JP2005045353A - Image processing apparatus, image processing method, and image processing program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image processing apparatus or the like capable of obtaining a color image or a grey level image wherein color unevenness is reduced and local variations in the lightness are suppressed. <P>SOLUTION: The image processing apparatus 1 includes an arithmetic processing section 2 for quantizing multi-level image data respectively associated with RGB colors and converting the data into output image data of a pseudo medium tone. First the arithmetic processing section 2 applies first quantization to a target pixel of the multi-level image data associated with the blue color to decide a dot incidence pattern C<SB>(B)i,j</SB>[k]. Then the arithmetic processing section 2 applies second quantization to a target pixel of the multi-level image data associated with the green color to decide a dot incidence pattern C<SB>(G)i,j</SB>[k] so as to increase inverse correlation with respect to the a dot incidence pattern C<SB>(B)i,j</SB>[k] decided by the first quantization. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

具体例として、Ｗ_{（ｃｏｌ）ｉ，ｊ}＝６４、Ｗ_{（ｃｏｌ）ｉ＋}δ_，ｊ＝３２としたときの出力結果を図２（ｅ）に示す。この図から分かるように、周辺画素のドット出現パターンを加算する場合には、ブルーのドットとグリーンのドットとの間で相関をより確実に調整し、色むらやざらつき感を低減するとともに、各色のドット位置に適度のランダム性を付与することができる。
【０１２４】
また、画像処理装置１は、ＲＧＢに関する複数色の多階調画像データを処理するものとして説明したが、Ｙ（イエロー），Ｍ（マゼンタ），Ｃ（シアン）などの３色の多階調画像データや、Ｙ，Ｍ，Ｃ，Ｋ（ブラック）の４色の多階調画像データなどを処理するものとしても良い。
この場合には、明度の低い色、つまり視認性の高い色の多階調画像データから逆相関型のデジタルハーフトーニング処理を行うことが好ましい。これにより、明度の高い順に出力用画像データを生成する場合に比べて、視認性の高い色のドットを確実に分散させることができるため、視覚的に色むらやざらつき感の少ない画像を得ることができる。
さらに、例えばＣＭＹの順で注目画素の処理を行う場合には、最も明度が高く視認性の低いイエローのドットは他の色のドットと重なっても視覚的に影響が少ないため、イエローのヒストグラムＨ_{（Ｙ）ｉ，ｊ}［ｋ］にシアン、マゼンタのドット出現パターンＣ_{（Ｃ）ｉ，ｊ}［ｋ］，Ｃ_{（Ｍ）ｉ，ｊ}［ｋ］をそれぞれ加算する際の重みＷの量を小さくしても良い。このように、先にドット出現パターンＣ_{（ｃｏｌ）ｉ，ｊ}［ｋ］が決定される色と、現在ドット出現パターンＣ_{（ｃｏｌ）ｉ，ｊ}［ｋ］を決定中の色との関係に基づいて重みＷの量を設定することにより、視認性の異なる複数色のドットを適切に分散させて配置することができる。なお、上記の添え字「（ｃｏｌ）」は何れかの色を表すものである。
【０１２５】
また、図１０及び図１１に示した６つの基礎フィルタＫ_１〜Ｋ_６の各画素の画素値は変更可能であり、必ずしも図１０及び図１１に示した通りの画素値である必要はない。また、ローカルフィルタＰの生成には、必ずしも図９を用いる必要はない。更に、ローカルフィルタＰは、基礎フィルタＫ_１〜Ｋ_６から計算しなくても良く、例えば入力画素値に応じたローカルフィルタＰを予め記憶したテーブルを用いることとしても良い。
【０１２６】
［第２の実施の形態］
続いて、第２の実施の形態における画像処理装置１について説明する。なお、上記第１の実施の形態と同一の構成要素には同一の符号を付し、その説明を省略する。
【０１２７】
本実施の形態における画像処理装置１は、上記第１の実施の形態で説明した画像処理装置１と同様の構成を有しているが、複数の濃淡色に関する多階調の白黒画像データを量子化処理し、擬似中間調の出力用画像データを生成するようになっている。
具体的には、この画像処理装置１は、例えば図５に示すように、濃淡分解テーブルを用いて２５６階調、つまり８ビットの白黒画像データを濃色画像データと淡色画像データとに濃淡分解した後、まず濃色画像データについて逆相関型のデジタルハーフトーニング（第１量子化）処理を施し、続いて濃ドットのドット出現パターンを下記の式のように用いて淡色画像データについて逆相関型のデジタルハーフトーニング（第２量子化）処理を施すことにより、３値化された出力用画像データを生成するようになっている。
Ｈ_{（ｌｉｇｈｔ）ｉ，ｊ}［ｋ］＋＝Ｗ＊Ｃ_{（ｄａｒｋ）ｉ，ｊ}［ｋ］
なお、濃淡分解テーブルとしては、例えば図６に示すものがある。また、上記の式中、「Ｈ_{（ｌｉｇｈｔ）ｉ，ｊ}［ｋ］」は淡ドットのドット出現パターン算出のためのヒストグラムであり、「Ｃ_{（ｄａｒｋ）ｉ，ｊ}［ｋ］」は濃ドットのドット出現パターンである。また、「Ｗ」は正の定数である。
【０１２８】
ここで、重みＷの量は、ｉ行ｊ列目の画素に関して先に処理された濃色画像データの画素値に応じて調整することが好ましい。具体的には、濃色画像データの画素値が小さい場合には、重みＷの量を０に近づけることによって濃ドットと淡ドットとをランダムに重ならせ、画素値が大きい場合には、重みＷの量を大きくすることによって濃ドットと淡ドットとのランダムな重なりを低減することが好ましい。
【０１２９】
この画像処理装置１によれば、濃淡のドットの出現をそれぞれ独立に決める場合に比べ、濃ドットと淡ドットとの分散が良くなり、ランダムな重なりが低減されるため、濃淡ドットの混じり方のむらや、ざらつき感を低減することができる。
【０１３０】
なお、上記第２の実施の形態においては、逆相関型のデジタルハーフトーニング処理を施す前に白黒画像データを濃色画像データと淡色画像データとの２つの画像データに濃淡分解することとして説明したが、３つ以上の画像データに濃淡分解することとしても良い。この場合には、明度が低い順に、つまり濃度が高い順に画像データを逆相関型のデジタルハーフトーニング処理し、明度の高い画像データについての逆相関型のデジタルハーフトーニング処理においては、明度が低い色のドット出現パターンＣ_{（ｄａｒｋ）ｉ，ｊ}［ｋ］ほど重みＷの量を大きくしてドット出現パターンＣ_{（ｌｉｇｈｔ）ｉ，ｊ}［ｋ］の算出に用いることが好ましい。これにより、明度が低く、視認性の高いドットほど分散性を向上させることができるため、視覚的に色むらやざらつき感の少ない画像を得ることができる。
【０１３１】
［第３の実施の形態］
続いて、第３の実施の形態における画像処理装置１について説明する。なお、上記第１の実施の形態と同一の構成要素には同一の符号を付し、その説明を省略する。
【０１３２】
本実施の形態における画像処理装置１は、上記第１の実施の形態で説明した画像処理装置１と同様の構成を有しているが、カラーの多階調画像データを処理し、色毎に多値化されたカラーの出力用画像データを生成するようになっている。具体的には、この画像処理装置１は、例えば図７に示すように、ＹＭＣＫの色毎に２５６階調を有する画像データから、濃色のブラック、シアン、マゼンタ、イエローのデータと、淡色のブラック、シアン、マゼンタのデータとを生成した後、何れか１色のデータについて逆相関型のデジタルハーフトーニング処理を施し、続いて、既に決定されたドット出現パターンを用いて２色目以降のデータについて逆相関型のデジタルハーフトーニング処理を施すことにより、イエローについて２値化され、ブラック、シアン、マゼンタについて３値化された出力用画像データを生成するようになっている。なお、逆相関型のデジタルハーフトーニング処理する順番としては、各色の明度に応じて濃色のブラック、濃色のシアン、濃色のマゼンタ、淡色のブラック、淡色のシアン、淡色のマゼンタ、濃色のイエローの順としても良いし、同系統の色同士でドットの分散性を良くするべく、濃色のブラック、淡色のブラック、濃色のシアン、淡色のシアン、濃色のマゼンタ、淡色のマゼンタ、濃色のイエローの順に出力用画像データを生成しても良い。また、各色の画像データの濃淡分解には、４次元のルックアップテーブルと補間演算との組み合わせや、前記濃淡分解テーブル等を用いることができる。
【０１３３】
この画像処理装置１によれば、異なる色及び濃度のドット間で相関が調整されるので、従来と異なり、色むらや濃度むら、ざらつき感を低減し、かつ明度の局所的な変動を抑えたＹＭＣＫ画像を得ることができる。
【０１３４】
【発明の効果】
請求項１，８，１５記載の発明によれば、従来と異なり、色むらやざらつき感を低減し、かつ明度の局所的な変動を抑えたカラー画像を得ることができる。
【０１３５】
請求項２，９，１６記載の発明によれば、請求項１，８，１５記載の発明と同様の効果が得られるのは勿論のこと、一層ざらつき感を低減し、かつ明度の局所的な変動を押さえたカラー画像を得ることができる。
【０１３６】
請求項３，１０，１７記載の発明によれば、請求項１，２，８，９，１５，１６記載の発明と同様の効果が得られるのは勿論のこと、異なる色のドット間での相関を容易に調整することができる。
請求項４，１１，１８記載の発明によれば、請求項１〜３，８〜１０，１５〜１７の何れか一項に記載の発明と同様の効果が得られるのは勿論のこと、色むらやざらつき感を低減し、かつ明度の局所的な変動を抑えたカラー画像を得ることができる。
【０１３７】
請求項５，１２，１９記載の発明によれば、請求項１〜４，８〜１１，１５〜１８の何れか一項に記載の発明と同様の効果が得られるのは勿論のこと、視覚的に色むらやざらつき感の少ない画像を得ることができる。
【０１３８】
請求項６，１３，２０記載の発明によれば、ＲＧＢに関する多階調画像データについて、請求項１〜５，８〜１２，１５〜１９の何れか一項に記載の発明と同様の効果を得ることができる。
【０１３９】
請求項７，１４，２１記載の発明によれば、ＹＭＣＫに関する多階調画像データについて、請求項１〜５，８〜１２，１５〜１９の何れか一項に記載の発明と同様の効果を得ることができる。
【０１４０】
請求項２２，２７，３２記載の発明によれば、従来と異なり、濃度むらやざらつき感を低減し、かつ明度の局所的な変動を抑えた濃淡画像を得ることができる。
【０１４１】
請求項２３，２８，３３記載の発明によれば、請求項２２，２７，３２記載の発明と同様の効果が得られるのは勿論のこと、一層ざらつき感を低減し、かつ明度の局所的な変動を押さえた濃淡画像を得ることができる。
【０１４２】
請求項２４，２９，３４記載の発明によれば、請求項２２，２３，２７，２８，３２，３３記載の発明と同様の効果が得られるのは勿論のこと、異なる濃度のドット間での相関を容易に調整することができる。
請求項２５，３０，３５記載の発明によれば、請求項２２〜２４，２７〜２９，３２〜３４記載の発明と同様の効果が得られるのは勿論のこと、濃度むらやざらつき感を低減し、かつ明度の局所的な変動を抑えた濃淡画像を得ることができる。
【０１４３】
請求項２６，３１，３６記載の発明によれば、請求項２２〜２５，２７〜３０，３２〜３５の何れか一項に記載の発明と同様の効果が得られるのは勿論のこと、視覚的に濃度むらやざらつき感の少ない画像を得ることができる。
【図面の簡単な説明】
【図１】本発明に係る画像処理装置の概略構成を示すブロック図である。
【図２】（ａ）は多階調画像データの出力画像を示す図であり、（ｂ），（ｄ），（ｅ）は本発明に係る画像処理方法を用いた場合の出力画像を示す図であり、（ｃ）従来の画像処理方法を用いた場合の出力画像を示す図である。
【図３】画像処理装置の演算処理部が実行する１色目の逆相関型のデジタルハーフトーニング処理を経時的に示したフローチャートである。
【図４】画像処理装置の演算処理部が実行する２色目以降の逆相関型のデジタルハーフトーニング処理を経時的に示したフローチャートである。
【図５】白黒画像データから、３値化された出力画像データを生成する手順を示す図である。
【図６】濃淡分解テーブルを示す図である。
【図７】カラーの多階調画像データから、各色について３値化された出力画像データを生成する手順を示す図である。
【図８】従来の逆相関型のデジタルハーフトーニング法による画像処理を経時的に示したフローチャートである。
【図９】画素値に基づく値ΔとローカルフィルタＰの情報とを対応づけた表である。
【図１０】基礎フィルタＫ１〜Ｋ３を示す図面である。
【図１１】基礎フィルタＫ４〜Ｋ６を示す図面である。
【図１２】ローカルフィルタＰ（＝Ｒ（Ｋ６，４，−１）を示す図面である。
【図１３】ヒストグラムＨ_ｉ，ｊ［ｋ］の生成を説明するための図面である。
【符号の説明】
１ 画像処理装置
２ 演算処理部[0001]
BACKGROUND OF THE INVENTION
The present invention relates to an image processing apparatus, an image processing method, and an image processing program that quantize multi-tone image data and convert the image data into pseudo-halftone output image data.
[0002]
[Prior art]
Conventionally, a systematic dither method, a blue noise mask method, an error diffusion method, and the like are known as quantization processing methods for converting multi-tone original image data into pseudo-halftone output image data. Among these, since the error diffusion method can obtain a relatively high image quality, it is often used for an application for obtaining a high-quality image, and various improvements have been made. The problem of the image quality of the error diffusion method is how to reduce the texture peculiar to the algorithm without amplifying the noise.
[0003]
For example, Patent Document 1 attempts to ensure uniformity using a plurality of error diffusion matrices. Specifically, two matrices are switched according to the input value, a large matrix is used in highlights and shadows to prevent worms, and a small matrix is used in mid-ranges to suppress noise.
[0004]
In Patent Document 2, in order to generate a more uniform result in the highlight and shadow regions, the threshold value is changed using a threshold value that depends on the output value. Specifically, whether the binarized output is white or black, the threshold value change according to the input is performed on the surrounding pixels, and this is repeatedly propagated and used. In addition, a method for optimizing the size and coefficient of the error diffusion matrix, a change in the processing order (scanning direction), and the like have been performed. Although some effects could be obtained from these methods, an effect that could be called sufficient could not be obtained.
[0005]
On the other hand, an inverse correlation type digital halftoning method has been proposed as a quantization processing method different from the error diffusion method, its improved method, dither method, and the like (see Non-Patent Document 1). In this method, dot presence information for each pixel is arranged (hereinafter referred to as a dot appearance pattern), that is, dot presence / absence information is arranged in a number substantially proportional to the number of gradations constituting the target pixel. This is a method of halftoning based on the information, and in determining the content of the dot appearance pattern for the target pixel, the dot appearance pattern already determined for the peripheral pixels of the target pixel is used. For each element number of the elements that make up the pattern, the expected value (hereinafter referred to as a histogram) that dots appear in the surrounding pixels is calculated. Based on this expected value, priority is given to dot presence information for element numbers with a small number of dot appearances. This is a method of determining the dot appearance pattern of the target pixel so that no-dot information is set for element numbers with a large number of dot appearances. .
[0006]
The inverse correlation type digital halftoning method will be described below with a specific example. In the following description, the process is performed by switching the target pixel in the direction from left to right.
As shown in FIG. 8, in the image processing by the inverse half-correlation type digital halftoning method, the random variable r () is randomly selected from the range of 0 to (n−1) in advance before the original image data is input. r is an integer) (step T1). “N” is the pixel value g_{i, j}Is the maximum value. Pixel value g_{i, j}Is a gradation value in the pixel in the i-th row and the j-th column, and here is any value in the range of 0 to n.
When the original image data is input, the pixel value g regarding the pixel in the i-th row and j-th column from the original image data_{i, j}Is acquired (step T2).
[0007]
Pixel value g_{i, j}Is obtained, its pixel value g_{i, j}Based on the above, a local filter P of the pixel in the i-th row and j-th column is generated (step T3). Specifically, first, the pixel value g_{i, j}9 is substituted into the following formula (1) to determine Δ, which range in the left column in FIG. 9 is specified, and information on the local filter P corresponding to the specified range is obtained from the right column in FIG. Identify. In the figure, for example, “Δ∈ [0, 13/255)” represents 0 ≦ Δ <13/255.
Δ = | g_{i, j}−n / 2 | / n (1)
[0008]
For example, the pixel value g_{i, j}9 is derived from the above equation (1), Δ = | 120−255 / 2 | /255=7.5/255, and this Δ (= 7.5 / 255) is the left column in FIG. Can be identified as belonging to Δ∈ [0, 13/255) described in the uppermost column. Then, R (K1, 6, 5) in a column arranged on the right side of the specified column in which Δ∈ [0, 13/255) is described can be specified as information on the local filter P.
[0009]
Next, when the specified information is generalized for convenience to be “R (K, lk, ε (lk))”, it is shown in FIG. 10 and FIG. 11 by referring to “K” first. 6 basic filters K₁~ K₆Select either of these. Next, by referring to “lk”, (lk−1) pixels are spread in the upward direction, the left direction, and the right direction from the pixel marked with X in the basic filter K, that is, the pixel in the i-th row and j-th column. Produces a filter of size lk rows × (2lk−1) columns. Then, referring to “lk” and “ε (lk)”, the pixel values of the basic filter K are used as they are for the pixels from the first column to the (lk−ε (lk)) column in the generated filter. The local filter P is generated by allocating and assigning 0 to each pixel in the column earlier than the (lk−ε (lk)) column.
[0010]
For example, the information of the local filter P is R (K₆, 4, -1), the local filter P is generated by the following procedure. That is, R (K₆, 4, -1) corresponds to the basic filter K "K₆Therefore, the three basic filters K in FIG.₄~ K₆Basic filter K shown in the lower row₆Is specified as the basic filter K. And R (K₆, 4, -1) is “4” corresponding to lk and “−1” corresponding to ε (lk). First, the basic filter K in the lower stage in FIG.₆A filter having a size of 4 rows × 7 (= 2 × 4-1) columns having a spread of 3 (= lk−1) pixels in the upward direction, the left direction, and the right direction is generated from the pixels marked with “×”. After that, in the generated 4 × 7 column filter, each pixel from the first column to the fifth (= (4-(− 1))) column has a basic filter K.₆Are assigned as they are, and 0 is assigned to each pixel in the columns beyond the fifth column. FIG. 12 shows the local filter P generated by such a procedure.
[0011]
When the local filter P is generated, the histogram H of the pixel in the i-th row and j-th column_{i, j}[K] is calculated (step T4). "Histogram H_{i, j}“[K]” means that the dot appearance pattern C of each pixel around the pixel of interest is represented by (x, y) when the arrangement position of the pixel around the pixel of interest shown by the x mark in the local filter P is expressed._{x, y}[K] is a total value obtained by weighting the value of the position (x, y) of the local filter P and adding each value of the element k. However, “k” is an arbitrary integer value from 0 to (n−1), and the dot appearance pattern C_{x, y}It is synonymous with the element k of [k]. In addition, “dot appearance pattern C_{i, j}“[K]” means C or 0 or 1 respectively._{i, j}[0] to C_{i, j}It is a numerical sequence composed of [n−1]. This dot appearance pattern C_{i, j}[K] indicates that if any value from 0 to (n−1) is assigned to the element k and becomes “1”, a dot is formed in the pixel in the i-th row and j-th column, and “0”. Indicates that no dots are formed.
[0012]
For example, when the local filter P shown in FIG. 12 is generated, the position (x, y) of each pixel in the local filter P, the pixel value p of each pixel in the local filter P_{x, y}Is set as shown in FIGS. 13A and 13B, the histogram H of the pixel of interest (the pixel marked with x in FIGS. 12 and 13) in the i-th row and j-th column._{i, j}[K] is calculated for each value of the element k according to the following equation.
H_{i, j}[0] = C_{x1, y1}[0] × p_{x1, y1}+ C_{x1, y2}[0] × p_{x1, y2}+ C_{x1, y3}[0] × p_{x1, y3}+… + C_{x4, y3}[0] × p_{x4, y3}
H_{i, j}[1] = C_{x1, y1}[1] × p_{x1, y1}+ C_{x1, y2}[1] × p_{x1, y2}+ C_{x1, y3}[1] x p_{x1, y3}+… + C_{x4, y3}[1] × p_{x4, y3}
H_{i, j}[2] = C_{x1, y1}[2] x p_{x1, y1}+ C_{x1, y2}[2] x p_{x1, y2}+ C_{x1, y3}[2] x p_{x1, y3}+… + C_{x4, y3}[2] x p_{x4, y3}
...
...
H_{i, j}[N-1] = C_{x1, y1}[N-1] × p_{x1, y1}+ C_{x1, y2}[N-1] × p_{x1, y2}+ C_{x1, y3}[N-1] × p_{x1, y3}+… + C_{x4, y3}[N-1] × p_{x4, y3}
[0013]
Next, the histogram H calculated for each element k_{i, j}[0] to H_{i, j}Rearrange [n−1] in ascending order of values, and histogram H_{i, j}The element number sequence S [k] of [k] is calculated (step T5).
[0014]
For example, the histogram H_{i, j}[0] to H_{i, j}[N-1] is H_{i, j}[8] <H_{i, j}[3] <H_{i, j}[4] <H_{i, j}[1] <H_{i, j}[5] <... <H_{i, j}When rearranged as [n−1], the element number sequence S [k] is calculated as {8, 3, 4, 1, 5,..., (N−1)}. In the element number sequence S [k], “8” is the 0th element and “3” is the 1st element.
[0015]
After calculating the element number sequence S [k], the counter value Count is set to 0 (step T6), and the element k ′ of the element number sequence S [k] is set to the counter value Count in the element number sequence S [k]. The value of the element S [Count] in the order corresponding to the value is substituted (step T7). That is, taking the element number sequence S [k] (= {8, 3, 4, 1, 5,..., (N−1)}) as an example, the counter value Count is 0. 8 (= S [0]) is substituted.
[0016]
Subsequently, the counter value Count and the pixel value (g_{i, j}-1) is compared (step T8).
As a result of the comparison, the counter value Count becomes the pixel value (g_{i, j}-1) If it is less than or equal to the dot appearance pattern C corresponding to the element k '_{i, j}[K ′] is set to “1” (step T9), and if it is larger, it is set to “0” (step T10). For example, the pixel value g_{i, j}Is 3 and the element number sequence S [k] is {8, 3, 4, 1, 5,..., (N−1)}, the dot appearance pattern C_{i, j}First, among elements [k], element C_{i, j}[8] is set to “1”.
[0017]
When the processing of step T9 or step T10 is completed, 1 is added to the counter value Count (step T11), the magnitude relationship between the counter values Count (= 1) and (n−1) is compared (step T12), and the counter By repeating the processes from step T7 to step T11 until the value Count becomes the same value as (n−1), the dot appearance pattern C_{i, j}[K] is determined. Thereby, the element C is processed as described above._{i, j}[8], C_{i, j}[3], C_{i, j}“1” is set in [4]. Dot appearance pattern C_{i, j}When [k] is determined, the dot appearance pattern C_{i, j}Among [k], the random variable r determined in step T1 is C._{i, j}[R] is the output value b of the pixel in the i-th row and j-th column._{i, j}(Step T13). Output value b_{i, j}Is “0” or “1”, as can be seen from the processing in step T9 or step T10.
[0018]
Output value b_{i, j}Is calculated, it is determined whether or not the output value b has been calculated for all the pixels of the input original image data (step T14). The processing from step T2 to step T13 is repeated for each pixel of processing. If it is determined that the output value b of all the pixels, that is, the output image data has been calculated, the process is terminated.
[0019]
According to the image processing by the inverse correlation type digital halftoning method described above, when attention is paid to one pixel, the appearance frequency of the dot is the pixel value g of the pixel._{i, j}In the case of paying attention to a plurality of adjacent pixels, the appearance of dots in each pixel substantially maximizes the inverse correlation with the surrounding pixels. The dispersibility of the dots formed in this is improved. For this reason, the inverse correlation type digital halftoning method has a characteristic that there are few peculiar textures found in the error diffusion method.
It should be noted that the dot appearance pattern C related to the edge of the image_{i, j}The determination of [k] requires a dot appearance pattern for peripheral pixels outside the image area. For the peripheral pixels outside the image area for this purpose, the dot appearance pattern is determined by using a random variable or the like. Specifically, for example, the definition is as follows.
C_{i, j}[K] = 1 (when rBR <nΔ), 0 (otherwise)
Where Δ = | g_{i, j}  −n / 2 | / n and “g_{i, j}"Is the pixel value of the target pixel. “RBR” is {0, 1,. . . It is a random integer included in (int) (n / 2)}, which is a different value each time.
[0020]
[Patent Document 1]
JP-A-4-328597
[Patent Document 2]
JP-A-8-107500
[Non-Patent Document 1]
Dmitri A. Gusev, “Anti-Correlation Digital Halftoning”, [online], August 1998, Indiana University, [2003, July 1 search], Internet <URLhttp: // / Www. cs. indiana. edu / cgi-bin / techreports / TRNNN. cgi? trnum = TR513>
[0021]
[Problems to be solved by the invention]
However, when the inverse-correlation type digital halftoning method is applied to a color image or a grayscale image decomposed at different levels of density, no consideration is given to dot arrangement between different colors, resulting in color unevenness. It's easy to do. Moreover, the local brightness change is also random, and the feeling of roughness becomes strong.
[0022]
An object of the present invention is to provide an image processing apparatus, an image processing method, and an image processing program capable of obtaining a color image or grayscale image with reduced color unevenness and rough feeling and suppressing local variations in brightness. is there.
[0023]
[Means for Solving the Problems]
The invention according to claim 1 is an image processing apparatus having an arithmetic processing unit for quantizing multi-tone image data for each of a plurality of different colors and converting the image data into pseudo-halftone output image data,
The arithmetic processing unit includes:
Performing a first quantization for determining a dot appearance information array for a target pixel of multi-tone image data relating to a first color among the plurality of colors;
Thereafter, for the target pixel of the multi-gradation image data related to the second color other than the first color among the plurality of colors, the inverse correlation increases with respect to the dot appearance information array determined by the first quantization. The second quantization for determining the dot appearance information array is performed.
[0024]
Here, the plurality of colors means a plurality of colors having at least one of brightness, saturation and hue.
Further, determining the dot appearance information array for the target pixel of the multi-tone image data means determining the dot appearance information array for the data regarding the target pixel in the multi-tone image data.
The first color dot appearance information array determined by the first quantization may be determined for the target pixel or may be determined for each of the target pixel and its surrounding pixels. .
Further, determining the second color dot appearance information array so that the inverse correlation becomes larger with respect to the first color dot appearance information array determined by the first quantization means that the first color dot appearance information is determined. In an element number where the expected value of the appearance of a dot is large in the array, the expected value of the appearance of the second color dot is decreased, and in the element number of the first color dot appearance information array, the expected value of the appearance of the dot is small This means that the second color dot appearance information array is determined so as to increase the expected value of the second color dot appearance.
Further, the first color and the second color are not limited to one color, but may be a plurality of colors.
[0025]
According to the first aspect of the present invention, in the second quantization, the second color dot appears for the target pixel so that the inverse correlation becomes larger with respect to the dot appearance information array previously determined by the first quantization. Since the information arrangement is determined, the correlation is adjusted between dots of different colors. Therefore, unlike the prior art, it is possible to obtain a color image in which the color unevenness and the rough feeling are reduced and the local variation in brightness is suppressed.
[0026]
The invention according to claim 2 is the image processing apparatus according to claim 1,
The dot information appearance information array for the pixel of interest determined by the first quantization has a substantial inverse correlation with the dot appearance information array for the pixel already subjected to the first quantization around the pixel of interest. It is characterized by being the largest.
[0027]
According to the second aspect of the present invention, the dot information appearance information array for the target pixel determined by the first quantization is a dot appearance for a pixel that has already been subjected to the first quantization around the target pixel. Since the inverse correlation is substantially the largest with respect to the information array, the dots of the first color are surely dispersed. Therefore, since the correlation between dots of different colors can be adjusted in advance after reducing the feeling of roughness in the first color, a color image that further reduces the feeling of roughness and suppresses local fluctuations in lightness can be obtained. Obtainable.
[0028]
Note that the inverse correlation of the dot information appearance information array for the pixel of interest with respect to the dot appearance information array for the pixel that has already been subjected to the first quantization around the pixel of interest indicates that the dot appearance information of the peripheral pixels is large. In the array, an element number with a large expected value of dot appearance reduces the expected value of the dot appearance at the target pixel, and in the dot appearance information array of surrounding pixels, an element number with a small expected value of dot appearance is the target pixel. It means that the expected value of the appearance of a dot at is large.
The pixels around the pixel of interest are pixels other than the pixel of interest, and are pixels within a range necessary for expressing the number of gradations necessary for the pixel of interest as an area ratio. For example, the peripheral pixels when binarizing 8-bit multi-gradation image data are ideally 256 pixels closest to the target pixel, but in practice, the reflectance and visual characteristics are nonlinear. Therefore, the number of pixels is 256 times the number of pixels. However, only a part of the number of pixels obtained by multiplying 256 by a number may be treated as a peripheral pixel in consideration of the amount of calculation in image processing.
[0029]
The invention according to claim 3 is the image processing apparatus according to claim 1 or 2,
The arithmetic processing unit includes:
In the second quantization, a dot appearance information array that has a large inverse correlation with respect to the dot appearance information array determined by the first quantization is used with a predetermined weight. And
[0030]
According to the third aspect of the present invention, since the dot appearance information array that has a large inverse correlation is determined with respect to the dot appearance information array previously determined in the first quantization and used with a predetermined weight, The correlation between the dots of different colors can be easily adjusted.
[0031]
Invention of Claim 4 is an image processing apparatus as described in any one of Claims 1-3,
The arithmetic processing unit includes:
After performing the second quantization, for the target pixel of the multi-gradation image data regarding the first color and the third color other than the second color among the plurality of colors, the first quantization and the The third quantization for determining the dot appearance information array of the third color is performed so that the inverse correlation becomes larger with respect to the dot appearance information array determined by at least one of the second quantization. .
[0032]
According to the fourth aspect of the present invention, in the third quantization, the inverse correlation is increased with respect to the dot appearance information array determined by at least one of the first quantization and the second quantization. Since the dot appearance information arrangement of the third color is determined for the target pixel, the correlation is adjusted between the dots of the third color and the first color or the second color. Therefore, unlike the prior art, it is possible to obtain a color image in which the color unevenness and the rough feeling are reduced and the local variation in brightness is suppressed.
[0033]
Here, the dot appearance information array of the first color and the second color determined in the first quantization and the second quantization may be determined for the target pixel, and the target pixel and the peripheral pixel It may be determined for each.
The third color is not limited to one color, and may be a plurality of colors.
[0034]
Invention of Claim 5 is an image processing apparatus as described in any one of Claims 1-4,
The first color is lighter than the second color.
[0035]
According to the invention described in claim 5, since the dot appearance information array is determined in the order of low lightness, that is, in the order of high visibility, the color having high visibility is different from the case of determining the dot appearance information array in order of high lightness. Can be reliably dispersed. Therefore, it is possible to obtain an image with little color unevenness and roughness.
[0036]
Invention of Claim 6 is an image processing apparatus as described in any one of Claims 1-5,
The first color is blue, and the second color is red or green.
[0037]
According to the invention described in claim 6, the same effect as that of the invention described in any one of claims 1 to 5 can be obtained for multi-tone image data relating to RGB.
[0038]
The invention according to claim 7 is the image processing apparatus according to any one of claims 1 to 5,
The first color is black, and the second color is magenta or cyan.
[0039]
According to the seventh aspect of the present invention, the same effect as that of any one of the first to fifth aspects can be obtained for the multi-tone image data relating to YMCK.
[0040]
The invention according to claim 8 is an image processing method for quantizing multi-tone image data for each of a plurality of different colors and converting the data into pseudo-halftone output image data,
Performing a first quantization for determining a dot appearance information array for a target pixel of multi-tone image data relating to a first color among the plurality of colors;
Thereafter, for the target pixel of the multi-gradation image data related to the second color other than the first color among the plurality of colors, the inverse correlation increases with respect to the dot appearance information array determined by the first quantization. The second quantization for determining the dot appearance information array is performed.
[0041]
According to the invention described in claim 8, in the second quantization, the second color dot appears for the target pixel so that the inverse correlation becomes larger with respect to the dot appearance information array previously determined by the first quantization. By determining the information arrangement, the correlation is adjusted between dots of different colors. Therefore, unlike the prior art, it is possible to obtain a color image in which the color unevenness and the rough feeling are reduced and the local variation in brightness is suppressed.
[0042]
The invention according to claim 9 is the image processing method according to claim 8,
In the first quantization, for the target pixel, the inverse correlation is substantially maximized with respect to the dot appearance information array for the pixel that has already been subjected to the first quantization around the target pixel. The dot information appearance information array is determined.
[0043]
According to the ninth aspect of the present invention, in the first quantization, the inverse correlation is substantially the largest with respect to the dot appearance information array for the pixels already subjected to the first quantization around the target pixel. Thus, by determining the dot information appearance information array for the pixel of interest, the dots of the first color are reliably dispersed. Therefore, since the correlation between dots of different colors can be adjusted in advance after reducing the feeling of roughness in the first color, a color image that further reduces the feeling of roughness and suppresses local fluctuations in lightness can be obtained. Obtainable.
[0044]
The invention according to claim 10 is the image processing method according to claim 8 or 9, wherein
In the second quantization, a dot appearance information array that has a large inverse correlation with respect to the dot appearance information array previously determined by the first quantization is used with a predetermined weight. It is characterized by.
[0045]
According to the tenth aspect of the present invention, a dot appearance information array that has a large inverse correlation with respect to the dot appearance information array previously determined in the first quantization is used with a predetermined weight. Thus, the correlation between dots of different colors can be easily adjusted.
[0046]
The invention according to claim 11 is the image processing method according to any one of claims 8 to 10,
After performing the second quantization, for the target pixel of the multi-gradation image data regarding the first color and the third color other than the second color among the plurality of colors, the first quantization and the The third quantization for determining the dot appearance information array of the third color is performed so that the inverse correlation becomes larger with respect to the dot appearance information array determined by at least one of the second quantization. .
[0047]
According to the invention of claim 11, in the third quantization, an inverse correlation is increased with respect to the dot appearance information array determined by at least one of the first quantization and the second quantization. The correlation between the dots of the third color and the first color or the second color is adjusted by determining the dot appearance information array of the third color for the pixel of interest. Therefore, unlike the prior art, it is possible to obtain a color image in which the color unevenness and the rough feeling are reduced and the local variation in brightness is suppressed.
[0048]
The invention according to claim 12 is the image processing method according to any one of claims 8 to 11,
In the first quantization, a color having lightness lower than that of the second color is selected as the first color.
[0049]
According to the twelfth aspect of the invention, since the dot appearance information array is determined in the order of low lightness, that is, in the order of high visibility, unlike the case where the dot appearance information array is determined in the order of high lightness, High color dots are surely dispersed. Therefore, it is possible to obtain an image with little color unevenness and roughness.
[0050]
The invention according to claim 13 is the image processing method according to any one of claims 8 to 12,
Select blue as the first color,
Red or green is selected as the second color.
[0051]
According to the thirteenth aspect of the present invention, the same effect as that of any of the eighth to twelfth aspects of the present invention can be obtained for multi-tone image data relating to RGB.
[0052]
The invention according to claim 14 is the image processing method according to any one of claims 8 to 12,
Select black as the first color,
Magenta or cyan is selected as the second color.
[0053]
According to the invention of the fourteenth aspect, the same effect as that of the invention described in any one of the eighth to twelfth aspects can be obtained for the multi-tone image data related to YMCK.
[0054]
The invention according to claim 15 is an image processing program,
A computer that quantizes multi-tone image data for each of a plurality of different colors and converts them into pseudo-halftone output image data.
A function of performing first quantization for determining a dot appearance information array for a target pixel of multi-tone image data relating to a first color among the plurality of colors;
Thereafter, for the target pixel of the multi-gradation image data related to the second color other than the first color among the plurality of colors, the inverse correlation increases with respect to the dot appearance information array determined by the first quantization. The second quantization function for determining the dot appearance information arrangement is realized.
[0055]
According to the fifteenth aspect of the present invention, in the second quantization, the dot appearance information array of the second color for the pixel of interest so that the inverse correlation becomes larger with respect to the dot appearance information array determined by the first quantization. Therefore, the correlation is adjusted between dots of different colors. Therefore, unlike the prior art, it is possible to obtain a color image in which the color unevenness and the rough feeling are reduced and the local variation in brightness is suppressed.
[0056]
The invention described in claim 16 is the image processing program according to claim 15,
On the computer,
In the first quantization, for the target pixel, the inverse correlation is substantially maximized with respect to the dot appearance information array for the pixel that has already been subjected to the first quantization around the target pixel. A function for determining the dot information appearance information array is realized.
[0057]
According to the sixteenth aspect of the present invention, in the first quantization, the inverse correlation is substantially maximized with respect to the dot appearance information array for the pixels that have already been subjected to the first quantization around the pixel of interest. As described above, since the dot information appearance information array for the target pixel is determined, the dots of the first color are surely dispersed. Therefore, since the correlation between dots of different colors can be adjusted in advance after reducing the feeling of roughness in the first color, a color image that further reduces the feeling of roughness and suppresses local fluctuations in lightness can be obtained. Obtainable.
[0058]
The invention according to claim 17 is the image processing program according to claim 15 or 16,
On the computer,
In the second quantization, a function for determining a dot appearance information array that has a large inverse correlation with respect to the one using the dot appearance information array determined by the first quantization with a predetermined weight is realized. It is characterized by making it.
[0059]
According to the seventeenth aspect of the invention, the computer determines a dot appearance information array that has a large inverse correlation with respect to the one using the dot appearance information array previously determined in the first quantization with a predetermined weight. Therefore, the correlation between dots of different colors can be easily adjusted.
[0060]
The invention according to claim 18 is the image processing program according to any one of claims 15 to 17,
On the computer,
After performing the second quantization, for the target pixel of the multi-gradation image data regarding the first color and the third color other than the second color among the plurality of colors, the first quantization and the Realizing a function of performing the third quantization for determining the dot appearance information arrangement of the third color so that the inverse correlation becomes larger with respect to the dot appearance information arrangement determined by at least one of the second quantization. It is characterized by.
[0061]
According to the invention of claim 18, in the third quantization, an inverse correlation is increased with respect to the dot appearance information array determined by at least one of the first quantization and the second quantization. Since the dot appearance information array of the third color is determined for the target pixel, the correlation is adjusted between the dots of the third color and the first color or the second color. Therefore, unlike the prior art, it is possible to obtain a color image in which the color unevenness and the rough feeling are reduced and the local variation in brightness is suppressed.
[0062]
The invention according to claim 19 is the image processing program according to any one of claims 15 to 18,
On the computer,
In the first quantization, a function of selecting a color having lightness lower than that of the second color as the first color is realized.
[0063]
According to the nineteenth aspect of the invention, since the dot appearance information array is determined in the order of low lightness, that is, in order of high visibility, unlike the case where the dot appearance information array is determined in order of high lightness, High color dots are surely dispersed. Therefore, it is possible to obtain an image with little color unevenness and roughness.
[0064]
The invention according to claim 20 is the image processing program according to any one of claims 15 to 19,
On the computer,
A function of selecting blue as the first color and selecting red or green as the second color is realized.
[0065]
According to the twentieth aspect, the same effect as that of the fifteenth aspect of the present invention can be obtained for RGB multi-tone image data.
[0066]
The invention according to claim 21 is the image processing program according to any one of claims 15 to 19,
On the computer,
The function of selecting black as the first color and selecting magenta or cyan as the second color is realized.
[0067]
According to the twenty-first aspect of the invention, the same effect as that of any of the fifteenth to nineteenth aspects of the present invention can be obtained for multi-tone image data relating to YMCK.
[0068]
According to a twenty-second aspect of the present invention, there is provided an arithmetic processing unit that decomposes multi-tone black-and-white image data into multi-tone image data having a plurality of different densities and then quantizes them to convert them into pseudo-halftone output image data. An image processing apparatus comprising:
The arithmetic processing unit includes:
Performing first quantization for determining a dot appearance information array for a target pixel of multi-tone image data related to a first density among the plurality of densities;
Thereafter, the inverse correlation is increased with respect to the dot appearance information array determined by the first quantization for the target pixel of the multi-tone image data regarding the second density other than the first density among the plurality of densities. The second quantization for determining a proper dot appearance information array is performed.
[0069]
According to the invention of claim 22, in the second quantization, the dot appearance information array of the second density for the target pixel so that the inverse correlation becomes larger with respect to the dot appearance information array determined by the first quantization. Therefore, the correlation is adjusted between dots of different densities. Therefore, unlike the prior art, it is possible to obtain a grayscale image with reduced density unevenness and roughness and with reduced local variations in brightness.
[0070]
The invention described in claim 23 is the image processing apparatus according to claim 22,
The dot information appearance information array for the pixel of interest determined by the first quantization has a substantial inverse correlation with the dot appearance information array for the pixel already subjected to the first quantization around the pixel of interest. It is characterized by being the largest.
[0071]
According to a twenty-third aspect of the present invention, the dot information appearance information array for the target pixel determined by the first quantization is a dot appearance for a pixel that has already been subjected to the first quantization around the target pixel. Since the inverse correlation is substantially the largest with respect to the information array, the dots of the first density are surely dispersed. Therefore, since the correlation between dots having different densities can be adjusted in advance after reducing the feeling of roughness at the first density, a grayscale image that further reduces the feeling of roughness and suppresses local fluctuations in lightness can be obtained. Obtainable.
[0072]
The invention according to claim 24 is the image processing apparatus according to claim 22 or 23, wherein
The arithmetic processing unit includes:
In the second quantization, a dot appearance information array that has a large inverse correlation with respect to the dot appearance information array determined by the first quantization is used with a predetermined weight. And
[0073]
According to the twenty-fourth aspect of the present invention, since the dot appearance information array that has a large inverse correlation is determined with respect to the dot appearance information array previously determined in the first quantization and used with a predetermined weight, The correlation between dots of different densities can be easily adjusted.
[0074]
The invention described in claim 25 is the image processing apparatus according to any one of claims 22-24,
The arithmetic processing unit includes:
After performing the second quantization, for the target pixel of the multi-gradation image data regarding the third density other than the first density and the second density among the plurality of densities, the first quantization and the The third quantization for determining the dot appearance information array of the third density is performed so that the inverse correlation becomes large with respect to the dot appearance information array determined by at least one of the second quantization. .
[0075]
According to the invention of claim 25, in the third quantization, the inverse correlation is increased with respect to the dot appearance information array determined by at least one of the first quantization and the second quantization. Since the dot appearance information array of the third density is determined for the target pixel, the correlation is adjusted between the dots of the third density and the first density or the second density. Therefore, unlike the prior art, it is possible to obtain a grayscale image with reduced color unevenness and roughness and with reduced local variations in brightness.
[0076]
The invention according to claim 26 is the image processing apparatus according to any one of claims 22 to 25, wherein:
The first density is lower in brightness than the second density.
[0077]
According to the twenty-sixth aspect of the present invention, since the dot appearance information array is determined in descending order of lightness, that is, in order of high visibility, unlike the case of determining the dot appearance information array in order of high lightness, the density with high visibility. Can be reliably dispersed. Therefore, it is possible to obtain an image with less visual unevenness in density and roughness.
[0078]
The invention according to claim 27 is an image processing method for decomposing multi-tone black-and-white image data into multi-tone image data having a plurality of different densities and then quantizing them to convert them into pseudo-halftone output image data. There,
Performing first quantization for determining a dot appearance information array for a target pixel of multi-tone image data related to a first density among the plurality of densities;
Thereafter, the inverse correlation is increased with respect to the dot appearance information array determined by the first quantization for the target pixel of the multi-tone image data regarding the second density other than the first density among the plurality of densities. The second quantization for determining a proper dot appearance information array is performed.
[0079]
According to the twenty-seventh aspect of the present invention, in the second quantization, the dot appearance information array of the second density with respect to the pixel of interest so that the inverse correlation becomes larger with respect to the dot appearance information array determined by the first quantization. , The correlation is adjusted between dots of different densities. Therefore, unlike the prior art, it is possible to obtain a grayscale image with reduced density unevenness and roughness and with reduced local variations in brightness.
[0080]
The invention according to claim 28 is the image processing method according to claim 27,
In the first quantization, for the target pixel, the inverse correlation is substantially maximized with respect to the dot appearance information array for the pixel that has already been subjected to the first quantization around the target pixel. The dot information appearance information array is determined.
[0081]
According to the invention described in claim 28, in the first quantization, the inverse correlation is substantially the largest with respect to the dot appearance information array for the pixels already subjected to the first quantization around the target pixel. As described above, by determining the dot information appearance information array for the target pixel, the dots having the first density are surely dispersed. Therefore, since the correlation between dots having different densities can be adjusted in advance after reducing the feeling of roughness at the first density, a grayscale image that further reduces the feeling of roughness and suppresses local fluctuations in lightness can be obtained. Obtainable.
[0082]
The invention according to claim 29 is the image processing method according to claim 27 or 28,
In the second quantization, a dot appearance information array that has a large inverse correlation with respect to the dot appearance information array previously determined by the first quantization is used with a predetermined weight. It is characterized by.
[0083]
According to the twenty-ninth aspect of the present invention, the dot appearance information array having a large inverse correlation is determined with respect to the dot appearance information array previously determined in the first quantization and used with a predetermined weight. Thus, the correlation between dots having different densities can be easily adjusted.
[0084]
The invention according to claim 30 is the image processing method according to any one of claims 27 to 29,
After performing the second quantization, for the target pixel of the multi-gradation image data regarding the third density other than the first density and the second density among the plurality of densities, the first quantization and the The third quantization for determining the dot appearance information array of the third density is performed so that the inverse correlation becomes large with respect to the dot appearance information array determined by at least one of the second quantization. .
[0085]
According to the invention of claim 30, in the third quantization, an inverse correlation is increased with respect to the dot appearance information array determined by at least one of the first quantization and the second quantization. The correlation between the dots of the third density and the first density or the second density is adjusted by determining the dot appearance information array of the third density for the target pixel. Therefore, unlike the prior art, it is possible to obtain a grayscale image with reduced color unevenness and roughness and with reduced local variations in brightness.
[0086]
The invention described in claim 31 is the image processing method according to any one of claims 27-30,
In the first quantization, a density having lightness lower than that of the second density is selected as the first density.
[0087]
According to the invention of claim 31, since the dot appearance information array is determined in the order of low brightness, that is, in the order of high visibility, unlike the case where the dot appearance information array is determined in the order of high brightness, the visibility High density dots are reliably dispersed. Therefore, it is possible to obtain an image with less visual unevenness in density and roughness.
[0088]
The invention according to claim 32 is an image processing program,
After separating the multi-tone black and white image data into multi-tone image data of different densities, it is quantized and converted into pseudo halftone output image data.
A function of performing first quantization for determining a dot appearance information array for a target pixel of multi-tone image data relating to a first density among the plurality of densities;
Thereafter, the inverse correlation is increased with respect to the dot appearance information array determined by the first quantization for the target pixel of the multi-tone image data regarding the second density other than the first density among the plurality of densities. And a second quantization function for determining a proper dot appearance information arrangement.
[0089]
According to the thirty-second aspect of the present invention, in the second quantization, the dot appearance information array of the second density for the target pixel is set so that the inverse correlation becomes larger with respect to the dot appearance information array determined by the first quantization. Therefore, the correlation is adjusted between dots of different densities. Therefore, unlike the prior art, it is possible to obtain a grayscale image with reduced density unevenness and roughness and with reduced local variations in brightness.
[0090]
According to a thirty-third aspect of the present invention, in the image processing program according to the thirty-second aspect,
On the computer,
In the first quantization, for the target pixel, the inverse correlation is substantially maximized with respect to the dot appearance information array for the pixel that has already been subjected to the first quantization around the target pixel. A function for determining the dot information appearance information array is realized.
[0091]
According to the invention of claim 33, in the first quantization, the computer has the most inverse correlation with respect to the dot appearance information array for the pixels already subjected to the first quantization around the pixel of interest. Since the dot information appearance information array for the pixel of interest is determined so as to increase, the dots having the first density are surely dispersed. Therefore, since the correlation between dots having different densities can be adjusted in advance after reducing the feeling of roughness at the first density, a grayscale image that further reduces the feeling of roughness and suppresses local fluctuations in lightness can be obtained. Obtainable.
[0092]
The invention according to claim 34 is the image processing program according to claim 32 or 33,
On the computer,
In the second quantization, a function for determining a dot appearance information array that has a large inverse correlation with respect to the dot appearance information array previously determined by the first quantization using a predetermined weight. It is characterized by realizing.
[0093]
According to the thirty-fourth aspect of the invention, the computer determines a dot appearance information array that has a large inverse correlation with respect to the dot appearance information array previously determined in the first quantization with a predetermined weight. Therefore, the correlation between dots having different densities can be easily adjusted.
[0094]
The invention according to claim 35 is the image processing program according to any one of claims 32 to 34.
On the computer,
After performing the second quantization, for the target pixel of the multi-gradation image data regarding the third density other than the first density and the second density among the plurality of densities, the first quantization and the Realizing a function of performing the third quantization for determining the dot appearance information array of the third density so that the inverse correlation becomes large with respect to the dot appearance information array determined by at least one of the second quantization. It is characterized by.
[0095]
According to the invention of claim 35, in the third quantization, the inverse correlation is increased with respect to the dot appearance information array determined by at least one of the first quantization and the second quantization. Since the dot appearance information array of the third density is determined for the target pixel, the correlation is adjusted between the dots of the third density and the first density or the second density. Therefore, unlike the prior art, it is possible to obtain a grayscale image with reduced color unevenness and roughness and with reduced local variations in brightness.
[0096]
The invention according to claim 36 is the image processing program according to any one of claims 32 to 35,
In the first quantization, a function of selecting a density having lightness lower than the second density as the first density is realized.
[0097]
According to the thirty-sixth aspect of the invention, since the dot appearance information array is determined in the order of low lightness, that is, in the order of high visibility, unlike the case where the dot appearance information array is determined in the order of high lightness, High density dots are reliably dispersed. Therefore, it is possible to obtain an image with less visual unevenness in density and roughness.
[0098]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, embodiments of the present invention will be described with reference to the drawings.
First, an image processing apparatus according to the present invention will be described.
FIG. 1 is a block diagram illustrating a schematic configuration of the image processing apparatus 1. As shown in this figure, the image processing apparatus 1 includes an arithmetic processing unit 2 for converting input multi-gradation image data into pseudo-halftone output image data and outputting it. The image processing apparatus 1 can be mounted on a known output device such as an ink jet printer. Further, in the present embodiment, as shown in FIG. 2A, the multi-tone image data has a gradation value of 0 to n for each of R (red), G (green), and B (blue). The following description is given as data relating to a gradation image that changes to (n ≧ 2).
[0099]
The arithmetic processing unit (computer) 2 includes a ROM (Read Only Memory) 3, a RAM (Random Access Memory) 4, and a CPU (Central Processing Unit) 5 connected to each other.
The ROM 3 stores an image processing program according to the present invention. This image processing program is for causing the arithmetic processing unit 2 to perform an inverse correlation type digital halftoning process for multi-tone image data.
The RAM 4 is provided with a work area for the CPU 5.
The CPU 5 develops the image processing program stored in the ROM 3 in the work area in the RAM 4 and generates output image data from the multi-tone image data.
[0100]
Next, an image processing method according to the present invention will be described with reference to FIGS. In the following description, the image processing apparatus 1 performs processing by switching the pixel of interest in a direction from left to right. However, regarding the processing direction after switching a row including a predetermined number of pixels. The direction from the left to the right may be set again, the direction may be reversed and the direction may be set from the right to the left, or one of the left and right directions may be selected at random. Preferably, the image processing apparatus 1 is configured to reverse the processing direction for each row or for each of a plurality of rows so as to perform the processing in the order of meandering as a whole.
[0101]
FIG. 3 shows that the arithmetic processing unit 2 of the image processing apparatus 1 generates output image data for one of red, green, and blue, in this embodiment, blue based on the image processing program. 5 is a flowchart showing the inverse correlation type digital halftoning (first quantization) process executed in step S2.
As shown in this figure, the arithmetic processing unit 2 determines a random variable r in advance before multi-tone image data is input, similarly to the conventional step T1 (step S1).
When the multi-gradation image data is input, the arithmetic processing unit 2 determines the pixel value g related to the pixel in the i-th row and j-th column from the blue multi-gradation image data._{i, j}(Step S2), and in the same manner as in the conventional step T3, this pixel value g_{i, j}Based on the above, a local filter P of the pixel in the i-th row and j-th column is generated (step S3).
[0102]
When the local filter P is generated, the arithmetic processing unit 2 performs the histogram H of the pixel in the i-th row and j-th column as in the conventional step T4._{(B) i, j}[K] is calculated (step S4). Note that the letter in parentheses of the subscript attached to “H” represents a color (in this case, blue).
[0103]
Next, the arithmetic processing unit 2 performs the element number sequence S [k] and the dot appearance pattern (dot appearance information array) C as in the conventional steps T5 to T14._{(B) i, j}[K] is determined, and output values b for all pixels, that is, output image data are calculated (steps S5 to S14). Thereby, when attention is paid to one pixel, the appearance frequency of the dot is the pixel value g of the pixel._{i, j}In the case of paying attention to a plurality of adjacent pixels, the appearance of dots in each pixel substantially maximizes the inverse correlation with the surrounding pixels. The dispersibility of the dots formed in this is improved.
[0104]
If it is determined that the output image data for blue is calculated, the arithmetic processing unit 2 performs an inverse correlation type digital halftoning (second quantization) process for generating green and red output image data.
[0105]
FIG. 4 shows the time-dependent inverse correlation type digital halftoning process executed by the arithmetic processing unit 2 of the image processing apparatus 1 to generate output image data for the second and subsequent colors based on the image processing program. It is the shown flowchart. In the present embodiment, the calculation processing unit 2 will be described as performing an inverse correlation type digital halftoning process in the order of green and red after blue.
[0106]
As shown in FIG. 4, the arithmetic processing unit 2 performs the same processing as the above steps S <b> 2 to S <b> 4, and the green pixel value g for the pixel in the i row and j column_{i, j}, A local filter P is generated, and the histogram H_{(G) i, j}[K] is calculated (steps S20 to S22).
[0107]
Histogram H about green_{(G) i, j}After calculating [k], the arithmetic processing unit 2 calculates the histogram H as shown in the following expression in C language._{(G) i, j}For [k], the dot appearance pattern C for blue determined by the above steps S7 to S11 for the pixel in the i-th row and j-th column_{(B) i, j}[K] is a predetermined weight W_{(R) i, j}(Step S23). That is, the histogram H_{(G) i, j}[0] to H_{(G) i, j}For [n-1], C_{(B) i, j}[0] to C_{(B) i, j}[N-1] is added with a predetermined weight W.
H_{(G) i, j}[K] + = W_{(B) i, j}* C_{(B) i, j}[K]
As a result, the correlation is adjusted between the blue dots and the green dots. Here, the “predetermined weight” is set based on the relationship between the previously processed color and the currently processed color, that is, the relationship between blue and green. This weight W_{(B) i, j}When the amount is large, the blue dots and the green dots are difficult to overlap each other, and when close to 0, the blue dots and the green dots are randomly dispersed as in the conventional case, and are negative values. In some cases, blue dots and green dots tend to overlap each other. In the present embodiment, the weight W is taken into account in consideration of the coefficient value of the local filter described above._{(B) i, j}Is 64 (times). The subscript “col” in FIG. 4 represents a processed color (in this case, blue).
[0108]
Next, the arithmetic processing unit 2 calculates the element number sequence S [k] in the same manner as in steps S5 to S14, and the dot appearance pattern C_{(G) i, j}[K] is determined, the output value b is calculated for all the pixels, and the generation of the green output image data is terminated (steps S24 to S33).
[0109]
Next, the arithmetic processing unit 2 performs the same processing as the above steps S2 to S4, and the red pixel value g for the pixel in the i-th row and j-th column._{i, j}, A local filter P is generated, and the histogram H_{(R) i, j}[K] is calculated (steps S40 to S42).
[0110]
Histogram H for red_{(R) i, j}After calculating [k], the arithmetic processing unit 2 calculates the histogram H as shown in the following expression in C language._{(R) i, j}For [k], the dot appearance pattern C for blue determined by the above steps S7 to S11 for the pixel in the i-th row and j-th column_{(B) i, j}[K] and the dot appearance pattern C for green determined in steps S26 to S30._{(G) i, j}[K] and a predetermined weight W_{(B) i, j}, W_{(G) i, j}(Step S43). As a result, the correlation is adjusted between the blue and green dots and the red dots. In the present embodiment, the weight W_{(G) i, j}Is also 64 (times).
H_{(R) i, j}[K] + = W_{(B) i, j}* C_{(B) i, j}[K] + W_{(G) i, j}* C_{(G) i, j}[K]
Here, when it is desired to simplify the calculation, not all the determined dot appearance patterns C_{(B) i, j}[K], C_{(G) i, j}[K] may not be used, for example, a blue dot appearance pattern C_{(B) i, j}Only [k] is used,
H_{(R) i, j}[K] + = W_{(B) i, j}* C_{(B) i, j}[K]
However, better results can be obtained compared to the conventional dot arrangement.
[0111]
Next, the arithmetic processing unit 2 calculates the element number sequence S [k] in the same manner as in steps S5 to S14, and the dot appearance pattern C_{(R) i, j}[K] is determined, the output value b is calculated for all the pixels, and the generation of the red output image data is terminated (steps S44 to S53).
[0112]
FIG. 2B shows an output image obtained by the above image processing method, and FIG. 2C shows an output image obtained by the conventional image processing method as a control. 2B and 2C, in order to display the superiority or inferiority of the output image in an easy-to-understand manner, when any dot of RGB is output, the pixel is white and the dot is not output at all. If not, the pixel is expressed in black.
As shown in these figures, the output image obtained by the above image processing method is different from the conventional output image in that the dots of each color are uniformly distributed, and as a result, there are fewer overlapping portions and missing colors. Yes.
[0113]
As described above, according to the image processing method, in the calculation of the histogram for determining the dot appearance pattern of the target pixel, not only the dot appearance pattern around the target pixel relating to the current processing target color but also the processed color. By taking into account the dot appearance pattern of the pixel of interest, the correlation between the dots of different colors is adjusted, and unlike conventional images, the RGB image reduces color unevenness and roughness and suppresses local fluctuations in brightness. Can be obtained.
That is, the blue dot appearance pattern C previously determined for the pixel in i row and j column_{(B) i, j}Based on [k], green and red dot appearance pattern C_{(G I, j}[K], C_{(R) i, j}Since [k] is determined, the correlation can be reliably adjusted between dots of different colors.
[0114]
[Modification (1) of the first embodiment]
Subsequently, a modification of the image processing apparatus 1 will be described. In addition, the same code | symbol is attached | subjected to the component same as the said 1st Embodiment, and the description is abbreviate | omitted.
[0115]
The image processing apparatus 1 in this modification has the same configuration as that of the image processing apparatus 1 described in the first embodiment, but the generation of output image data is not a frame sequential system but a line sequential system. It is supposed to be done with. In other words, in step S14 (S33), the image processing apparatus 1 calculates the blue (green) output value b for all the pixels constituting the row, and performs an inverse correlation digital halftoning process for blue (green). , And an inverse correlation type digital halftoning process for green (red) is started.
[0116]
Even with such an image processing apparatus 1, since the dot appearance patterns already determined for other colors are used in determining the dot appearance patterns for the second and subsequent colors, the same effects as in the first embodiment can be obtained. Can do.
[0117]
[Modification (2) of the first embodiment]
Subsequently, another modification of the image processing apparatus 1 will be described. In addition, the same code | symbol is attached | subjected to the component same as the said 1st Embodiment, and the description is abbreviate | omitted.
[0118]
The image processing apparatus 1 in this modification has the same configuration as that of the image processing apparatus 1 described in the first embodiment, but the generation of output image data is not a frame sequential method but a dot sequential method. It is supposed to be done with. That is, the image processing apparatus 1 ends the inverse correlation type digital halftoning process for blue (green) after calculating the blue output value b for one pixel in step S14 (S33). An inverse correlation type digital halftoning process for green (red) is started.
[0119]
Even with such an image processing apparatus 1, since the dot appearance patterns already determined for other colors are used in determining the dot appearance patterns for the second and subsequent colors, the same effects as in the first embodiment can be obtained. Can do.
[0120]
In the first embodiment and its modification, the blue output value b_{i, j}As described above, the inverse-correlation type digital halftoning process for green is started after calculation of the blue dot appearance pattern C._{(B) i, j}After determining [k], the output value b_{i, j}It is good also as starting before calculating.
[0121]
In addition, the random variable r has been described as being determined in step S1, but a fixed value in the range of 0 to (n-1) may be determined in advance, or dot appearance patterns of all images. It is also possible to determine the random value r after the determination is made.
Further, when calculating the element number sequence S [k], the histogram H_{i, j}Although it has been described that [k] are arranged in ascending order, they may be arranged in another order as long as the dispersibility is not impaired between the appearing dots. When calculating the element number sequence S [k], the histogram H_{i, j}Instead of using [k], an evaluation function related to the distribution of dots around the target pixel may be set and used. Such an evaluation function can be set using a dot appearance pattern for the peripheral pixels of the target pixel.
[0122]
Also, the weight W_{(B) i, j}, W_{(G) i, j}However, it is also possible to use other amounts such as -32 (times) and 128 (times). In the case of −32 (times), it is possible to reduce the dispersibility between dots of different colors and to superimpose dots of almost all colors. Further, when it is 128 (times), as shown in FIG. 2D, the dots of each color are more evenly distributed, and as a result, the overlapping portion and the missing color portion can be further reduced.
[0123]
Also, a red histogram H relating to the pixel in the i-th row and the j-th column_{(R) i, j}For [k], the blue and green dot appearance pattern C for this pixel_{(B) i, j}[K], C_{(G) i, j}[K] is weight W_{(B) i, j}, W_{(G) i, j}However, as shown in the following formula, the blue and green dot appearance pattern C related to the peripheral pixels of this pixel is shown._{(B) i +}δ_{, J}[K], C_{(G) i +}δ_{, J}[K] is also a predetermined weight W_{(B) i +}δ_{, J}, W_{(G) i +}δ_{, J}It is good also as adding each by. In this equation, the summation symbol “Σ” on the right side means that the summation is performed for all processed colors. Further, “δ” is “−1” or “+1”, and indicates the index of the processed row according to the processing direction of the image recording apparatus 1. However, the value of “δ” is not limited to this.
[Expression 1]

As a specific example, W_{(Col) i, j}= 64, W_{(Col) i +}δ_{, J}The output result when = 32 is shown in FIG. As can be seen from this figure, when adding the dot appearance patterns of the surrounding pixels, the correlation between the blue dots and the green dots is more reliably adjusted to reduce color unevenness and roughness, and each color Appropriate randomness can be imparted to the dot positions.
[0124]
Further, the image processing apparatus 1 has been described as processing multi-tone image data of a plurality of colors relating to RGB, but a multi-tone image of three colors such as Y (yellow), M (magenta), and C (cyan). Data, multi-tone image data of four colors Y, M, C, and K (black) may be processed.
In this case, it is preferable to perform an inverse correlation type digital halftoning process from multi-tone image data of a low brightness color, that is, a highly visible color. This makes it possible to reliably disperse dots with high visibility compared to the case of generating output image data in order of increasing lightness, so that it is possible to obtain an image that is visually less uneven and rough. Can do.
Further, for example, when processing the target pixel in the order of CMY, the yellow dot with the highest brightness and the low visibility has little visual effect even if it overlaps with other color dots._{(Y) i, j}[K] is cyan and magenta dot appearance pattern C_{(C) i, j}[K], C_{(M) i, j}The amount of weight W for adding [k] may be reduced. Thus, the dot appearance pattern C first_{(Col) i, j}The color for which [k] is determined and the current dot appearance pattern C_{(Col) i, j}By setting the amount of weight W based on the relationship between [k] and the color being determined, it is possible to disperse and arrange dots of a plurality of colors with different visibility. Note that the subscript “(col)” represents one of the colors.
[0125]
Further, the six basic filters K shown in FIGS.₁~ K₆The pixel values of the respective pixels can be changed, and are not necessarily the pixel values as shown in FIGS. Further, FIG. 9 is not necessarily used for generating the local filter P. Furthermore, the local filter P is a basic filter K.₁~ K₆For example, a table in which local filters P corresponding to input pixel values are stored in advance may be used.
[0126]
[Second Embodiment]
Next, the image processing apparatus 1 according to the second embodiment will be described. In addition, the same code | symbol is attached | subjected to the component same as the said 1st Embodiment, and the description is abbreviate | omitted.
[0127]
The image processing apparatus 1 in the present embodiment has the same configuration as that of the image processing apparatus 1 described in the first embodiment, but multi-tone black-and-white image data relating to a plurality of shades of color is quantized. To generate pseudo halftone output image data.
Specifically, as shown in FIG. 5, for example, the image processing apparatus 1 uses a density separation table to separate 256 gradations, that is, 8-bit monochrome image data into dark color image data and light color image data. After that, first, an inverse correlation type digital halftoning (first quantization) process is applied to the dark color image data, and then the inverse color correlation type is applied to the light color image data by using the dot appearance pattern of the dark dots as in the following equation. The digital halftoning (second quantization) process is performed to generate ternary output image data.
H_{(Light) i, j}[K] + = W * C_{(Dark) i, j}[K]
An example of the density separation table is shown in FIG. In the above formula, “H_{(Light) i, j}“[K]” is a histogram for calculating the dot appearance pattern of light dots._{(Dark) i, j}[K] ”is a dark dot appearance pattern. “W” is a positive constant.
[0128]
Here, it is preferable that the amount of the weight W is adjusted according to the pixel value of the dark color image data previously processed for the pixel in the i-th row and j-th column. Specifically, when the pixel value of the dark color image data is small, the dark dot and the light dot are randomly overlapped by bringing the weight W amount close to 0, and when the pixel value is large, the weight is It is preferable to reduce random overlap between dark dots and light dots by increasing the amount of W.
[0129]
According to this image processing apparatus 1, since the dispersion of dark dots and light dots is improved and random overlap is reduced as compared with the case where the appearance of dark and light dots is determined independently, unevenness in the mixing of light and dark dots is reduced. In addition, the feeling of roughness can be reduced.
[0130]
In the second embodiment, the black-and-white image data is divided into two image data of dark color image data and light color image data before performing inverse correlation type digital halftoning processing. However, it is also possible to perform light / dark separation into three or more image data. In this case, image data is inversely correlated with digital halftoning in the order of increasing lightness, that is, in descending order of density. In inversely correlated digital halftoning with respect to image data with high lightness, colors with low lightness are used. Dot appearance pattern C_{(Dark) i, j}As the amount of the weight W increases as [k], the dot appearance pattern C_{(Light) i, j}It is preferably used for calculating [k]. As a result, the lower the lightness and the higher the visibility of the dots, the more the dispersibility can be improved. Therefore, it is possible to obtain an image that is visually less uneven in color and rough.
[0131]
[Third Embodiment]
Next, the image processing apparatus 1 in the third embodiment will be described. In addition, the same code | symbol is attached | subjected to the component same as the said 1st Embodiment, and the description is abbreviate | omitted.
[0132]
The image processing apparatus 1 according to the present embodiment has the same configuration as that of the image processing apparatus 1 described in the first embodiment. However, the image processing apparatus 1 processes color multi-tone image data for each color. Multi-valued color output image data is generated. Specifically, as shown in FIG. 7, for example, the image processing apparatus 1 starts from image data having 256 gradations for each color of YMCK, data of dark black, cyan, magenta, yellow, and light color. After generating black, cyan, and magenta data, the inverse half-correlation type digital halftoning process is applied to any one of the data, and then the data for the second and subsequent colors using the already determined dot appearance pattern. By performing inverse correlation type digital halftoning processing, output image data binarized for yellow and ternarized for black, cyan, and magenta are generated. Note that the order of reverse correlation type digital halftoning is dark black, dark cyan, dark magenta, light black, light cyan, light magenta, dark color according to the brightness of each color. In order to improve the dispersibility of dots between the same colors, dark black, light black, dark cyan, light cyan, dark magenta, light magenta The output image data may be generated in the order of dark yellow. Further, for the density separation of the image data of each color, a combination of a four-dimensional lookup table and an interpolation operation, the above-described density separation table, or the like can be used.
[0133]
According to this image processing apparatus 1, since the correlation is adjusted between dots of different colors and densities, unlike the conventional case, color unevenness, density unevenness, and a feeling of roughness are reduced, and local fluctuations in brightness are suppressed. A YMCK image can be obtained.
[0134]
【The invention's effect】
According to the first, eighth, and fifteenth aspects of the present invention, unlike conventional ones, it is possible to obtain a color image that reduces color unevenness and roughness and suppresses local variations in brightness.
[0135]
According to the inventions of claims 2, 9, and 16, the same effects as those of the inventions of claims 1, 8, and 15 can be obtained, and further, the feeling of roughness can be further reduced and the locality of the brightness can be reduced. A color image with suppressed variation can be obtained.
[0136]
According to the third, tenth, and seventeenth aspects of the invention, the same effects as those of the first, second, eighth, ninth, fifteenth, and sixteenth aspects can be obtained. The correlation can be easily adjusted.
According to the inventions of claims 4, 11 and 18, the same effect as that of any one of claims 1 to 3, 8, 10 and 15 to 17 can be obtained. It is possible to obtain a color image in which unevenness and roughness are reduced and local fluctuations in brightness are suppressed.
[0137]
According to the fifth, twelfth, and nineteenth inventions, it is possible to obtain the same effect as that of any one of the first to fourth, eighth to eleventh, and fifteenth to eighteenth inventions. Therefore, it is possible to obtain an image with little color unevenness and roughness.
[0138]
According to the sixth, thirteenth and twentieth inventions, the same effects as those of any one of the first to fifth, eighth to twelfth and fifteenth to nineteenth aspects can be obtained for multi-tone image data relating to RGB. Obtainable.
[0139]
According to the invention of Claims 7, 14, and 21, the same effect as that of any one of Claims 1 to 5, 8 to 12, and 15 to 19 can be obtained for multi-gradation image data related to YMCK. Obtainable.
[0140]
According to the invention described in claims 22, 27, and 32, unlike the prior art, it is possible to obtain a grayscale image in which density unevenness and roughness are reduced and local variations in brightness are suppressed.
[0141]
According to the inventions of claims 23, 28 and 33, the same effects as those of the inventions of claims 22, 27 and 32 can be obtained, as well as a further reduction in the feeling of roughness and local brightness. A gray image with suppressed fluctuations can be obtained.
[0142]
According to the inventions of claims 24, 29, and 34, the same effects as those of the inventions of claims 22, 23, 27, 28, 32, and 33 can be obtained. The correlation can be easily adjusted.
According to the inventions of claims 25, 30, and 35, the same effects as those of the inventions of claims 22 to 24, 27 to 29, and 32 to 34 can be obtained, and density unevenness and roughness are reduced. In addition, it is possible to obtain a grayscale image in which local fluctuations in brightness are suppressed.
[0143]
According to the inventions of claims 26, 31, and 36, the same effects as those of any of claims 22 to 25, 27 to 30, and 32 to 35 can be obtained. Therefore, it is possible to obtain an image with less uneven density and rough feeling.
[Brief description of the drawings]
FIG. 1 is a block diagram showing a schematic configuration of an image processing apparatus according to the present invention.
2A is a diagram showing an output image of multi-tone image data, and FIGS. 2B, 2D, and 2E show output images when the image processing method according to the present invention is used. It is a figure, and (c) is a figure showing an output picture at the time of using a conventional image processing method.
FIG. 3 is a flowchart showing, over time, a first-color inverse correlation digital halftoning process executed by an arithmetic processing unit of the image processing apparatus;
FIG. 4 is a flowchart showing, over time, an inverse correlation type digital halftoning process for the second and subsequent colors executed by an arithmetic processing unit of the image processing apparatus;
FIG. 5 is a diagram illustrating a procedure for generating ternary output image data from black-and-white image data.
FIG. 6 is a diagram showing a density separation table.
FIG. 7 is a diagram showing a procedure for generating output image data ternarized for each color from color multi-tone image data.
FIG. 8 is a flowchart showing image processing over time according to a conventional inverse correlation type digital halftoning method;
FIG. 9 is a table in which a value Δ based on a pixel value is associated with information on a local filter P.
FIG. 10 is a diagram showing basic filters K1 to K3.
FIG. 11 is a diagram showing basic filters K4 to K6.
FIG. 12 is a diagram showing a local filter P (= R (K6, 4, −1)).
FIG. 13 Histogram H_{i, j}It is drawing for demonstrating the production | generation of [k].
[Explanation of symbols]
1 Image processing device
2 Arithmetic processing part

Claims (36)

An image processing apparatus having an arithmetic processing unit for quantizing multi-tone image data for each of a plurality of different colors and converting the image data into pseudo-halftone output image data,
The arithmetic processing unit includes:
Performing a first quantization for determining a dot appearance information array for a target pixel of multi-tone image data relating to a first color among the plurality of colors;
Thereafter, for the target pixel of the multi-gradation image data related to the second color other than the first color among the plurality of colors, the inverse correlation increases with respect to the dot appearance information array determined by the first quantization. An image processing apparatus that performs second quantization for determining such a dot appearance information array. The image processing apparatus according to claim 1.
The dot information appearance information array for the pixel of interest determined by the first quantization has a substantial inverse correlation with the dot appearance information array for the pixel already subjected to the first quantization around the pixel of interest. Image processing apparatus characterized by being the largest in size. The image processing apparatus according to claim 1 or 2,
The arithmetic processing unit includes:
In the second quantization, a dot appearance information array that has a large inverse correlation with respect to the dot appearance information array determined by the first quantization is used with a predetermined weight. An image processing apparatus. In the image processing device according to any one of claims 1 to 3,
The arithmetic processing unit includes:
After performing the second quantization, for the target pixel of the multi-gradation image data regarding the first color and the third color other than the second color among the plurality of colors, the first quantization and the An image processing apparatus that performs third quantization for determining a dot appearance information array so that an inverse correlation becomes larger with respect to a dot appearance information array determined by at least one of the second quantization. In the image processing device according to any one of claims 1 to 4,
The image processing apparatus according to claim 1, wherein the first color has lightness lower than that of the second color. In the image processing apparatus according to any one of claims 1 to 5,
The image processing apparatus according to claim 1, wherein the first color is blue, and the second color is red or green. In the image processing apparatus according to any one of claims 1 to 5,
The image processing apparatus according to claim 1, wherein the first color is black, and the second color is magenta or cyan. An image processing method for quantizing multi-tone image data for each of a plurality of different colors and converting it into pseudo-halftone output image data,
Performing a first quantization for determining a dot appearance information array for a target pixel of multi-tone image data relating to a first color among the plurality of colors;
Thereafter, for the target pixel of the multi-gradation image data related to the second color other than the first color among the plurality of colors, the inverse correlation increases with respect to the dot appearance information array determined by the first quantization. An image processing method comprising performing second quantization for determining such a dot appearance information array. The image processing method according to claim 8.
In the first quantization, for the target pixel, the inverse correlation is substantially maximized with respect to the dot appearance information array for the pixel that has already been subjected to the first quantization around the target pixel. An image processing method characterized by determining a dot information appearance information array. The image processing method according to claim 8 or 9,
In the second quantization, a dot appearance information array that has a large inverse correlation with respect to the dot appearance information array previously determined by the first quantization is used with a predetermined weight. An image processing method characterized by the above. In the image processing method as described in any one of Claims 8-10,
After performing the second quantization, for the target pixel of the multi-tone image data related to the first color and a third color other than the second color among the plurality of colors, the first quantization and the An image processing method comprising: performing third quantization for determining a dot appearance information array so that an inverse correlation becomes large with respect to the dot appearance information array determined by at least one of the second quantization. In the image processing method according to any one of claims 8 to 11,
In the first quantization, an image processing method, wherein a color having a lightness lower than that of the second color is selected as the first color. In the image processing method according to any one of claims 8 to 12,
Select blue as the first color,
An image processing method, wherein red or green is selected as the second color. In the image processing method according to any one of claims 8 to 12,
Select black as the first color,
An image processing method, wherein magenta or cyan is selected as the second color. A computer that quantizes multi-tone image data for each of a plurality of different colors and converts them into pseudo-halftone output image data.
A function of performing first quantization for determining a dot appearance information array for a target pixel of multi-tone image data relating to a first color among the plurality of colors;
Thereafter, for the target pixel of the multi-gradation image data related to the second color other than the first color among the plurality of colors, the inverse correlation increases with respect to the dot appearance information array determined by the first quantization. An image processing program for realizing a second quantization function for determining such dot appearance information arrangement. The image processing program according to claim 15,
On the computer,
In the first quantization, for the target pixel, the inverse correlation is substantially maximized with respect to the dot appearance information array for the pixel that has already been subjected to the first quantization around the target pixel. An image processing program for realizing a function of determining dot information appearance information arrangement. The image processing program according to claim 15 or 16,
On the computer,
In the second quantization, a function for determining a dot appearance information array that has a large inverse correlation with respect to the dot appearance information array determined by the first quantization using a predetermined weight is realized. An image processing program for making In the image processing program as described in any one of Claims 15-17,
On the computer,
After performing the second quantization, for the target pixel of the multi-gradation image data regarding the first color and the third color other than the second color among the plurality of colors, the first quantization and the An image processing program for realizing a function of performing third quantization for determining a dot appearance information array so that an inverse correlation becomes large with respect to the dot appearance information array determined by at least one of the second quantization. In the image processing program as described in any one of Claims 15-18,
On the computer,
In the first quantization, an image processing program for realizing a function of selecting a color having lightness lower than that of the second color as the first color. In the image processing program as described in any one of Claims 15-19,
On the computer,
An image processing program for realizing a function of selecting blue as the first color and selecting red or green as the second color. In the image processing program as described in any one of Claims 15-19,
On the computer,
An image processing program for realizing a function of selecting black as the first color and selecting magenta or cyan as the second color. An image processing apparatus having an arithmetic processing unit that decomposes multi-tone black-and-white image data into multi-tone image data having a plurality of different densities and then quantizes the converted data into pseudo-halftone output image data,
The arithmetic processing unit includes:
Performing first quantization for determining a dot appearance information array for a target pixel of multi-tone image data related to a first density among the plurality of densities;
Thereafter, the inverse correlation is increased with respect to the dot appearance information array determined by the first quantization for the target pixel of the multi-tone image data regarding the second density other than the first density among the plurality of densities. An image processing apparatus that performs second quantization for determining a proper dot appearance information array. The image processing apparatus according to claim 22, wherein
The dot information appearance information array for the pixel of interest determined by the first quantization has a substantial inverse correlation with the dot appearance information array for the pixel already subjected to the first quantization around the pixel of interest. Image processing apparatus characterized by being the largest in size. The image processing apparatus according to claim 22 or 23.
The arithmetic processing unit includes:
In the second quantization, a dot appearance information array that has a large inverse correlation with respect to the dot appearance information array determined by the first quantization is used with a predetermined weight. An image processing apparatus. The image processing apparatus according to any one of claims 22 to 24,
The arithmetic processing unit includes:
After performing the second quantization, for the target pixel of the multi-gradation image data regarding the third density other than the first density and the second density among the plurality of densities, the first quantization and the An image processing apparatus for performing a third quantization for determining a dot appearance information array so that an inverse correlation becomes large with respect to a dot appearance information array determined by at least one of the second quantization. In the image processing device according to any one of claims 22 to 25,
The image processing apparatus according to claim 1, wherein the first density is lighter than the second density. An image processing method for decomposing multi-tone black-and-white image data into multi-tone image data of different multiple densities, and then quantizing and converting to pseudo-halftone output image data,
Performing first quantization for determining a dot appearance information array for a target pixel of multi-tone image data related to a first density among the plurality of densities;
Thereafter, the inverse correlation is increased with respect to the dot appearance information array determined by the first quantization for the target pixel of the multi-tone image data regarding the second density other than the first density among the plurality of densities An image processing method comprising performing second quantization for determining a proper dot appearance information array. The image processing method according to claim 27, wherein
In the first quantization, for the target pixel, the inverse correlation is substantially maximized with respect to the dot appearance information array for the pixel that has already been subjected to the first quantization around the target pixel. An image processing method characterized by determining a dot information appearance information array. The image processing method according to claim 27 or 28, wherein:
In the second quantization, a dot appearance information array that has a large inverse correlation with respect to the dot appearance information array previously determined by the first quantization is used with a predetermined weight. An image processing method characterized by the above. The image processing method according to any one of claims 27 to 29,
After performing the second quantization, for the target pixel of the multi-gradation image data regarding the third density other than the first density and the second density among the plurality of densities, the first quantization and the An image processing method comprising: performing third quantization for determining a dot appearance information array so that an inverse correlation becomes large with respect to the dot appearance information array determined by at least one of the second quantization. The image processing method according to any one of claims 27 to 30, wherein
In the first quantization, an image processing method, wherein a density having lightness lower than the second density is selected as the first density. After separating the multi-tone black and white image data into multi-tone image data of different densities, it is quantized and converted into pseudo halftone output image data.
A function of performing first quantization for determining a dot appearance information array for a target pixel of multi-tone image data relating to a first density among the plurality of densities;
Thereafter, the inverse correlation is increased with respect to the dot appearance information array determined by the first quantization for the target pixel of the multi-tone image data regarding the second density other than the first density among the plurality of densities. Image processing program for realizing a second quantization function for determining a proper dot appearance information array. The image processing program according to claim 32,
On the computer,
In the first quantization, for the target pixel, the inverse correlation is substantially maximized with respect to the dot appearance information array for the pixel that has already been subjected to the first quantization around the target pixel. An image processing program for realizing a function of determining dot information appearance information arrangement. The image processing program according to claim 32 or 33,
On the computer,
In the second quantization, a function for determining a dot appearance information array that has a large inverse correlation with respect to the dot appearance information array previously determined by the first quantization using a predetermined weight. An image processing program for realizing In the image processing program as described in any one of Claims 32-34,
On the computer,
After performing the second quantization, for the target pixel of the multi-gradation image data regarding the third density other than the first density and the second density among the plurality of densities, the first quantization and the An image processing program for realizing a function of performing third quantization for determining a dot appearance information array so that an inverse correlation becomes large with respect to the dot appearance information array determined by at least one of the second quantization. In the image processing program according to any one of claims 32 to 35,
In the first quantization, an image processing program for realizing a function of selecting a density having lightness lower than the second density as the first density.

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