Image processing apparatus and method thereof for decreasing the tonal number of an image

• US 8,351,083 B2
• Filed: 04/14/2009
• Issued: 01/08/2013
• Est. Priority Date: 04/18/2008
• Status: Active Grant

• Current Assignee: Canon Kabushiki Kaisha (Canon Inc.)
• Original Assignee: Canon Kabushiki Kaisha (Canon Inc.)
• Inventors: Moribe, Shoei, Ochiai, Takashi
• Primary Examiner(s): Poon, King
• Assistant Examiner(s): Washington, Jamares Q
• Application Number:
  US12/423,414
• Publication Number:
  US 20090262372A1
• Time in Patent Office: 1,365 Days
• Field of Search: 358/3.03, 358/3.04, 358/3.05
• US Class Current: 358/3.05
• CPC Class Codes: H04N 1/4053 with threshold modulated relative to input image data or vice versa

Abstract

Image data of a pixel of interest is inputted, diffusion data diffused from errors occurred by decrease of a tonal number of image data is added to the image data of the pixel of interest, the tonal number of the image data of the pixel of interest, to which the diffusion data has been added, is decreased using an error diffusion method or minimized average error method, and an error occurred in the decrease of the tonal number for the pixel of interest is diffused to pixels close to the pixel of interest. Further, the errors occurred by the decrease of the tonal number of the image data in a predetermined image region are integrated as an integrated error, and a threshold value used in the error diffusion method or minimized average error method is set based on the image data of the pixel of interest and the integrated error.

Claims

1. An image processing apparatus comprising:

an input section, configured to input image data of a pixel of interest;

an adder, configured to add diffusion data diffused from errors occurred by decrease of a tonal number of image data to the image data of the pixel of interest;

a converter, configured to decrease the tonal number of the image data of the pixel of interest, to which the diffusion data has been added, using an error diffusion method or minimized average error method; and

a diffuser, configured to diffuse an error occurred in the decrease of the tonal number for the pixel of interest to pixels close to the pixel of interest,

wherein the apparatus further comprising;

a calculator, configured to integrate, per a pixel, the errors occurred by the decrease of the tonal number of the image data in a predetermined image region as a total amount of the errors, wherein the predetermined image region is larger than an area including the pixels to which the error is diffused by the diffuser; and

a threshold setting section, configured to set a threshold value used in the error diffusion method or minimized average error method based on the image data of the pixel of interest and the total amount so that the threshold value becomes small when the total amount becomes large, and the threshold value becomes large when the total amount becomes small.

2. The apparatus according to claim 1, wherein the threshold setting section sets the threshold value based on the image data of the pixel of interest, and a product of the total amount and a predetermined coefficient.

3. The apparatus according to claim 2, wherein the threshold setting section calculates the threshold value by subtracting the product from a sum of a threshold value uniquely defined from the image data of the pixel of interest and a threshold noise uniquely defined from a pixel position of the pixel of interest.

4. The apparatus according to claim 1, further comprising a coefficient setting section configured to set a coefficient indicating a degree that the total amount influences the setting of the threshold value, based on the image data of the pixel of interest.

5. The apparatus according to claim 4, wherein the threshold setting section sets the threshold value based on the image data of the pixel of interest, and a product of the total amount and the coefficient.

6. The apparatus according to claim 5, wherein the threshold setting section calculates the threshold value by subtracting the product from a sum of a threshold value uniquely defined from the image data of the pixel of interest and a threshold noise uniquely defined from a pixel position of the pixel of interest.

7. The apparatus according to claim 4, wherein the coefficient setting section sets a large value as the coefficient in a region of an input image that includes an edge, and sets a small value as the coefficient in a region of the input image that does not include the edge.

8. The apparatus according to claim 1, wherein the calculator restricts the total amount to an upper limit value when the total amount exceeds the upper limit value, and restricts the total amount to a lower limit value when the total amount is lower than the lower limit value.

9. The apparatus according to claim 1, wherein the threshold setting section sets the threshold value so that the total amount is drawn to a zero near.

12. The apparatus according to claim 1, wherein the threshold setting section sets the threshold value based on a result of subtracting a product of the total amount and a coefficient from a reference-threshold value uniquely defined based on the image data.

13. The apparatus according to claim 1, wherein the predetermined region is an image region including pixels on which the decrease of the tonal number of the image data has been performed.

14. The apparatus according to claim 1, wherein the predetermined region is an image region including pixels for at least one line on which the decrease of the tonal number of the image data has been performed.

10. An image processing method comprising the steps of:

inputting image data of a pixel of interest;

adding diffusion data diffused from errors occurred by decrease of a tonal number of image data to the image data of the pixel of interest;

decreasing the tonal number of the image data of the pixel of interest, to which the diffusion data has been added, using an error diffusion method or minimized average error method; and

diffusing an error occurred in the decrease of the tonal number for the pixel of interest to pixels close to the pixel of interest,

wherein the method further comprising the steps of;

integrating, per a pixel, the errors occurred by the decrease of the tonal number of the image data in a predetermined image region as a total amount of the errors, wherein the predetermined image region is larger than an area including the pixels to which the error is diffused in the diffusing step; and

setting a threshold value used in the error diffusion method or minimized average error method based on the image data of the pixel of interest and the total amount so that the threshold value becomes small when the total amount becomes large, and the threshold value becomes large when the total amount becomes small.

11. A non-transitory computer-readable medium storing a computer-executable program for causing a computer to perform an image processing method, the method comprising the steps of:

inputting image data of a pixel of interest;

adding diffusion data diffused from errors occurred by decrease of a tonal number of image data to the image data of the pixel of interest;

decreasing the tonal number of the image data of the pixel of interest, to which the diffusion data has been added, using an error diffusion method or minimized average error method; and

diffusing an error occurred in the decrease of the tonal number for the pixel of interest to pixels close to the pixel of interest,

wherein the method further comprising the steps of;

integrating, per a pixel, the errors occurred by the decrease of the tonal number of the image data in a predetermined image region as a total amount of the errors, wherein the predetermined image region is larger than an area including the pixels to which the error is diffused in the diffusing step; and

setting a threshold value used in the error diffusion method or minimized average error method based on the image data of the pixel of interest and the total amount so that the threshold value becomes small when the total amount becomes large, and the threshold value becomes large when the total amount becomes small.