JP2000332994A - Image processor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To obtain an image processor capable of performing fast processing at a low cost while maintaining high image quality. SOLUTION: This image processor has an image data thinning part 102 which thins pixels constituting an image in image data, an image processing part 101 which performs prescribed image processing of the image data thinned by the part 102 and an image data restoring part 103 which performs restoration processing of the thinned image data processed by the part 101.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

[0001] 1. Field of the Invention [0002] The present invention relates to an image processing apparatus such as a printer and a copying machine.

[0002]

2. Description of the Related Art In recent years, image data handled by image processing apparatuses such as printers and scanners and information processing apparatuses such as personal computers have been developed to have higher resolution, color,
Due to the increase in size and the like, there is a remarkable tendency to increase the capacity of image data. In addition, under such a tendency, speeding up is required in various aspects such as high speed printing, high speed reading, and high speed processing.

Here, in a conventional general image processing apparatus, these image data are regarded as a group of pixels in a two-dimensional space, and various processing processes are performed on each pixel or a block including a pixel of interest and having a certain spatial spread. Is treated as a collection of Even today, when image data has been increasing in capacity, basically, such image processing is performed, so that the processing time increases in proportion to the data amount.

[0004]

On the other hand, in a printer, for example, high-speed printing is realized by a tandem-type print engine or the like. Also, large-scale processing has been dealt with by a dedicated image processing chip capable of high speed.

However, due to the speed limit of the hardware, there is a tendency for the speed to reach a maximum. In addition, according to the above-described means, it is not possible to realize a low-cost image processing apparatus with increased cost.

As described above, while image processing is becoming a bottleneck in the speed of a printer system, there is a demand for a low-cost and high-speed image data amount which is expected to increase in the future.

It is an object of the present invention to provide an inexpensive image processing apparatus capable of high-speed processing while maintaining high image quality.

[0008]

In order to solve this problem, an image processing apparatus according to the present invention comprises an image data thinning means for thinning out pixels constituting an image in image data, and a thinning-out method using image data thinning means. The image processing apparatus includes an image processing unit that performs predetermined image processing on the processed image data, and an image data restoration unit that performs a restoration process on the thinned image data processed by the image processing unit.

Thus, image processing is performed on the image data in which the pixels have been thinned out, and thereafter the thinned out pixels are restored, so that an image processing apparatus capable of performing high-speed processing at low cost while maintaining high image quality is provided. It is possible to obtain.

[0010]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The invention according to claim 1 of the present invention is directed to an image data thinning means for thinning out pixels constituting an image in image data, and a method for generating a predetermined image data thinned by the image data thinning means. An image processing apparatus having an image processing unit that performs image processing, and an image data restoration unit that performs a restoration process on the thinned image data processed by the image processing unit, and performs image processing on the image data with the pixels thinned out. Thereafter, since the thinned-out pixels are restored, the amount of image processing operation is greatly reduced, and an image processing apparatus capable of performing high-speed processing at low cost while maintaining high image quality can be obtained. Having.

According to a second aspect of the present invention, in the first aspect of the present invention, the image data thinning means is an image processing apparatus for thinning out pixels forming an image in a staggered manner.
In the image processing for referring to the peripheral pixels of the target pixel, the image processing can be performed without deteriorating the image quality.

According to a third aspect of the present invention, in the first or second aspect of the invention, the image data restoring means obtains a predicted value of a pixel to be restored, and obtains a plurality of candidate values of the pixel to be restored. Is calculated from the comparison between the distance between the restored pixel and the upper and lower pixels and the distance between the restored pixel and the left and right pixels, and determines one of the candidate values as the restored pixel value. Since the candidate value close to is used as the restored pixel value, it is possible to obtain a reproduced image that is visually uncomfortable.

According to a fourth aspect of the present invention, in the third aspect, the image data restoring means restores pixels of another channel based on the determined restoration pixel value selection rule. This is an apparatus and has an effect that it is possible to increase the speed of image processing without deteriorating image quality.

According to a fifth aspect of the present invention, in the first aspect of the present invention, the image data restoring means points the arrangement of line-sequential or plane-sequential image data. This is an image processing apparatus for sequentially converting and restoring an image, and has an effect that it is possible to minimize the storage capacity of image data.

FIG. 1 shows an embodiment of the present invention.
This will be described with reference to FIG.

FIG. 1 is a block diagram showing an outline of an image processing apparatus according to an embodiment of the present invention, FIG. 2 is a flowchart showing an outline of processing by the image forming apparatus of FIG. 1, and FIG.
FIG. 4 is an explanatory diagram showing an example of image processing by the image forming apparatus of FIG. 1, FIG. 4 is a flowchart showing a flow of decoding processing of thinned image data in the image forming apparatus of FIG. 1, and FIG. FIG. 6 is an explanatory diagram showing a process of thinning out image data in a staggered manner in the main scanning direction, FIG. 6 is an explanatory diagram showing a process of thinning out image data in a staggered manner in the sub-scanning direction by the image forming apparatus of FIG. 1, and FIG. FIG. 8 is an explanatory diagram showing a target pixel of the filtering process when there is no image, FIG. 8 is an explanatory diagram showing a target pixel of the filtering process when the image data is thinned out, and FIG. 9 is an explanatory diagram showing a decoding process of the thinned image data. is there.

As shown in FIG. 1, the image forming apparatus according to the present embodiment includes an image data thinning section (image data thinning means) 102 for thinning pixels constituting an image in image data, and an image data thinning section 102. An image processing unit (image processing unit) 101 that performs predetermined image processing on the thinned image data, and an image data restoration unit (image data restoration unit) 103 that performs a restoration process on the thinned image data processed by the image processing unit 101 And an image data storage unit (image data storage means) 104 for storing image data before, after, and during processing.

In such an image forming apparatus, as shown in FIG. 2, pixels of the image data stored in the image data storage unit 104 are thinned out by the image data thinning unit 102 and stored in the image data storage unit 104 again. Thinning processing (step S201), image processing in which the image processing unit 101 performs image processing on the thinned image data stored in the image data storage unit 104 and stores the processed image data in the image data storage unit 104 (step S202); The restoration processing (step S203) of restoring the thinned-out image data stored in the image data and storing the restored image data in the image data storage unit 104 is sequentially performed.

In the present embodiment, the image data storage unit 104 is provided as described above, and image data before, after, and during processing is sequentially stored. The process may proceed to the next process without storing the image data, or an independent image data storage unit may be provided for each of steps S201, S202, and S203. In the image processing performed in step 101, for example, as shown in FIG. 3, the basic three primary colors R (R
ed), G (Green) and B (Blue) are the primary colors C (Cyan), M (Magenta) and Y (Yel)
low) and a process of converting to K (Black) are performed.

This process is performed when an image displayed on the display is output to a printer.

Here, first, the luminance information R, G,
A logarithmic transformation is performed on the value of B to generate C, M, and Y values as density information (process 1), and a new K is generated based on the generated C, M, and Y values. Under color removal processing (U
CR) (process 2).

Next, a masking process for removing the influence of the unnecessary absorption characteristics of the C, M, and Y toners or inks is performed (process 3) to improve the sharpness of the image and remove noise. Filter processing is performed (process 4).

Then, the contrast / brightness is adjusted (gamma conversion) according to the image (process 5).

The image processing process described above is only an example, and some processes may be omitted or another process may be added. Further, the order of the processes may be changed according to the content of the image processing.

Referring to FIG. 5, a description will be given, with reference to FIG. 5, of the image processing apparatus according to the present embodiment configured as described above, in which the image data is thinned out in a staggered manner.

Here, thinning processing is performed on image data stored in the memory shown in FIG. 5A so as to thin pixels at discrete positions. At this time, the pixel positions of the thinning-out in the even-numbered rows and the odd-numbered rows are reversed. For example, in odd rows, 1, 3, 5 in the main scanning direction,
.., (2n + 1),.
The second, fourth,..., 2n,. In even-numbered rows, 2, 4, 6,.
.., The second pixel is selected, and 1, 3, 5,.
.., (2n + 1),. Here, n is a positive integer value of 1, 2, 3,....

In FIG. 5A, one of the images before the thinning is performed.
The pixels in the row are P11, P12, P13,..., P1
F and P1G. From these, P11, P13, P1
, P1, F, P12, P14, P16,
..., P1G is thinned out. Then, as shown in the first row of the thinned data in FIG. 5B, P11, P13,.
.., P1F remains, and the number of pixels in one row is reduced by half. It is to be noted that the rule of thinning out pixels of the odd rows and the even rows described above may be reversed. In that case, the first line is P1
2, P14, P16,..., P1G remain, but the number of pixels is also halved.

In the column direction, the odd-numbered rows have the first pixel,
In an even-numbered row, the second pixel remains. as a result,
As shown in FIG. 5B, in the first column of the thinned data, pixels are configured as P11, P22, P31, P42,. By the above steps, the number of pixels is reduced to 1 /
Can be 2.

The thinning-out processing shown in FIG. 5 is for the main scanning direction. However, as shown in FIG. 6, in the case of thinning-out in the sub-scanning direction, the same processing is performed for columns instead of rows. , The number of pixels can be reduced to half.

In this case, although the number of pixels in one row is the same as that before thinning-out, the arrangement of pixels is data of alternating odd-numbered rows and even-numbered rows. That is, as shown in FIG. 6B, the first line after the thinning is P11, P22, P13, P24,.
・ ・The first column of the thinned data is P
11, P31, P51, ..., that is, 1 before thinning
The first pixel in the third, fifth, fifth,... Rows.
Therefore, the data is compressed by に in the sub-scanning direction.

Next, it will be described with reference to FIGS. 7 and 8 that the filtering process on the thinned image data can obtain substantially the same effect as the filtering process without thinning. Here, the size of the window of the filter is 8
The description will be made as a so-called 3 × 3 including pixels.

FIG. 7 shows target pixels to be subjected to filter processing in a general 3 × 3 window when no thinning is performed.

In FIG. 7, assuming that the pixel of interest is P22, generally, eight pixels P1 around it are used for filtering.
1, P12, P13, P21, P23, P31, P3
2, all of P33 are used. For example, in the case of a low-pass filter, the pixel value Q2 of the target pixel P22 after the filtering process is performed.
2 is given as (Equation 1).

[0034]

(Equation 1)

Here, in the filtering process, the vertical four
In many cases, a coefficient for suppressing the degree of involvement of the pixels P12, P21, P23, and P32 is set low. Also, in (Equation 1), the degree of involvement of these pixels is the lowest. This focuses on the fact that, in terms of human visual characteristics, changes in vertical and horizontal directions are less perceived than changes in oblique directions. Therefore, when the highest priority is given to speeding up, the convolution coefficients of these four pixels in the vertical and horizontal directions may be 0, that is, may not be referred to. In this case, the pixel value Q22 of the target pixel P22 after the filtering process is given as in (Equation 2).

[0036]

(Equation 2)

Incidentally, the deterioration of the image quality due to this is not remarkably inferior to the case where all the surrounding eight pixels are referred to.

By the way, when nine pixels P11 to P33 in FIG. 7 are thinned out in a zigzag pattern, as shown in FIG.
The shape is formed by five pixels 22, P31 and P33. Therefore, all necessary pixels to be filtered remain in (Equation 2), and the effect of image quality degradation on filter processing due to thinning is exactly the same as in the case of filter processing in (Equation 2).

The above example applies to the case where the pixel of interest is an even-numbered row. Therefore, the case where the pixel of interest is an odd-numbered row will be described below.

When P6D in FIG. 7 is set as a target pixel, P5
C, P5D, P5E, P6C, P6E, P7C, P7
A block is composed of 9 pixels including 8 pixels of D and P7E. At this time, the pixels after decimation are the five pixels P5C, P5E, P6D, P7C, and P7E, as shown in FIG.
Pixel. Also in this case, the pixels requiring the filtering process of (Expression 2) remain as they are, and it can be seen that the thinning does not affect the image quality as in the case of the even-numbered rows.

Next, the processing procedure for decoding image data is as follows.
This will be described with reference to FIGS.

Here, when restoring image data, a method of simply taking the average value of the upper, lower, left, and right pixels of the pixel to be restored involves applying a strong low-pass filter, and the image quality of the restored image is greatly deteriorated. Therefore, restoration is performed in the following procedure.

First, pixel value data necessary for restoring image data (in FIG. 9, the pixel values of four pixels (U, D, R, L) at the top, bottom, left and right of the pixel X to be restored, (S data of five pixel values) is extracted (step S401).

Next, the predicted restoration value P is calculated. Here, as shown in FIG. 9, the restoration prediction value P is obtained by plane prediction using three pixels of the upper pixel U and the left pixel L of the restoration target pixel X and the already restored upper left pixel S, as shown in FIG. Calculation is performed on the restoration target pixel X using Expression 3) (Step S40).
2).

[0045]

(Equation 3)

In the present embodiment, the prediction is performed on one color channel. However, such a prediction may be repeatedly performed and performed on a plurality of or all channels. The channel to be selected is R
It is desirable to use a color channel with the highest visibility, such as the G channel in the case of GB and the M channel in the case of CMY, but it is also possible to select another channel depending on the color tone.

When the predicted restoration value P is obtained in this manner, a plurality of candidate values are calculated. Here, the two candidate values are calculated as C1 and C2 by the following (Equation 4) and (Equation 5).

[0048]

(Equation 4)

[0049]

(Equation 5)

Note that these candidate values are also configured to be performed for one color channel, but may be performed for a plurality of or all channels similarly to the case where the predicted value P is obtained.

In the case where the thinning-out is in a staggered manner, at the time of restoration, the remaining pixels after the thinning-out have a shape sandwiching the restored pixel from above, below, left and right. Therefore, it is appropriate to perform restoration using these four pixels having the closest spatial distance.

The candidate value C1 is an average value of the upper pixel U and the lower pixel D of the pixel X to be restored (step S40).
3). The candidate value C2 is an average value of the left pixel L and the right pixel R of the restoration target pixel X (Step S404).

After the two candidate values C1 and C2 have been obtained, the restored pixel value A is determined. That is, the target pixel and its surrounding pixels are checked, and a candidate obtained from a pixel group having a high correlation with the target pixel, that is, a pixel group having a short spatial distance is set as a restored pixel value A.

Here, first, a distance X1 between the restored pixel and the upper and lower pixels and a distance X2 between the restored pixel and the left and right pixels are obtained.

That is, the distance X1 between the upper and lower pixels is determined by the absolute value of the difference between the predicted value of the restored pixel obtained in step S402 and the upper pixel value, and the difference between the predicted value P of the restored pixel and the lower pixel. (Step S40)
5). Similarly, the distance X2 between the left and right pixels is the absolute value of the difference between the predicted value P of the restored pixel obtained in step S402 and the right pixel value, and the difference between the predicted value P of the restored pixel and the left pixel. It is obtained as a sum with the absolute value (step S406).

Then, the closer of the two distances is checked (step S407), and the distance X between the restored pixel and the upper and lower pixels is determined.
If 1 is close, the candidate value C1 obtained from the upper pixel U and the lower pixel D of the pixel X to be restored is selected as the restored pixel value A (step S408). Conversely, if the distance X2 between the restoration pixel and the left and right pixels is short, the left pixel L and the right pixel R of the restoration target pixel X
The obtained candidate value C2 is selected as the restored pixel value A (step S409).

When the restored pixel value A is obtained, the other color channel is finally restored. That is, the restored pixel value A
Is applied to the remaining channels to perform pixel restoration.

As a result, it is not necessary to perform the processing of steps S401 to S409 in restoring pixels of another channel, and it is possible to increase the speed without deteriorating the image quality.

When the arrangement of the image data is line-sequential or plane-sequential, the storage capacity for storing such selection rules becomes large. Particularly, in the case of frame sequential, it is necessary to store the selection rules for all the pixels after the thinning, so that the storage capacity is further increased. Therefore, the image data format is changed to dot sequential (for RGB, R, G, B, R, G, B,...)
In this case, since the selection rule only needs to be held for each pixel, the storage capacity can be minimized.

In the above description, the format of the image data to be processed is not particularly mentioned, but any format such as a band unit or a block unit can be adopted.

[0061]

As described above, according to the present invention, image processing is performed on image data in which pixels have been decimated, and after that, restoration processing has been performed on the decimated pixels. It is possible to obtain an effective effect that it is possible to obtain an inexpensive image processing apparatus capable of high-speed processing while maintaining high image quality.

If the pixels constituting the image are thinned out in a staggered manner, it is possible to perform the image processing without deteriorating the image quality in the image processing for referring to the peripheral pixels of the target pixel. Is obtained.

If a candidate value spatially close to the restored predicted value is used as the restored pixel value, an effective effect that it is possible to obtain a reproduced image that is visually uncomfortable can be obtained.

If pixels of other channels are restored based on the determined restoration pixel value selection rule, an effective effect is obtained in that it is possible to speed up image processing without deteriorating image quality. Can be

If an image restoration is performed by converting a line-sequential or plane-sequential image data sequence into a dot-sequential one, the storage capacity of the image data can be minimized. Is obtained.

[Brief description of the drawings]

FIG. 1 is a block diagram schematically illustrating an image processing apparatus according to an embodiment of the present invention;

FIG. 2 is a flowchart showing an outline of processing by the image forming apparatus of FIG. 1;

FIG. 3 is an explanatory diagram illustrating an example of image processing performed by the image forming apparatus of FIG. 1;

FIG. 4 is a flowchart showing the flow of decoding processing of thinned image data in the image forming apparatus of FIG. 1;

FIG. 5 is an explanatory diagram showing processing for thinning out image data in a zigzag manner in the main scanning direction by the image forming apparatus of FIG. 1;

FIG. 6 is an explanatory diagram showing processing for thinning out image data in a zigzag manner in the sub-scanning direction by the image forming apparatus of FIG. 1;

FIG. 7 is an explanatory diagram showing target pixels of a filtering process when no thinning is performed on image data;

FIG. 8 is an explanatory diagram showing target pixels of a filtering process when image data is thinned out.

FIG. 9 is an explanatory diagram showing a decoding process of thinned image data.

[Explanation of symbols]

 101 image processing unit (image processing unit) 102 image data thinning unit (image data thinning unit) 103 image data restoring unit (image data restoring unit)

 ──────────────────────────────────────────────────続 き Continued on the front page F term (reference) 2C062 AA24 5C076 AA21 AA22 BB06 BB13 BB15 5C078 AA09 BA21 CA00 DA02 DB14 EA00 9A001 BB02 BB03 BB04 CC02 EE02 EE04 HH24 HH25 HH27 HH31 JJ35 KK42KK56

Claims (5)

[Claims] An image data thinning unit for thinning out pixels constituting an image in the image data; an image processing unit for performing predetermined image processing on the image data thinned out by the image data thinning unit; An image data restoring unit for restoring the thinned image data processed by the unit. 2. The image processing apparatus according to claim 1, wherein said image data thinning means thins out pixels forming an image in a staggered manner. 3. The image data restoring means calculates a predicted value of a pixel to be restored, calculates a plurality of candidate values of the pixel to be restored, and calculates a distance between the restored pixel and upper and lower pixels, a restored pixel, and left and right pixels. 3. The image processing apparatus according to claim 1, wherein one of the candidate values is determined as a restored pixel value based on a comparison with the distance. 4. The image processing apparatus according to claim 3, wherein said image data restoring means restores pixels of another channel based on the determined restoration pixel value selection rule. 5. The image data restoring means according to claim 1, wherein said image data restoring means restores the image by converting a line-sequential or plane-sequential arrangement of said image data into a dot-sequential manner. An image processing apparatus according to claim 1.