JP2667860B2 - Image processing method and apparatus - Google Patents

Description

Description: BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing method and apparatus for quantizing image data, and more particularly to performing quantization by a density-conserving quantization method such as an error diffusion method. The present invention relates to an image processing method and apparatus.

[Conventional technology]

2. Description of the Related Art Conventionally, in an apparatus such as a digital copying machine and a digital scanner, various image processing methods for reproducing a halftone image by a binary image have been known. One of the image processing methods is an error diffusion method. This method calculates a density difference (error data) for each pixel between the image density of a document read by a reading device and the output image density output from a printer, and binarizes the error data as the calculation result. This is a method in which specific weighting is applied to the preceding peripheral pixels to disperse them.

Since this method spatially corrects the error data between the original image density and the output density, there is no restriction on the number of gradations due to the matrix size as in the other image processing methods such as dither processing, and the original density is faithful. Output image can be reproduced. Further, this error diffusion method makes it possible to achieve both gradation and resolution, which are problems in dither processing.

Regarding this error diffusion method, refer to PWFloyd and L. Stei
nberg “An Adaptive Algorihm for Spatial Gray Scal
Published in e "SID75 Digest (1976).

[Problems to be solved by the invention]

However, in the error diffusion method described above, for example, the luminance data read by the reading device is quantized into 6-bit density data (0 (white) to 63 (black)) for each pixel, and the quantized density data is quantized by the error diffusion method. In the case of binarization, for example, if data of density level 1 is uniformly distributed, an error between output data 0 and density data 1 when binarizing this 1 is sequentially added to neighboring pixels. There is a drawback that black dots are output when the generated pixel value exceeds the threshold for binarization.

In other words, although the portion where the density level 1 is uniformly distributed looks all white to human eyes, the above-described black spots cause graininess noise in the white portion. Then, the image quality deteriorates due to the granularity noise in the high contrast portion.

Also, even if you read an all-white image, if the video signal from the CCD is small against the dynamic range of the AD converter, a certain number of values will be output from the AD converter even with all-white, and graininess noise will occur as in the case above. And the quality of the image is degraded.

In addition, there is a disadvantage that, despite the all-white image, the generation of graininess noise (black point) also lowers the encoding efficiency.

[Means and Actions for Solving Problems] According to the image processing method of the present invention, when the input image density data is judged to be white, regardless of the binarized output of the density storage type binarization processing method. The binarized output of the input image density data is set to white, and then the binarized data is encoded. If the input image density data is equal to or less than a predetermined value, a density preserving type binarization process is performed. Image density and 2 in the method
The error from the output image density after the binarization process is set to 0, and then 2
It encodes the data that has been subjected to the value processing.

According to the image processing apparatus of the present invention, the first binarizing means for binarizing the image data by the error diffusion method, and the second binarizing means for binarizing the image data with a predetermined threshold value 2
Binarizing means, encoding means for encoding the binarized binary data, and final binary data based on a result of the binarizing processing of the first and second binarizing means. Determining means for determining, when the image data is binarized to white by the second binarizing means, irrespective of the binarization result of the first binarizing means. The binary data is determined to be white, and when the image data is binarized to black by the second binarization unit, the binarization result of the first binarization unit is converted to a final binarization result. The encoding means determines the value of the value data, and the encoding means encodes the binary data finally determined by the determining means.

It is an object of the present invention to improve the coding efficiency by removing the granular noise in the highlight part of the image by these configurations.

〔Example〕

Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. FIG. 1 is a block diagram of a facsimile apparatus to which the present invention is applied.

In FIG. 1, reference numeral 1 denotes a network control unit NCU (Network Control Uni) for controlling connection between a telephone line 1a, a telephone set 2 and a hybrid circuit 3 on a data communication device side and holding a loop.
t).

 2 is a telephone.

Reference numeral 3 denotes a hybrid circuit for separating a signal of a transmission system and a signal of a reception system. In the hybrid circuit 3, the transmission signal from the signal line 3b is transmitted to the telephone line via the signal line 1c and the NCU 1, and the signal transmitted from the other party is transmitted to the NCU 1 and the signal line 1c.
To the signal line 3a via the

Reference numeral 6 denotes a reading / binarization circuit which sequentially reads image signals for one line in the main scanning direction from a transmission original and creates a signal sequence representing white and black values by an error diffusion method. This reading
The binarization circuit includes an image sensor such as a CCD (Charge Coupled Device), an optical system, and a binarization circuit which converts read luminance data into density data and binarizes the data by an error diffusion method.
The binarization circuit will be described later in detail. The black and white binarized signal sequence is output to the signal line 6a.

5 inputs the binarized data output to the signal line 6a,
Encoding (MH (Modified Eye Huffman) encoding or MR
This is an encoding circuit that outputs (modified read) encoded data to the signal line 5a.

4 is a known CCITT recommendation V27ter (differential phase modulation) or V
This is a modulator that performs modulation based on 29 (quadrature modulation). The modulator 4 receives the signal on the signal line 5a, modulates the signal, and outputs modulated data to the signal line 3b.

Reference numeral 7 denotes a demodulator that performs demodulation based on the known CCITT recommendation V27ter or V29. The demodulator 7 receives the signal on the signal line 3a, performs demodulation, and outputs demodulated data to the signal line 7a.

Reference numeral 8 denotes a decoding circuit which receives demodulated data from the signal line 7a and outputs the decoded data (MH decoding or MR decoding) to the signal line 8a.

9 inputs the signal output to the signal line 8a, and sequentially 1
This is a recording circuit that performs recording for each line.

Next, the MH coding method will be described as an example in the coding circuit 5 of FIG.

The signal line of one scanning line from the reading / binarization circuit 6 can be alternately divided into a white portion and a black portion as shown in FIG. In FIG. 2, A, C, and E are white portions, and B and D are black portions. The number of pixels in each of the white and black portions is called run length.

The colors (white or black) of A to E and the run length are encoded, and the image information is compressed and then sent to the modulator 4. When encoding, each run is encoded with a Huffman code. The Huffman code consists of a terminating code and a make-up code.

In the Huffman code, the longer the run of the same color, the higher the compression ratio. The pattern with the lowest encoding efficiency, that is, the pattern with the lowest compression ratio is a case where black and white appear alternately. That is,
If graininess noise occurs in all white portions of the document, the coding efficiency is significantly reduced. Therefore, it is necessary to read the all-white portion as all-white information.

FIG. 3 is a hardware configuration diagram showing details of the read / binarization circuit of FIG.

The binarization process by the error diffusion method will be described below with reference to FIG.

10 is an input sensor unit for reading a document, 11 is an AD converter, 12
Is a correction circuit for performing shading correction, etc., 13 is a conversion table for converting luminance data into density data, 14 is a comparator, 15 is an error correction circuit that adds an error value to the density of the original pixel, and 16 is binary data. A binarization circuit for converting to data, 17 is an error calculation circuit for calculating an error, 18 is an AND circuit, and 5 is the same as the encoding circuit 5 in FIG.

 Hereinafter, this embodiment will be described with reference to FIG.

The input sensor unit 10 includes a photoelectric conversion element such as a CCD and a driving device for scanning the same, and performs scanning of a document. The image data read by the input sensor unit 10 is an AD converter
Sent to 11. Here, each pixel is converted into, for example, 6-bit digital data and quantized into data having 64 gradation levels. Next, in the correction circuit 12, shading distortion correction is performed to correct the sensitivity unevenness of the CCD and the light distribution characteristics of the light source. The data here is luminance data (0 (black) to 63
(White)), and the data is input to the conversion table 13 in order to convert this luminance data into density data.

FIG. 4 shows an example in which a ROM is used for the conversion table. 4th
The ROM shown in FIG. 2 is written with luminance data as input data and density data as output data. FIG. 5 shows an input / output correspondence table of the conversion table. That is, in FIG.
The luminance data from the correction circuit 12 is input to the addresses A0 to A5 of the ROM. Then, density data based on the input-output correspondence table of FIG. ₅ is output to the error correction circuit 15 from _{0-5 of} FIG. 4 as output data.

Next, the binarization process performed by the error correction circuit 15 and thereafter will be described.

In the error correction circuit 15, the output data X of the conversion table 13 is output.
_i, the error data E _i calculated in the error calculation circuit 17 to _{j, j}
Add. Here, the subscripts i and j indicate the i-th pixel data on the j-th line. The output data of the error correction circuit Z _i, when the _{j Z i, j = X i} , j + E i, j and expressed.

Z _{i, j} is sent to the binarization circuit 16, where Z _{i, j is set} to a threshold value TH.
Is converted to binary. That is, Z _{i, j} TH... P _{i, j} = 63 Z _{i, j} <TH... P _{i, j} = 0. Here, P _{i, j} is binary data.

Further, Z _{i, j} is also sent to the error calculation circuit 17 to calculate the error.

That is, when Z _{i, j} is larger than the threshold TH, α _{i, j} = (63−Z _{i, j} ) / 10 When Z _{i, j} is smaller than the threshold TH, α _{i, j} = (Z _{i, j} / 10) Is performed.

Further, α _{i, j} is weighted by an error matrix shown in FIG. 6 and fed back to the error correction circuit 35 as an error value E _{i, j} . The output Z _{i, j} from the error correction circuit 15
Is compared with a threshold value TH in a binarizing circuit 16, and a binary signal of 1,0 is sent to an AND circuit 18.

On the other hand, X _{i, j} is compared by a comparator 14 with a preset value. This set value is a threshold value, such as 3 or 4, for determining that all pixels are white if X _{i, j} is less than or equal to this value.
It is. If X _{i, j} is below this threshold, comparator 1
The signal of ▲ ▼ = 0 from 4 is output to the AND circuit 18. If the output from the comparator is 0, the AND circuit 18 outputs 0 to the encoding circuit 5 regardless of the output signal of the binarization circuit 16.

In the encoding circuit 5, based on the output from the AND circuit 18, MH
Alternatively, encoding by the MR method is performed. The encoding in the encoding circuit 5 can be performed efficiently in the highlight portion. In other words, the density data below the predetermined density is subjected to the binarization process by setting it to 0 irrespective of the binary output of the error diffusion method, so that even if the density of the pixel is actually low, the error data exceeds the threshold value. This is because the noise can be prevented and the granular noise in the highlight portion can be prevented.

The encoding circuit 5 has a line buffer for storing at least one line of binary data from the AND circuit 18 for encoding.

As described above, according to the above-described embodiment, if the pixel density processed by the error diffusion method is equal to or less than a certain value, the pixel is output as white regardless of the binary output of the error diffusion method.
It is possible to remove the graininess noise in the high contrast portion, which has the effect of improving the image quality. In addition, since the granular noise has been removed, the coding efficiency can be improved.

Next, another embodiment of the reading / binarizing circuit of FIG. 1 will be described with reference to FIG.

101 is an input sensor for reading the original, 102 is an AD converter,
103 is a correction circuit that performs shading distortion correction, and 104 is a conversion table that converts luminance data to density data.
5 is a comparator, 106 is an error correction circuit that adds an error value to the density of the original pixel, 107 is a binarization circuit that converts multivalued data into binary data by using a threshold value, 5 is an encoding circuit, and 109 is an error data operation 110 is a circuit that outputs 0 as an error, 111 is a switch that selects 109 and 110, 112 is a control circuit that controls the memory 114 and the like, 113 is a circuit that performs weighting, 114 is a memory, and 115 is an addition circuit. Circuit.

The input sensor unit 101 includes a photoelectric conversion element such as a CCD and a driving device for scanning the photoelectric conversion element, and performs scanning for reading a document. Image data read by the input sensor unit 101 is sent to the AD converter 102. Here, each pixel is converted into, for example, 6-bit digital data and quantized into data having 64 gradation levels. Next, in the correction circuit 103, shading distortion correction is performed to correct the sensitivity unevenness of the CCD and the light distribution characteristics of the light source. The data here is luminance data (0 (black) to 63
(White)), and the luminance data is input to the conversion table 104 in order to convert the luminance data into density data. FIG. 5 shows the values of this conversion table. This conversion process is the sixth
Use the ROM shown. That is, the ROM addresses A0 to A5 in FIG.
, And output data based on the conversion table shown in FIG. ₅ is output from outputs ₀ to _{5 of the} ROM.

The output data X _{i, j} is the density data (0 (white) to 63
(Black)).

Then the output data X _i of the error correction circuit 106 in the conversion table _104, the output data _i of the adder circuit 115 _j, a _j addition, the output Z _i, and _j. Here, the subscripts i and j indicate the i-th pixel data on the j-th line. That is, the output data Z of the error correction circuit
_{i and j} can be described as follows.

Z _{i, j} = X _{i, j} + _{i, j} Z _{i, j} is sent to the binarization circuit 107, where Z _{i, j} is compared with a threshold TH to convert it into binary data P _{i, j} . That is, Z _{i, j} TH... P _{i, j} = 63 Z _{i, j} <TH... P _{i, j} = 0 Then, the subtraction circuit 109 calculates the error from the output data Z _{i, j of the} error correction circuit. The output data P _{i, j} of the binarization circuit is reduced. That is, the output data of the subtraction circuit 109 is
_{If i, j} , it can be described as Ei _{, j} = Zi _{, j-} Pi _{, j} .

E _{i, j} is input to the switch 111. Also, switch 111
Is input from the circuit 110, and the circuit 110 always outputs 0.

The switch 111 selects and outputs one input data from two input data according to the control signal S _{i, j} . The control signal S _{i, j} is output from the comparator 105.
The density data X _{i, j} is smaller than the set value BTH, that is, the set value
If it is brighter than BTH, S _{i, j} becomes H and the set value BT
If it is larger than H, that is, darker than the set value BTH, S _{i, j} becomes L. Then, the S _i, S by _{j i, j} is the output data of H if switch 111 is the output signal of the circuit 110 is selected,
If S _{i, j} is L, the output data of the switch 109 is selected as the output data of the switch 111. This is because the input data is
If the brightness is less than H, it is determined that the human eyes feel white. Therefore, the error is forcibly set to 0 in such a high contrast portion so as not to output a black point.

The output data E _{i, j} (error) of the switch 111 is spatially diffused by the weighting circuit 113.

The weighting circuit 113 performs weighting using the error matrix shown in FIG. In this error matrix, the error is diffused to the i-th line and the (j + 1) -th line, so that the data B _{i, j} to be diffused to the (j + 1) -th line is stored in the memory 114.

When the processing pixel shifts to the next line, the memory 114
The data is read out. Then, the errors A _{i, j} and M _{i, j} in the currently processed line calculated by the weighting circuit 113 are calculated.
Is added by the adder circuit 115, and is fed back to the error correction circuit as the error value _{i, j} .

On the other hand, the output signal P _{i, j} of the binarization circuit 107 is sent to the coding circuit 5, and the coding is performed by the MH or MR method according to the value of the output signal P _{i, j} .

The encoding in the encoding circuit 5 can be performed efficiently in the highlight portion. That is, since the diffusion error in the error diffusion method is set to 0 for density data equal to or less than the predetermined density, even if the density of the pixel is actually low, it is possible to prevent the error data from exceeding the threshold, and the granularity in the highlight portion This is because noise can be prevented.

The encoding circuit 5 has a line buffer capable of storing at least one line of binary data from the AND circuit 18 for encoding.

As described above, according to the above-described embodiment, if the pixel density processed by the error diffusion method is equal to or lower than the predetermined value, the error data generated in the pixel is set to 0, thereby removing the granular noise in the high contrast portion. This makes it possible to improve the image quality. In addition, since the granular noise has been removed, the coding efficiency can be improved.

〔The invention's effect〕

As described above, according to the image processing method and apparatus of the present invention, it is possible to remove the grainy noise in the highlight portion generated in the binarization processing in the error diffusion method, and further improve the coding efficiency. There is.

[Brief description of the drawings]

FIG. 1 is a block diagram of a facsimile machine to which the present invention is applied, FIG. 2 is a diagram showing an example of a signal of one scanning line, and FIGS. 3 and 7 are reading / binarization of FIG. FIG. 4 shows the details of the circuit, and FIG. 4 shows the data of the luminance-density conversion table.
FIG. 5 is a diagram showing a ROM, FIG. 5 is a diagram showing a luminance-density data conversion table, and FIG. 6 is a diagram showing an example of an error diffusion matrix. 1 ... NCU, 2 ... phone 3 ... hybrid circuit, 4 ... modulator 5 ... encoding circuit, 6 ... reading / binarization circuit 7 ... demodulator, 8 ... decoding circuit 9 ... … Recording circuit 10,101… Input sensor section 11,102… AD converter 12,103… Correction circuit 13,104… Conversion table 14,105… Comparator 15,106… Error correction circuit 16,107… Binarization circuit 17… Error calculation circuit

Claims (3)

(57) [Claims] An input image density data is binarized by a density preserving type binarization processing method such as an error diffusion method and the input image density data is encoded when encoding the binarized data. If the input image density data is determined to be white in comparison with the value of, the binarized output of the input image density data is output as white regardless of the binarized output of the density preserving type binarization processing method. And an encoding method for encoding the data after the binarization processing. 2. An input image density data is binarized by a density preserving type binarization processing method such as an error diffusion method, and when the binarized data is encoded, the input image density data is converted to a predetermined value. If the input image density data is equal to or less than the predetermined value, the error between the input image density and the output image density after the binarization processing in the density preserving type binarization processing method is set to 0, and then An image processing method characterized by encoding data that has been binarized. A first binarizing means for binarizing the image data by an error diffusion method, a second binarizing means for binarizing the image data with a predetermined threshold value, Encoding means for encoding the binarized binary data; and deciding means for deciding final binary data based on a result of the binarization processing of the first and second binarization means. And the determining means determines that the image data is white and 2 by the second binarizing means.
When the binarization is performed, the final binary data is determined to be white regardless of the binarization result of the first binarization unit, and the image data is converted to black and binary by the second binarization unit. When the binarization is performed, the binarization result of the first binarization unit is determined as a final binary data value, and the encoding unit encodes the binary data finally determined by the determination unit. An image processing apparatus characterized in that: