method for processing a frame, comprising the steps of: finding all of gradation data for the frame from image signals of a picked-up image; making a truncated histogram by counting the gradation data in each gradation up to a predetermined frequency which is defined as an upper limit and ceasing said counting when said upper limit has been reached; calculating a reference minimum value of said image signal by detecting a point where a cumulative frequency counted from a smallest gradation for the reference minimum value in said truncated histogram reaches a predetermined rate set for total number of said gradation data; calculating a reference maximum value of said image signal by detecting a point where a cumulative frequency counted from a largest gradation in the reference maximum value in said histogram reaches said predetermined rate; and image-processing according to the calculated reference minimum value and reference maximum value of the image signal.

Claim 3

The image processing method according to claim 1, wherein said gradation data is a value which is obtained by dividing the frame into plural areas and averaging the image signals of picture elements in each area.

Claim 4

The image processing method according to claim 1, wherein said predetermined frequency for making said histogram equals the cumulative frequency of said predetermined rate.

Claim 5

The image processing method according to claim 1, wherein said predetermined rate for total number of said gradation data is 1%.

Claim 6

An image processing method comprising the steps of: calculating a reference minimum value and a reference maximum value of an image signal based on the image signal of a picked-up image; dividing said reference maximum value by said reference minimum value to calculate a luminance ratio; and correcting at least one of said reference minimum value and maximum value, thereby making said calculated luminance ratio closer to a predetermined reference luminance ratio.

Claim 7

An image processing method comprising the steps of: calculating a reference minimum value and a reference maximum value of an image signal based on the image signal of a picked-up image; dividing said reference maximum value by said reference minimum value to calculate a luminance ratio; and correcting at least one of said reference minimum value and maximum value, thereby making said calculated luminance ratio closer to a predetermined reference luminance ratio.

Claim 8

The image processing method according to claim 5, comprising the steps of: finding all of gradation data for the frame from image signals of the picked-up image; making a truncated histogram by counting the gradation data in each gradation up to a predetermined frequency which is defined as an upper limit and ceasing said counting which said upper limit has been reached; and finding a reference minimum value and a reference maximum value of said image signals from said truncated histogram.

Claim 9

The image processing method according to claim 5, wherein the step of correcting includes correcting said reference minimum value and reference maximum value respectively by a same value so as to make said calculated luminance ratio closer to said predetermined reference luminance ratio.

Claim 10

The image processing method according to claim 5, comprising the steps of: correcting a hard gradation to be softer by correcting the minimum value or the maximum value of said image signals to be a predetermined lower limit value of said reference luminance ratio when said luminance ratio is less than a predetermined lower limit value; and, correcting a soft gradation to be harder by correcting the minimum value or the maximum value of said image signals to be a predetermined upper limit value of said reference luminance ratio when said luminance ratio is more than a predetermined upper limit value.

Claim 11

An image processing method which comprises the steps of calculating offset values and gain of R, G and B signals of a picked-up image from a reference minimum value and a reference maximum value for respective R, G and B signals, adding the offset values to said R, G and B signals, and multiplying the offset R, G and B signals by said gain so as to equalize a black level and a white level, comprising the steps of: finding all gradation data for a frame for said respective R, G and B signals; making a histogram for said respective R, G and B signals by counting the gradation data in each gradation up to a predetermined frequency which is defined as an upper limit and ceasing said counting when said upper limit has been reached; calculating a reference minimum value for said respective R, G and B signals by detecting a point where a cumulative frequency which is cumulated from a smallest gradation for the reference minimum value in respective histograms for said R, G and B signals reaches a predetermined rate for total number of said gradation data; and calculating a reference maximum value for said R, G and B signals by detecting a point where a cumulative frequency which is cumulated from a largest gradation for the reference maximum value of respective histograms for said R, G and B signals reaches said predetermined rate.

Claim 12

An image processing method which comprises the steps of calculating offset values and gain of R, G and B signals of a picked-up image from a reference minimum value and a reference maximum value for respective R, G and B signals, adding the offset values to said R, G and B signals, and multiplying the offset R, G and B signals by said gain so as to equalize a black level and a white level, comprising the steps of: finding all gradation data for a frame for said respective R, G and B signals; making a histogram for said respective R, G and B signals by counting the gradation data in each gradation up to a predetermined frequency which is defined as an upper limit and ceasing said counting when said upper limit has been reached; calculating a reference minimum value for said respective R, G and B signals by detecting a point where a cumulative frequency which is cumulated from a smallest gradation for the reference minimum value in respective histograms for said R, G and B signals reaches a predetermined rate for total number of said gradation data; and calculating a reference maximum value for said R, G and B signals by detecting a point where a cumulative frequency which is cumulated from a largest gradation for the reference maximum value of respective histograms for said R, G and B signals reaches said predetermined rate.

Claim 13

The image processing method according to claim 9, wherein offset values R offset , G offset , and B offset of said R, G and B signals are calculated with the following equations when the reference maximum value for the R, G and B signals are R ref max, G ref max and B ref max, respectively, and a target maximum value is A: R.sub.offset =A-R.sub.ref max G.sub.offset =A-G.sub.ref max B.sub.offset =A-B.sub.ref max.

Claim 14

The image processing method according to claim 9, wherein gain R gain , G gain and B gain are calculated with following equations when reference minimum values for the R, G and B signals are R ref min, G ref min and B ref min, respectively, the reference maximum values for the R, G and B signals are R ref max, G ref max and B ref max, respectively, and a target difference is A: R.sub.gain =A/(R.sub.ref max -R.sub.ref min) G.sub.gain =A/(G.sub.ref max -G.sub.ref min) B.sub.gain =A/(B.sub.ref max -B.sub.ref min). 12.

Claim 15

An image processing apparatus for processing a frame, comprising: means for finding all of gradation data for the frame from image signals of a picked-up image; means for counting the gradation data in each gradation up to a predetermined frequency which is defined as an upper limit and for ceasing said counting when said upper limit has been reached; means for generating a truncated histogram from counted gradation data; means for calculating a reference minimum value of said image signal by detecting a point where a cumulative frequency counted from a smallest gradation for the reference minimum value in said truncated histogram reaches a predetermined rate set for total number of said gradation data; means for calculating a reference maximum value of said image signal by detecting a point where a cumulative frequency counted from a largest gradation for the reference maximum value in said histogram reaches said predetermined rate; and an image processor processing the image signal according to the calculated reference minimum value and reference maximum value of the image signal.

Claim 16

An image processing apparatus for processing a frame, comprising: means for finding all of gradation data for the frame from image signals of a picked-up image; means for counting the gradation data in each gradation up to a predetermined frequency which is defined as an upper limit and for ceasing said counting when said upper limit has been reached; means for generating a truncated histogram from counted gradation data; means for calculating a reference minimum value of said image signal by detecting a point where a cumulative frequency counted from a smallest gradation for the reference minimum value in said truncated histogram reaches a predetermined rate set for total number of said gradation data; means for calculating a reference maximum value of said image signal by detecting a point where a cumulative frequency counted from a largest gradation for the reference maximum value in said histogram reaches said predetermined rate; and an image processor processing the image signal according to the calculated reference minimum value and reference maximum value of the image signal.

Claim 17

The image processing apparatus according to claim 12, wherein said means for finding includes means for dividing the frame into plural areas and for averaging the image signals of picture elements in each area.

Claim 18

An image processing apparatus comprising: means for calculating a reference minimum value and a reference maximum value of an image signal based on the image signal of a picked-up image; means for dividing said reference maximum value by said reference minimum value to calculate a luminance ratio; and means for correcting at least one of said reference minimum value and maximum value, thereby making said calculated luminance ratio closer to a predetermined reference luminance ratio.

Claim 19

An image processing apparatus comprising: means for calculating a reference minimum value and a reference maximum value of an image signal based on the image signal of a picked-up image; means for dividing said reference maximum value by said reference minimum value to calculate a luminance ratio; and means for correcting at least one of said reference minimum value and maximum value, thereby making said calculated luminance ratio closer to a predetermined reference luminance ratio.

Claim 20

The image processing apparatus according to claim 14, further comprising: means for finding all of gradation data for one frame from image signals of the picked-up image; means for counting the gradation data in each gradation up to a predetermined frequency which is defined as an upper limit and ceasing said counting which said upper limit has been reached; means for generating a truncated histogram from counted gradation data; and means for finding a reference minimum value and a reference maximum value of said image signals from the truncated histogram.

Claim 21

The image processing apparatus according to claim 14, wherein said means for correcting includes means for correcting said reference minimum value and reference maximum value respectively by a same value so as to make said calculated luminance ratio closer to said predetermined reference luminance ratio.

Claim 22

The image processing apparatus according to claim 14, further comprising: means for correcting a hard gradation to be softer by correcting the minimum value of said image signals to be a predetermined lower limit value of said reference luminance ration when said luminance ration is less than a predetermined lower limit value; and means for correcting a soft gradation to be harder by correcting the minimum value or the maximum value of said image signals to be a predetermined upper limit value of said reference luminance ratio when said luminance ratio is more than a predetermined upper limit value.

Claim 23

An image processing apparatus for processing a frame comprising: means for calculating offset values and gain of R, G and B signals of a picked-up image of the frame from a reference minimum value and a reference maximum value for respective R, G and B signals; means for adding the offset values to said R, G and B signals, and for multiplying the offset R, G and B signals by said gain so as to equalize a black level and a white level; means for finding all gradation data for the frame for said respective R, G and B signals; means for counting the gradation data in each gradation up to a predetermined frequency which is defined as an upper limit and ceasing said counting when said upper limit has been reached; means for generating a histogram for said respective R, G and B signals from counted gradation data; means for calculating a reference minimum value for said respective R, G and B signals by detecting a point where a cumulative frequency which is cumulated from a smallest gradation for the reference minimum value in respective histograms for said R, G and B signals reaches a predetermined rate for total number of said gradation data; and means for calculating a reference maximum value for said R, G and B signals by detecting a point where a cumulative frequency which is cumulated from a largest gradation for the reference maximum value of respective histograms for said R, G and B signals reaches said predetermined rate.

Claim 24

An image processing apparatus for processing a frame comprising: means for calculating offset values and gain of R, G and B signals of a picked-up image of the frame from a reference minimum value and a reference maximum value for respective R, G and B signals; means for adding the offset values to said R, G and B signals, and for multiplying the offset R, G and B signals by said gain so as to equalize a black level and a white level; means for finding all gradation data for the frame for said respective R, G and B signals; means for counting the gradation data in each gradation up to a predetermined frequency which is defined as an upper limit and ceasing said counting when said upper limit has been reached; means for generating a histogram for said respective R, G and B signals from counted gradation data; means for calculating a reference minimum value for said respective R, G and B signals by detecting a point where a cumulative frequency which is cumulated from a smallest gradation for the reference minimum value in respective histograms for said R, G and B signals reaches a predetermined rate for total number of said gradation data; and means for calculating a reference maximum value for said R, G and B signals by detecting a point where a cumulative frequency which is cumulated from a largest gradation for the reference maximum value of respective histograms for said R, G and B signals reaches said predetermined rate.

Claim 25

The image processing apparatus according to claim 18, further comprising means for calculating offset values R offset , G offset and B offset with following equations when reference maximum values for the R, G, and B signs are R ref max, G ref max and B ref max, respectively, and a target difference is A: R.sub.offset =A-R.sub.ref max G.sub.offset =A-G.sub.ref max B.sub.offset =A-B.sub.ref max.

Claim 26

The image processing apparatus according to claim 18, further comprising means for calculating gain R gain , G gain and B gain with following equations when reference minimum values for the R, G and B signals are R ref min, G ref min and B ref min, respectively, the reference maximum values for the R, G and B signals are R ref max, G ref max and B ref max, respectively, and a target difference is A: R.sub.gain =A/(R.sub.ref max -R.sub.ref min) G.sub.gain =A/(G.sub.ref max -G.sub.ref min) B.sub.gain =A/(B.sub.ref max -B.sub.ref min). 21.

Claim 27

The image processing method according to claim 5, wherein a corrected reference maximum value T top and a corrected reference minimum value T down are calculated with following equations when said calculated luminance ratio is R 1 , said reference luminance ratio is R 2 , said reference maximum value is T 1 , and said predetermined reference minimum value is T 2 : T.sub.top =(R.sub.2 /R.sub.1).sup.1/2 ×T.sub.1 T.sub.down =(R.sub.1 /R.sub.2).sup.1/2 ×T.sub.2.

Claim 28

The image processing method according to claim 14, wherein said means for correcting calculates a corrected reference maximum value T top and a corrected reference minimum value T down with following equations when said calculated luminance ratio is R 1 , said reference luminance ratio is R 2 , said predetermined reference maximum value T 1 , and said reference minimum value is T 2 : T.sub.top =(R.sub.2 /R.sub.1).sup.1/2 ×T.sub.1 T.sub.down =(R.sub.1 /R.sub.2).sup.1/2 ×T.sub.2. 23.

Claim 29

The image processing method according to claim 5, wherein said calculated luminance ratio represents a dynamic gradation range of the picked-up image, said predetermined reference luminance ratio represents a desired dynamic gradation range, and said correcting step equalizes gray balance of the picked-up image by adjusting said dynamic gradation range of the picked-up image toward said desired dynamic gradation range.

Claim 30

The image processing method according to claim 5, wherein said reference minimum value is corrected by dividing said reference maximum value by said predetermined reference luminance ratio, and said reference maximum value is corrected by multiplying said reference minimum value by said predetermined reference luminance ratio.

Claim 31

The image processing method according to claim 5, wherein said correcting step equalizes gray balance of the picked-up image by hardening a relatively soft gradation of the picked-up image.

Claim 32

The image processing method according to claim 5, wherein said correcting step equalizes gray balance of the picked-up image by softening a relatively hard gradation of the picked-up image.

Claim 33

The image processing apparatus according to claim 14, wherein said calculated luminance ratio represents a dynamic gradation range of the picked-up image, said predetermined reference luminance ratio represents a desired dynamic gradation range, and said means for correcting equalizes gray balance of the picked-up image by adjusting said dynamic gradation range of the picked-up image toward said desired dynamic gradation range.

Claim 34

The image processing apparatus according to claim 14, wherein said reference minimum value is corrected by dividing said reference maximum value by said predetermined reference luminance ratio, and said reference maximum value is corrected by multiplying said predetermined reference minimum value by said predetermined luminance ratio.

Claim 35

The image processing apparatus according to claim 14, wherein said means for correcting equalizes gray balance of the picked-up image by hardening a relatively soft gradation of the picked-up image.

Claim 36

The image processing apparatus according to claim 14, wherein said means for correcting equalizes gray balance of the picked-up image by softening a relatively hard gradation of the picked-up image.