Image processor and image processing program for binary processing

• US 20060290989A1
• Filed: 06/22/2006
• Published: 12/28/2006
• Est. Priority Date: 06/24/2005
• Status: Active Grant

• Current Assignee: Seiko Epson Corporation (Seiko Group)
• Original Assignee: Seiko Epson Corporation (Seiko Group)
• Inventors: Kobayashi, Makoto
• Granted Patent: US 7,620,255 B2
• Time in Patent Office: Days
• US Class Current: 358/3.03
• CPC Class Codes: H04N 1/4053 with threshold modulated relative to input image data or vice versa

Abstract

An image processor for converting input multi-grayscale image data, into binary data for each of the pixels has a binary unit. If a grayscale value of the multi-grayscale image data of a target pixel is lower than a reference value less than ½ of a maximum grayscale value, the binary unit converts the input image data into the binary data of a “dot present” or “dot absent” based on a threshold matrix, in which a plurality of threshold values are dispersed without concentrating a plurality of low threshold values, and if the grayscale value of the multi-grayscale image data is higher than the reference value, the binary unit converts the input image data into the binary data of a “dot present”, and propagating an error by adding the error to the peripheral pixels based on a first error diffusion matrix.

Claims

1. An image processor for converting input image data that has multi-grayscale image data for each pixel with a predetermined resolution into binary data for each of said pixels, comprising:

a binary unit, wherein

if a grayscale value of multi-grayscale image data of a processing target pixel is lower than a reference value, which is less than ½

of a maximum grayscale value of said multi-grayscale image data, said binary unit performs a first binary processing for converting the input image data of said processing target pixel into binary data of “

dot present”

or “

dot absent”

based on a threshold matrix, in which a plurality of threshold values are dispersed without concentrating a plurality of low threshold values, and

if the grayscale value of the multi-grayscale image data of the processing target pixel is higher than said reference value, said binary unit performs a second binary processing for converting the input image data of said processing target pixel into binary data of “

dot present”

, and an error diffusion processing for adding an error between the grayscale value of said processing target pixel and the maximum grayscale value to said multi-grayscale image data of peripheral pixels based on a first error diffusion matrix.

2. The image processor according to claim 1, wherein said binary unit performs an error diffusion processing for adding an error of the processing target pixel, for which said first binary processing is performed, to said multi-grayscale image data of peripheral pixels, along with said first binary processing.

3. The image processor according to claim 1, wherein said binary unit performs an error diffusion processing for adding an error of the processing target pixel binarized to “

dot present”

in said first binary processing to said multi-grayscale image data of peripheral pixels based on said first error diffusion matrix.

4. The image processor according to claim 3, wherein said first error diffusion matrix propagates all of said errors to peripheral pixels positioned in a first direction from the processing target pixel with higher weight.

5. The image processor according to claim 1, wherein said binary unit performs an error diffusion processing for adding an error of the processing target pixel binarized to “

dot absent”

in said first binary processing to said multi-grayscale image data of peripheral pixels based on a second error diffusion matrix.

6. The image processor according to claim 5, wherein said second error diffusion matrix propagates a part of said errors to peripheral pixels positioned in a second direction from the processing target pixel with higher weight.

7. The image processor according to claim 1, further comprising a gamma correction processing unit for correcting the input image data having multi-grayscale image data for each of said pixels by correcting non-linear characteristics between a grayscale value of the input image data and a grayscale value of an image reproduced by binary data, which are generated in accordance with binary processing by said binary unit, wherein

corrected input image data, corrected by said gamma correction processing unit, is binarized by said binary unit.

8. The image processor according to claim 7, wherein said gamma correction processing unit further performs dot removal correction for correcting said input image data to be lower density grayscale in a low density grayscale area, and black enhancement correction for correcting said input image data to be higher density grayscale in a high density grayscale area.

9. The image processor according to claim 8, wherein said gamma correction processing unit corrects said input image data based on a composite correction table where said non-linear characteristics correction, said dot removal correction and said black enhancement correction are combined.

10. The image processor according to claim 1, wherein the multi-grayscale data of the processing target pixel is processed by a binary data format which is made of a plurality of bits, and in said error diffusion processing, an error to be propagated is calculated by shifting said multi-grayscale data to the right by a predetermined number of bits.

17. The image processor according to claim 1, wherein the multi-grayscale data of the processing target pixel is processed by a binary data format which is made of a plurality of bits, and in said error diffusion processing, an error to be propagated is calculated by shifting said multi-grayscale data to the right by a predetermined number of bits.

11. An image processing program for converting input image data that has multi-grayscale image data for each pixel with a predetermined resolution into binary data for each of said pixels, wherein

if a grayscale value of multi-grayscale image data of a processing target pixel is lower than a reference value, which is less than ½

of a maximum grayscale value of said multi-grayscale image data, the program causes a computer to perform a first binary processing step of converting the input image data of said processing target pixel into binary data of “

dot present”

or “

dot absent”

based on a threshold matrix, in which a plurality of threshold values are dispersed without concentrating a plurality of low threshold values, and

if the grayscale values of the multi-grayscale image data of the processing target pixel is higher than said reference value, the program causes the computer to perform a second binary processing step of converting the input image data of said processing target pixel into binary data of “

dot present”

, and an error diffusion processing step of adding an error between the grayscale value of said processing target pixel and the maximum grayscale value to said multi-grayscale image data of peripheral pixels based on a first error diffusion matrix.

12. An image processor for converting input image data, that has multi-grayscale image data for each pixel with a predetermined resolution, into binary data for each of the pixels, comprising:

a binary unit, which performs a binary processing for converting the input image data of the processing target pixel into binary data of “

dot present”

or “

dot absent”

based on a threshold matrix, in which a plurality of threshold values are arranged, and an error diffusion processing for adding an error between the input grayscale value of the “

dot present”

pixel and the maximum grayscale value to the multi-grayscale image data of peripheral pixels based on a first error diffusion matrix, and adding an error between the input grayscale value of the pixel of “

dot absent” and

the minimum grayscale value to the multi-grayscale image data of peripheral pixels based on a second error diffusion matrix which is different from the first error diffusion matrix.

13. The image processor according to claim 12, wherein for the first error diffusion matrix, the error is propagated to peripheral pixels positioned in a first direction from the processing target pixel with a higher weight, and for the second error diffusion matrix, the error is propagated to peripheral pixels in a second direction, which is different from the first direction from the processing target pixel with a higher weight.

14. The image processor according to claim 12, wherein for the first error diffusion matrix, all the errors are propagated to peripheral pixels, and for the second error diffusion matrix, a part of the errors is propagated to peripheral pixels.

15. The image processor according to claim 12, wherein if a grayscale value of the multi-grayscale image data of a processing target pixel is lower than a reference value, which is less than ½

of a maximum grayscale value of the multi-grayscale image data, the binary unit performs binary processing based on the threshold matrix, and if a grayscale value of the multi-grayscale image data of a processing target pixel is higher than the reference value, the binary unit converts the input image data of the processing target pixel into the binary data “

dot present”

, and adds the error to the multi-grayscale image data of the peripheral pixels based on the first error diffusion matrix.

16. The image processor according to claim 12, wherein in the threshold matrix, a plurality of threshold values are dispersed without concentrating a plurality of low threshold values.

18. An image processor for converting input image data, that has multi-grayscale image data for each pixel with a predetermined resolution, into binary data for each of the pixels, comprising:

a binary unit, wherein

if a grayscale value of the multi-grayscale image data of a processing target pixel is lower than a reference value, which is less than ½

of a maximum grayscale value of the multi-grayscale image data, the binary unit performs binary processing for converting the input image data of the processing target pixel into the binary data of “

dot present”

or “

dot absent”

based on the threshold matrix, in which a plurality of threshold values are dispersed without concentrating a plurality of low threshold values, and the binary unit performs an error diffusion processing for adding an error of the processing target pixel generated by the binary processing to the multi-grayscale image data of peripheral pixels.

19. The image processor according to claim 18, wherein in the error diffusion processing, the binary unit adds the error of the pixel of “

dot present”

to the multi-grayscale image data of peripheral pixels based on a first error diffusion matrix, and adds the error of the pixel with “

dot absent”

to the multi-grayscale image data of peripheral pixels based on a second error diffusion matrix, which is different from the first error diffusion matrix.

20. The image processor according to claim 19, wherein the multi-grayscale data of the processing target pixel is processed by a binary data format which is made of a plurality of bits, and in said error diffusion processing, an error to be propagated is calculated by shifting said multi-grayscale data to the right by a predetermined number of bits.

21. An image processing program for converting input image data, that has multi-grayscale image data for each pixel with a predetermined resolution, into binary data for each of the pixels, the image processing program causing a computer to perform:

a binary processing step of converting the input image data of the processing target pixel into binary data of “

dot present”

or “

dot absent”

based on a threshold matrix, in which a plurality of threshold values are arranged, and

an error diffusion processing step of adding an error between the input grayscale value of the “

dot present”

pixel and the maximum grayscale value to the multi-grayscale image data of peripheral pixels based on a first error diffusion matrix, and adding an error between the input grayscale value of the pixel of “

dot absent” and

the minimum grayscale value to the multi-grayscale image data of peripheral pixels based on a second error diffusion matrix which is different from the first error diffusion matrix.

22. An image processing program for converting input image data, that has multi-grayscale image data for each pixel with a predetermined resolution, into binary data for each of the pixels, the image processing program causing a computer to perform:

if a grayscale value of the multi-grayscale image data of a processing target pixel is lower than a reference value, which is less than ½

of a maximum grayscale value of the multi-grayscale image data, a binary processing step of converting the input image data of the processing target pixel into the binary data of “

dot present”

or “

dot absent”

based on the threshold matrix, in which a plurality of threshold values are dispersed without concentrating a plurality of low threshold values, and an error diffusion processing step of adding an error of the processing target pixel generated by the binary processing to the multi-grayscale image data of peripheral pixels.

23. An image processor for converting input image data, that has multi-grayscale image data for each pixel with a predetermined resolution, into binary data for each of the pixels, comprising:

a binary unit, wherein

if a grayscale value of the multi-grayscale image data of a processing target pixel is lower than a reference value, which is less than ½

of a maximum grayscale value of the multi-grayscale image data, said binary unit performs a first binary processing for converting the input image data of the processing target pixel into the binary data of “

dot present”

or “

dot absent”

based on the threshold matrix, in which a plurality of threshold values are dispersed without concentrating a plurality of low threshold values, and a first error diffusion processing for adding an error between the input grayscale value of the “

dot present”

pixel and the maximum grayscale value to the multi-grayscale image data of peripheral pixels based on a first error diffusion matrix, and adding an error between the input grayscale value of the pixel of “

dot absent” and

the minimum grayscale value to the multi-grayscale image data of peripheral pixels based on a second error diffusion matrix which is different from the first error diffusion matrix,

if the grayscale values of the multi-grayscale image data of the processing target pixel is higher than said reference value, the binary unit performs a second binary processing for converting said processing target pixel into binary data of “

dot present”

, and a second error diffusion processing for adding an error between the input grayscale value of said processing target pixel and the maximum grayscale value to said multi-grayscale image data of peripheral pixels based on a first error diffusion matrix.

24. The image processor according to claim 23,

wherein for the first error diffusion matrix, the error is propagated to peripheral pixels positioned in a first direction from the processing target pixel with a higher weight, and for the second error diffusion matrix, the error is propagated to peripheral pixels in a second direction, which is different from the first direction from the processing target pixel with a higher weight,

wherein for the first error diffusion matrix, all the errors are propagated to peripheral pixels, and for the second error diffusion matrix, a part of the errors is propagated to peripheral pixels.