Method and apparatus for binary coding of image data

Abstract: Error data Σe P accumulated for a target pixel of binary coding or pixel of interest P is read from an error memory (20) and inputted into an adder (22). The adder (22) adds the error data to multi-valued image data regarding the pixel of interest P, so as to correct the multi-valued image data. A comparator (24) compares the corrected multi-valued image data outputted from the adder (22) with a predetermined reference value and outputs binary image data based on a result of the comparison. A bit converter (28) converts the one-bit binary image data to 8-bit data. A subtracter (26) subtracts the binary-coded image data from the image data prior to binary coding to obtain an error 'e'. An error distribution circuit (30) distributes the error 'e' into pixels A, C, D, and E, where the pixels A, C, and D are respectively located on the immediate right of, immediately below, and on the lower left of the pixel of interest P and the pixel E is located on the immediate left of the pixel D. The error memory (20) adds distributed portions of the error e A , e C , e D , and e E to error data Σe A , Σe C , Σe D , and Σe E , so as to update the values of the error data Σe A , Σe C , Σe D , and Σe E . This method of distributing the error 'e' into the above peripheral pixels enables multi-valued image data for the pixel of interest P having a relatively low tone value or a relatively high tone value to be binary coded without damaging the quality of picture.

Description: BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method of binary coding multi-valued image data and also to an apparatus for the same. 2. Description of the Related Art An error diffusion method has widely been used for binary coding multi-valued image data, in order to record multi-tone images in a simulating manner with a binary recording device, such as an ink jet printer. Fig. 9 is a block diagram illustrating a conventional image data binary device for which the error diffusion method is applied. Referring to Fig. 9, the image data binary device includes an error memory 120, an adder 122, a comparator 124, a subtracter 126, a bit converter 128, and an error distribution circuit 130. Multi-valued image data has a dynamic range of 0 to 255. In the drawing of Fig. 9, the thick lines represent multi-bit data lines including symbol bit, integral bits, and decimal bits. The error diffusion method distributes an error, which arises as a difference between image data for a specific pixel before and after binary coding, into pixels in the vicinity of the specific pixel at a predetermined ratio. Figs. 10(a) and 10(b) show the principle of the error diffusion method. In the drawing of Fig. 10(a), the procedure of binary coding pixels 232 included in an image 230 is carried out in an order specified by an arrow 231. In order to binary code multi-valued image data regarding a target pixel P of binary coding (hereinafter referred to as pixel of interest), the multi-valued image data is first corrected by adding error data accumulated for the pixel of interest P to the multi-valued image data. When X denotes a value of multi-valued image data regarding the pixel of interest P and ∑e p represents error data accumulated for the pixel of interest P, a value X' of corrected multi-valued image data is given as: X' = X + ∑e P The error data accumulated for the pixel of interest P represents the total of errors distributed to the pixel of interest P from the peripheral pixels that have already undergone the binary coding as discussed later. The value X' of corrected multi-valued image data is then compared with a predetermined threshold value (for example, 128), so that the multi-valued image data regarding the pixel of interest P is binary coded. When Y (0 or 255) represents a value of binary-coded image data, an error 'e' arising by binary coding the multi-valued image data for the pixel of interest P is expressed as: e = X' - Y The value of the error 'e' is subsequently distributed at a predetermined ratio (ratio of error distribution) into pixels that are located in the vicinity of the pixel of interest P and have not yet undergone the binary coding. As an example of the conventional error diffusion method, the value of the error 'e' is distributed into four pixels in the vicinity of the pixel of interest P; that is, pixels A, B, C, and D located on the immediate right of, on the lower right of, immediately below, and on the lower left of the pixel of interest P as shown in Fig. 10(b). The ratio of error distribution applied is 7/16 to the pixel A, 1/16 to the pixel B, 5/16 to the pixel C, and 3/16 to the pixel D as shown below: The error distributed into the pixels prior to the binary coding is accumulated as the error data for each pixel in the error memory 120. The following describes the structure of the image data binary device shown in Fig. 9. The adder 122 receives multi-valued image data regarding the pixel of interest P. The error data ∑e P accumulated for the pixel of interest P is read from the error memory 120 and inputted into the adder 122. The adder 122 adds the error data to the input multi-valued image data to correct the multi-valued image data. The comparator 124 receives the corrected multi-valued image data from the adder 122, compares the corrected multi-valued image data with a separately input reference value, and outputs one-bit data or binary image data based on the results of comparison. This process binary codes the corrected multi-valued image data sent from the adder 122. The bit converter 128 converts the one-bit binary image data outputted from the comparator 124 to 8-bit binary image data (0 or 255). The subtracter 126 receives the corrected multi-valued image data (image data before the binary coding) outputted from the adder 122 and the image data (image data after the binary coding) outputted from the bit converter 128, subtracts the image data after the binary coding from the image data before the binary coding, and calculates the error 'e' arising in the process of binary coding the multi-valued image data for the pixel of interest P. The error distribution circuit 130 distributes the error 'e' obtained by the subtracter 126 into the pixels A, B, C, and D, which are located in the vicinity of the pixel of interest P and have not yet undergone the binary coding, at a predetermined distribution ratio as discussed above with the drawing of Fig. 10. In accordance with a concrete procedure, the error distribution circuit 130 multiplies the error 'e' by predetermined coefficients corresponding to the pixels A, B, C, and D, for example, those shown in Equations (3) given above, and adds the results of operations e A , e B , e C , and e D to error data Σe A , Σe B , Σe C , and Σe D for the pixels A, B, C, and D accumulated in the error memory 120, so as to update the values of the respective error data Σe A , Σe B , Σe C , and Σe D . The image data binary device shown in Fig. 9 utilizes the error diffusion method to binary code the multi-valued image data. This allows the image to be expressed in quasi multi-tones even when the multi-valued image data has been binary coded. In the conventional image data binary device, when the input multi-valued image data has a relatively low tone value, the pixels having the value '1' as the binary-coded image data are arranged as dotted lines in the image. When the input multi-valued image data has a relatively high tone value, on the other hand, the pixels having the value '0' as the binary-coded image data are arranged as dotted lines in the image. These dotted lines significantly deteriorate the quality of picture. When the image data binary device shown in Fig. 57 is applied, for example, to a printer, the tone value of the multi-valued image data corresponds to the density in the resulting recorder image. This means that the input of the multi-valued image data having a relatively low tone value gives a printed image of a relatively low density, whereas the input of the multi-valued image data having a relatively high tone value gives a printed image of a relatively high density. Figs. 11, 12, 13, and 14 show the printing results with a printer when the image data binary device of Fig. 9 is applied. Fig. 11 shows the printing result for a low-density area of 0% to 10%; Fig. 12 shows the printing result for a high-density area of 90% to 100%; Fig. 13 shows the printing result for a low-density area of 0% to 15%; and Fig. 14 shows the printing result for a high-density area of 85% to 100%. In these drawings, the density continuously varies in the respective ranges along the width of the image. The binary coding process in these examples is carried out in the manner shown in Fig. 10(a) ; that is, starting from the upper. left corner of the image and scanning from the left to the right one line by one line. This is also applied to the binary coding process in the examples below. Fig. 15 shows the printing result specifically selected for the density of 3% among the low-density area of Fig. 11; Fig. 16 shows the printing result specifically selected for the density of 97% among the high-density area of Fig. 12; Fig. 17 shows the printing result specifically selected for the density of 1% among the low-density area of Fig. 11; and Fig. 18 shows the printing result specifically selected for the density of 99% among the high-density area of Fig. 12. In these drawings, the whole image has a homogeneous density. As shown in Figs. 11, 13, 15, and 17, black dots (that is, the pixels having the value '1') are partially arranged as dotted lines running from the upper right to the lower left in the relatively low-density area of 0% to 5%. In a similar manner, as shown in Figs. 12, 14, 16, and 18, white dots (that is, the pixels having the value '0') are partially arranged as dotted lines running from the upper right to the lower left in the relatively high-density area of 95% to 100%. In either cases, the black dots or the white dots are not homogeneously dispersed and such dotted lines remarkably deteriorate the quality of picture. Fig. 19 shows the printing result with a printer for the whole density range of 0% to 100% when the image data binary device of Fig. 57 is applied. In the drawing of Fig. 19, the density continuously varies from the left to the right in the range of 0% to 100% along the width of the image. Fig. 20 shows the printing result with a printer for an intermediate-density range of 15% to 85% when the image data binary device of Fig. 9 is applied. In the drawing of Fig. 20, the density continuously varies in the above range along the width of the image. In the conventional image data binary device discussed above, when the input image data has an intermediate tone value, textures are often observed in the intermediate-density area of 15% to 85% in the image obtained as the binary-coded image data as shown in Figs. 19 and 20, thereby deteriorating the quality of picture. The textures herein denote patterns having some regularity and included in a planar distribution of depth. As disclosed in the paper 'Output Image Characteristics by Modified Error Diffusion (MED) method' , pp443-449, Bulletin of Image Electronics Society of Japan, Vol. 20, No. 5 (1991), the proposed method to prevent textures in the intermediate-density area adds dither data to the input image data prior to binary coding and varies the ratio of error distribution for distributing the value of the error 'e' at random according to random numbers. The known technique disclosed in the above paper depresses the occurrence of textures irrespective of the tone value of the input image data prior to binary coding. The processing is thus carried out even for the image data having a relatively low tone value or a relatively high tone value, wherein no significant textures are originally generated. This significantly deteriorates the quality of picture in the low-density area and in the high-density area. Another proposed method removes the textures observed in the intermediate-density area by adding a random number to a reference value used in the binary-coding means. This method, however, can not remove the textures observed in the low-density area or the high-density area. The textures observed in the low-density area or the high-density area can be improved by distributing the error, which arises in the process of binary coding the multi-valued image data for the pixel of interest, into a larger number of pixels which have not yet undergone the binary coding. This method, however, undesirably increases the scale of the hardware and lengthens the required processing time for binary coding. EP 0 531 170 A2 relates to a process, where an error term between non-coded and coded optical density values is determined and a preselected fraction of the error term on a weighted basis is distributed to a predetermined set of neighboring pixels. Other methods are described in the introductory part of said document, and error distribution schemes are addressed, but it is generally seen as a disadvantage, that procedures given there calculate errors that include all errors previously made. One of the error distribution schemes mentioned is described in US 5 313 287 in further detail in relation to figure 5A thereof, namely the adaptive algorithm developed by Floyd and Steinberg. The adaptive algorithm of Floyd and Steinberg is detailed in an article of Robert W. Floyd and Louis Steinberg, "An Adaptive Algorithm for Spatial Grayscale", XP 000616372: Proc SID, vol. 17, no. 2, April 1976, pp. 75 - 77. The error distribution method disclosed in EP 0 341 984 A2 is configured such that a surplus of an error generated when the error is dispersed with weighing in the error diffusion method is added to input image data of a next picture element. An Article of Liu Yong-Kui "2-level rendition of continuous-tone pictures", Displays, April 1991, pages 84, 85 describes a simplified algorithm which distributes 50 % of the intensity error of a picture element to the right and 50 % to the lower neighboring picture element to reduce the calculations needed to distribute the error. Various error matrix schemes are disclosed in EP 0 667 704 A2 and illustrated in figures 5(a) to 5(e). Similar error distribution matrices are shown in the Article of Reiner Eschbach "Reduction of artifacts in error diffusion by means of input-dependent weights", J. Electronic Imaging, Oct. 1993, Vol. 2(4), pp. 352 - 358 with differences in weightings. Similar matrices are also found in EP 0 696 129 A2. In the Article of Ping Wah Wong "Error Diffusion with dynamically adjusted Kernel", Proc. IEEE ICASSO, Adelaide, 19-22 April 1994, pp. 113 - 116 a scheme is used which, like the aforementioned method, corrects a value of a target pixel using an error valve accumulated for the target pixel, only. Finally, PCT/US90/07037 is concerned with electronic screening where a first screen matrix having a plurality of sub-elements having a threshold value pattern which grows around a target pixel is generated, wherein a second screen matrix having a plurality of sub-elements having a threshold value pattern which grows in a high spatial frequency is generated and then either the first matrix or the second matrix is selected to compare to an image signal value based on the tonal value of the picture element to achieve at optimum representation. SUMMARY OF THE INVENTION The object of the present invention is thus to provide an improved method of binary coding multi-valued image data without damaging the quality of picture as well as an improved apparatus for the same. In accordance with the present invention, a method of binary coding multi-valued image data for each pixel includes the steps of: (a) adding first error data accumulated for a target pixel of binary coding and at least part of second error data accumulated for a specific pixel which is located in the vicinity of the target pixel and which is to be processed by binary coding after the target pixel, so as to correct the multi-valued image data; (b) comparing the corrected multi-valued image data with a predetermined reference value and binary coding the corrected multi-valued image data based on the result of the comparison; (c) obtaining an error that is difference between the corrected multi-valued image data and the binary-coded image data for the target pixel; (d) distributing the error obtained for the target pixel into pixels which are to be processed by binary coding after the target pixel; (e) accumulating the distributed errors as error data for each pixel; and (f) repeating the steps (a) through (e) for successive the target pixels of said multi-valued image data. In the method of the present invention, the multi-valued image data for the target pixel is corrected by adding not only the error data accumulated for the target pixel but the error data accumulated for a specific pixel which is located in the vicinity of the target pixel and which is to be processed by binary coding after the target pixel. This remarkably improves the quality of the image expressed by the binary-coded image data. At least part of the above and the other related objects is also realized by an apparatus for binary coding multi-valued image data for each pixel which apparatus includes: correction means for adding first error data accumulated for a target pixel of binary coding and at least part of second error data accumulated for a specific pixel which is located in the vicinity of said target pixel and which is to be processed by binary coding after said target pixel, so as to correct the multi-valued image data; updating means for updating said first error data and said second error data according to a specified rule; binary coding means for comparing the corrected multi-valued image data of the target pixel with a predetermined reference value and binary coding the corrected multi-valued image data based on the result of the comparison; error calculation means for obtaining an error that is difference between the corrected multi-valued image data and the binary-coded image data for said target pixel; error distribution means for distributing the error obtained for said target pixel into pixels which are to be processed by binary coding after said target pixel; and error accumulation means for accumulating for each pixel the distributed errors as error data thereof. In the apparatus of the present invention, the multi-valued image data for the target pixel is corrected by adding not only the error data accumulated for the target pixel but the error data accumulated for a specific pixel which is located in the vicinity of said target pixel and which is to be processed by binary coding after said target pixel. This remarkably improves the quality of the image expressed by the binary-coded image data. These and other objects, features, aspects, and advantages of the present invention will become more apparent from the following detailed description of the preferred embodiments with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 is a block diagram illustrating an image data binary device as an embodiment according to the present invention; Fig. 2 is a flowchart showing an image data binary coding routine executed by the image data binary device of Fig. 1; Figs. 3(a) through 3(c) show an error diffusion process applied for the embodiment; Fig. 4 shows the printing result with a printer for a low-density area of 0% to 15% when the image data binary device of Fig. 1 is applied; Fig. 5 shows the printing result with a printer for a high-density area of 85% to 100% when the image data binary device of Fig. 1 is applied; Fig. 6 shows the printing result with a printer for the density of 1% when the image data binary device of Fig. 1 is applied; Fig. 7 shows the printing result with a printer for the density of 99% when the image data binary device of Fig. 1 is applied; Fig. 8 shows the printing result with a printer for an intermediate-density area of 15% to 85% when the image data binary device of Fig. 1 is applied; Fig. 9 is a block diagram illustrating a conventional image data binary device; Figs. 10 (a) and 10(b) show the principle of the conventional error diffusion method; Fig. 11 shows the printing result with a printer for a low-density area of 0% to 10% when the conventional image data binary device is applied; Fig. 12 shows the printing result with a printer for a high-density area of 90% to 100% when the conventional image data binary device is applied; Fig. 13 shows the printing result with a printer for a low-density area of 0% to 15% when the conventional image data binary device is applied; Fig. 14 shows the printing result with a printer for a high-density area of 85% to 100% when the conventional image data binary device is applied; Fig. 15 shows the printing result with a printer for the density of 3% when the conventional image data binary device is applied; Fig. 16 shows the printing result with a printer for the density of 97% when the conventional image data binary device is applied; Fig. 17 shows the printing result with a printer for the density of 1% when the conventional image data binary device is applied; Fig. 18 shows the printing result with a printer for the density of 99% when the conventional image data binary device is applied; Fig. 19 shows the printing result with a printer for the whole density range of 0% to 100% when the conventional image data binary device is applied; and Fig. 20 shows the printing result with a printer for an intermediate-density range of 15% to 85% when the conventional image data binary device is applied. DESCRIPTION OF THE PREFERRED EMBODIMENTS Modes of carrying out the present invention are described as preferred embodiments. Fig. 1 is a block diagram illustrating an image data binary device as an embodiment according to the present invention. Referring to Fig. 1, the image data binary device of the embodiment includes an error memory 20, a comparator 24, a subtracter 26, a bit converter 28, an error distribution circuit 30, a data correction circuit 34, and an error updating circuit 36. Fig. 2 is a flowchart showing an image data binary coding routine executed by the image data binary device of Fig. 1. Prior to the description of the image data binary device shown in Fig. 1, the processing executed in the third embodiment is described briefly. Figs. 3(a) through 3(c) show an error diffusion process applied for the third embodiment. In the drawing of Fig. 3(a), the procedure of binary coding pixels 222 included in an image 220 is carried out in an order specified by an arrow 221. In the embodiment, before multi-valued image data regarding a target pixel of binary coding or pixel of interest P undergoes the binary coding process, the multi-valued image data is corrected by adding error data ∑e P accumulated for the pixel of interest P and part of the error data accumulated for a pixel that is located in the vicinity of the pixel of interest P and has not yet undergone the binary coding process to the multi-valued image data for the pixel of interest P. In accordance with a concrete procedure, a pixel E is selected as a pixel that is located in the vicinity of the pixel of interest P and has not yet undergone the binary coding process, as shown in Fig. 3(b). The whole value of error data ∑e P accumulated for the pixel of interest P and one eighth the value of error data ∑e E accumulated for the pixel E are added to the multi-valued image data regarding the pixel of interest P. When X represents the value of the multi-valued image data regarding the pixel of interest P, a value X'' of corrected multi-valued image data is given as: X'' = x + ∑e P + 1/8∑e E After the correction is completed for the multi-valued image data regarding the pixel of interest P, the values of the error data accumulated for the pixels P and E in the error memory 20 are updated in accordance with the following Equations (8): ∑e P → 0 ∑e E → 7/8∑e E The value X' ' of corrected multi-valued image data is then compared with a predetermined threshold value (for example, 128), so that the multi-valued image data regarding the pixel of interest P is binary coded. When Y (0 or 255) represents a value of binary-coded image data, an error 'e' arising by binary coding the multi-valued image data for the pixel of interest P is expressed as: e = X'' - Y The value of the error 'e' is then distributed at a predetermined ratio (ratio of error distribution) to pixels A, B, C, and D that are located in the vicinity of the pixel of interest P and have not yet undergone the binary coding process as shown in Fig. 3(c). The errors distributed to the pixels prior to the binary coding are accumulated as error data for each pixel in the error memory 20. The following describes the processes carried out in the image data binary device shown in Fig. 1, based on the drawings. In case that P is the pixel of interest in the embodiment of Fig. 1, the data correction circuit 34 receives multi-valued image data regarding the pixel P. The multi-valued image data is, for example, 8-bit data and may take any value of 0 to 255 as the tone value. The error data ∑e P distributed from the binary-coded peripheral pixels and accumulated for the pixel of interest P and the error data ∑e E distributed from the binary-coded peripheral pixels and accumulated for the pixel E are read from the error memory 20 and also inputted into the data correction circuit 34. When the program enters the routine of Fig. 2, the data correction circuit 34 carries out a correction process at step S50. Namely, the data correction circuit 34 corrects the input multi-valued image data for the pixel of interest P by adding the error data ∑e P accumulated for the pixel of interest P and part of the error data ∑e E accumulated for the pixel E, that is, 1/8∑e E , to the multi-valued image data. When the error data ∑e P and ∑e E are read out, the error updating circuit 36 updates the error data ∑e P and∑e E stored in the error memory 20 respectively to 0 and 7/8∑e E according to Equations (8) given above at step S51. In a binary coding process executed at step S52, the comparator 24 receives the corrected multi-valued image data from the data correction circuit 34, compares the corrected multi-valued image data with a separately input reference value, and outputs one-bit data or binary image data based on the results of comparison. The value '128' that is an intermediate value between the value '0' and the value 255' is used as the reference value. The comparator 24 outputs the value '1' when the value of the corrected multi-valued image data outputted from the data correction circuit 34 is equal to or greater than the reference value '128'. The comparator outputs the value '0', on the other hand, when the value of the corrected multi-valued image data is smaller than the reference value '128'. This process binary codes the corrected multi-valued image data outputted from the data correction circuit 34. The bit converter 28 converts the one-bit binary image data outputted from the comparator 24 to 8-bit binary image data. When the value of the image data outputted from the comparator 24 is equal to '0', the bit converter 28 outputs data of the value '0' as the 8-bit image data. When the value of the image data outputted from the comparator 24 is equal to '1', on the contrary, the bit converter 28 outputs data of the value '255' as the 8-bit image data. In an error calculation process executed at step S54, the subtracter 26 receives the corrected multi-valued image data (image data before the binary coding) outputted from the data correction circuit 34 and the image data (image data after the binary coding) outputted from the bit converter 28, subtracts the image data after the binary coding from the image data before the binary coding, and calculates the error 'e' arising in the process of binary coding the multi-valued image data for the pixel of interest P. In an error distribution process executed at subsequent step S56, the error distribution circuit 30 distributes the error 'e' obtained by the subtracter 26 into the pixels A, B, C, and D, which are located in the vicinity of the pixel of interest P and have not yet undergone the binary coding, as discussed in Fig. 3(c). An error accumulation process executed at step S58 then accumulates the distributed errors into the error memory 20 as error data for each pixel. In accordance with a concrete procedure, the error distribution circuit 30 multiplies the error 'e' by predetermined coefficients corresponding to the pixels A, B, C, and D, for example, those shown in Equations (3) given above, and adds the results of operations e A , e B , e C , and e D to error data Σe A , Σe B , Σe C , and Σe D for the pixels A, B, C, and D accumulated in the error memory 20, so as to update the values of the respective error data Σe A , Σe B , Σe C , and Σe D . After the series of processes are concluded for the pixel of interest P, the target of the binary coding shifts from the pixel P to the next pixel A and the same processes are repeated for the next pixel of interest A. Figs. 4 and 5 respectively show the results of printing with a printer when the image data binary device of Fig. 1 is applied. Fig. 4 shows the printing result for a low-density area of 0% to 15%, whereas Fig. 5 shows the printing result for a high-density area of 85% to 100%. In these drawings, the density continuously varies in the respective ranges along the width of the image. Fig. 6 shows the printing result specifically selected for the density of 1% among the low-density area of Fig. 4, and Fig. 7 shows the printing result specifically selected for the density of 99% among the high-density area of Fig. 5. As shown in Figs. 6 and 7, the structure of the embodiment enables the black dots and white dots to be arranged in a substantially homogeneous manner, thus remarkably improving the quality of picture. Fig. 8 shows the printing result with a printer for an intermediate-density area of 15% to 85% when the image data binary device of Fig. 1 is applied. There is no significant difference between the printing result of Fig. 8 by the method of the third embodiment and that of Fig. 20 by the conventional process in this intermediate-density area. In the embodiment, the multi-valued image data for the pixel of interest P is corrected by adding a fixed fraction (1/8 in the embodiment) of the error data ∑e E accumulated for the pixel E to the multi-valued image data. The fraction (ratio of addition) can be set suitably corresponding to the multi-valued image data regarding the pixel of interest. This further improves the quality of the binary-coded image and shortens the required time for binary coding.

Claims: A method of binary coding multi-valued image data for each pixel, said method comprising the steps of: (a) adding (S50) first error data (Σ eP ) accumulated for a target pixel (P) of binary coding and at least part of second error data (Σe E ) accumulated for a specific pixel (E) which is located in the vicinity of said target pixel and which is to be processed by bi-nary coding after said target pixel, so as to correct (34) the multi-valued image data (X); (b) comparing (S52) the corrected multi-valued image data with a predetermined reference value and binary coding (24) the corrected multi-valued image data (X") based on the result of the comparison; (c) obtaining(S54) an error that is the difference between the corrected multi-valued image data (X") and the binary-coded image data for said target pixel; (d) distributing (S56) the error (S56) obtained for said target pixel (P) into pixels (A-D) which are to be processed by binary coding after said target pixel; (e) accumulating (S58) the distributed errors as error data for each pixel; and (f) repeating said steps (a) through (e) for successive target pixels of said multi-valued image data. A method in accordance with claim 1, wherein said step (a) comprises the step of up-dating (S51) said first error data (Σe P ) and said second error data (Σe E ) according to a specified rule, after said adding. A method in accordance with claim 1, wherein said step (a) comprises the steps of: (a-1) multiplying said second error data by a coefficient K (0≤K≤1) determined depending upon the multi-valued image data for said target pixel to obtain a product data; and (a-2) adding said first error data and said product data to the multi-valued image data for said target pixel to correct the multi-valued image data. An apparatus for binary coding multi-valued image data for each pixel, said apparatus comprising: correction means (34) for adding first error data (∑e P ) accumulated for a target pixel (P) of binary coding and at least part of second error data (∑e E ) accumulated for a specific pixel (E) which is located in the vicinity of said target pixel and which is to be processed by binary coding after said target pixel, so as to correct the multi-valued image data (X); updating means (36) for updating said first error data and said second error data according to a specified rule; binary coding means (24) for comparing the corrected multi-valued image data of the target pixel with a predetermined reference value and binary coding the corrected multi-valued image data (X") based on the result of the comparison; error calculation means (26) for obtaining an error that is difference between the corrected multi-valued image data (X") and the binary-coded image data for said target pixel; error distribution means (30) for distributing the error obtained for said target pixel (P) into pixels (A-D) which are to be processed by binary coding after said target pixel; and error accumulation means (30, 20) for accumulating for each pixel the distributed errors as error data thereof.

Inventors: IKUTA KUNIO, IKUTA, KUNIO, Ikuta, Kunio, c/o Dainippon Screen Mfg. Co., Ltd.
Applicants: DAINIPPON SCREEN MFG, DAINIPPON SCREEN MFG. CO., LTD.
Application year: 1997
Main classification: H04N1
IPC codes: H04N1, G06T9