Image processing apparatus carrying out multi-value error diffusion process

• US 6,501,566 B1
• Filed: 03/31/1998
• Issued: 12/31/2002
• Est. Priority Date: 04/03/1997
• Status: Expired due to Term

• Current Assignee: Minolta Co., Ltd. (Konica Minolta Inc.)
• Original Assignee: Minolta Co., Ltd. (Konica Minolta Inc.)
• Inventors: Hirota, Soh, Tada, Kaoru, Ishiguro, Kazuhiro
• Primary Examiner(s): ROGERS, SCOTT A
• Application Number:
  US09/050,905
• Time in Patent Office: 1,736 Days
• Field of Search: 358/1.9, 358/1.8, 358/455, 358/466, 358/298, 358/3.05, 358/3.04, 358/3.03, 358/3.26, 382/272, 382/169, 382/237, 382/252, 382/275
• US Class Current: 358/3.05
• CPC Class Codes: H04N 1/40087 Multi-toning, i.e. converting a continuous-tone signal for reproduction with more than two discrete brightnesses or optical densities, e.g. dots of grey and black inks on white paper

Abstract

An image processing apparatus that can carry out an error diffusion process without degrading the quality of image data generates a density histogram according to pixels included in the input image data. Reference densities S1 and S2 are calculated from pixels having a certain density in a set range. Threshold values T1-T3 are calculated according to the obtained reference densities S1 and S2.

Claims

1. An image processing apparatus comprising:

reception means for receiving image data represented in M gray levels;

detection means for detecting gray levels of the received image data;

set means for setting reference densities used to classify said detected gray levels into one of N output values, when 2<

N<

M, according to the detected image density data; and

conversion means to apply an error diffusion process on the received image data using said set reference densities for converting the image data into image data of N gray levels.

2. The image processing apparatus according to claim 1, wherein said image data having the gray levels is density data with a maximum number of pixels.

3. The image processing apparatus according to claim 2, wherein said density data is determined from a density histogram according to the image data.

4. The image processing method according to claim 1, wherein the reference density is unique for each image.

5. An image processing apparatus for converting image data represented in M gray levels into image data of N (2<

N<

M) gray levels using an error diffusion process, said image processing apparatus comprising;

detection means for detecting density distribution of each pixel from said image data; and

set means for setting a density having the greatest distribution as reference densities for threshold values used in the error diffusion process to distinguish between said N gray levels as a result of detection by said detection means.

6. The image processing apparatus according to claim 5, wherein said set means comprises initial value set means for setting a reference density initial value,

range set means for setting a predetermined range including said reference density initial value, and

determination means for determining image data most frequent in said predetermined range as density data.

7. The image processing apparatus according to claim 6, wherein (N−

2) of said reference density initial values are set in a halftone region.

8. The image processing apparatus according to claim 6, wherein said reference density initial value is used as the reference density when the number of said image data most frequent in said predetermined range is smaller than a predetermined value.

9. The image processing apparatus according to claim 8, wherein said predetermined value can be modified.

10. The image processing method according to claim 5, wherein the reference density is unique for each image.

11. An image processing apparatus comprising:

reader means for reading an original document image for generating image data;

detection means for detecting from said read image data particular density data according to image data thereof;

set means for setting said detected density data as reference density data for discriminating between different output levels in a multi-value error diffusion process; and

process means for applying the multi-value error diffusion process on said read image data using said set reference density data.

12. The image processing apparatus according to claim 11, wherein said reader means generates image data of M gray levels, and

wherein said process means converts image data of M gray levels into data of N(2< N<

M) gray levels.

13. The image processing apparatus according to claim 12, wherein said detection means detects (N−

2) of said density data.

14. The image processing apparatus according to claim 13, wherein said particular density data detected by said detection means is a peak value in a density histogram of image data.

15. The image processing method according to claim 11, wherein the reference density data is unique for each original document image.

16. An image processing apparatus for converting image data represented in M gray levels into image data of N (2<

N<

M)) gray levels using an error diffusion process, said image processing apparatus comprising;

detection means for detecting particular density data from said image data represented in M gray levels according to the image data thereof; and

set means for setting said detected particular density data as reference densities used to discriminate between different output levels in the error diffusion process.

17. The image processing apparatus according to claim 16, wherein said detection means detects said particular density data from a density histogram according image data.

18. The image processing apparatus according to claim 17, wherein said particular density data is a peak value of said density histogram.

19. The image processing apparatus according to claim 16, wherein said detection means comprises

means for setting (N− 2) predetermined density ranges, and

means for determining image data most frequent in each of said predetermined density ranges as said particular density data.

20. The image processing method according to claim 16, wherein the reference density is unique for each image.

21. An image processing method comprising the steps of:

receiving image data obtained by reading out an original document image;

detecting particular density data from said received image data according to image data thereof;

setting said detected density data as reference density data for threshold values used in a multi-value error diffusion process; and

applying the multi-value error diffusion process on said read image data using said set reference density data.

22. The image processing apparatus according to claim 21, wherein said particular density data is detected from a density histogram according to image data.

23. The image processing method according to claim 22, wherein said particular density data is a peak value of said density histogram.

24. The image processing method according to claim 21, wherein the reference density data is unique for each original document image.