JP3985147B2 - Image processing apparatus, image processing method, and medium on which image processing program is recorded - Google Patents

Description

となる。
【０１２０】
ｍ１：ｍ２＝ｎ１：ｎ２であるから、
ｎ１＝ｍ１(ｎ１＋ｎ２)／(ｍ１＋ｍ２)
＝ｙmin(255−ａ・ｙdif)／(255−ｙdif)
一方、Ｙ＝ａｙ＋ｂであるから、ｂ＝Ｙ−ａｙとなり、よって、
ｂ＝Ｙmin−ａ・ｙmin
＝ｙmin｛(255−ａ・ｙdif)／(255−ｙdif)−ａ｝ …（１０）
となる。また、Ｙmax を用いて求めると、
ｂ＝Ｙmax−ａ・ｙmax
＝255−(255−ｙmax)(255−ａ・ｙdif)／(255−ｙdif)−ａ・ｙmax …（１１）
以上のようにしてパラメータｂを得ることができ、ステップＳ２０４を終了する。
【０１２１】
ところで、輝度の変換時に、毎回、上記変換式（Ｙ＝ａｙ＋ｂ）を実行するのは非合理的である。というのは、輝度ｙの取りうる範囲が「０」〜「２５５」でしかあり得ないため、予め輝度ｙが取りうる全ての値に対応して変換後の輝度Ｙを求めておくことも可能である。従って、ステップＳ２０６にてこの対応関係を求め、図１８に示すようなテーブルとして記憶しておく。
【０１２２】
このような変換テーブルが形成されたところで画像データを変更することが可能になる。しかし、このような輝度の範囲の拡大によってコントラストを強調するだけでなく、合わせて明るさを調整することも極めて有効である。例えば、図１９にて実線で示すように輝度分布の山が全体的に暗い側に寄っている場合には鎖線で示すように全体的に明るい側に山を移動させると良いし、逆に、図２０にて実線で示すように輝度分布の山が全体的に明るい側に寄っている場合には鎖線で示すように全体的に暗い側に山を移動させると良い。
【０１２３】
各種の実験を行った結果、本実施形態においては、輝度分布におけるメジアンｙｍｅｄを求め、同メジアンｙｍｅｄが「８５」未満である場合に暗い画像と判断して以下のγ値に対応するγ補正で明るくする。
【０１２４】
γ＝ｙｍｅｄ／８５ …（１２）
あるいは、
γ＝（ｙｍｅｄ／８５）**（１／２） …（１３）
とする。
【０１２５】
この場合、γ＜０．７となっても、γ＝０．７とする。このような限界を設けておかないと夜の画像が昼間のようになってしまうからである。なお、明るくしすぎると全体的に白っぽい画像になってコントラストが弱い画像になりやすいため、彩度を合わせて強調するなどの処理が好適である。
【０１２６】
一方、メジアンｙｍｅｄが「１２８」より大きい場合に明るい画像と判断して以下のγ値に対応するγ補正で暗くする。
【０１２７】
γ＝ｙｍｅｄ／１２８ …（１４）
あるいは、
γ＝（ｙｍｅｄ／１２８）**（１／２） …（１５）
とする。この場合、γ＞１．３となっても、γ＝１．３として暗くなり過ぎないように限界を設けておく。なお、暗くしすぎると色が乗りすぎて濃い画像になるので、合わせて彩度強調を弱くするなどの処理が好適である。ただし、明るい背景の中の被写体に対してはこのような暗くする処理はかえって悪影響を及ぼす場合もある。例えば、空が画像の半分をしめるような風景画像や晴れた日の記念写真などでは、ただでさえ逆光で顔が暗くつぶれ気味であることが多いからである。これらの画像の場合は暗い部分と明るい部分とが混じっているので輝度の標準偏差ｙｓｔｄを求めると比較的高い値となっていることが多い。従って、輝度の標準偏差ｙｓｔｄ＞７０の場合には暗くするためのγ補正を行わないようにする。
【０１２８】
なお、このγ補正は変換前の輝度分布に対して行っても良いし、変換後の輝度分布に対して行っても良い。γ補正をした場合における対応関係を図２１に示しており、γ＜１であれば上方に膨らむカーブとなり、γ＞１であれば下方に膨らむカーブとなる。むろん、かかるγ補正の結果も図１８に示すテーブル内に反映させておけばよく、ステップＳ２０８にてテーブルデータに対して同補正を行っておく。
【０１２９】
最後に、ステップＳ２１０にて画像データの変換を行う。ここまでは輝度を変換するための対応関係を求めてきており、例えば、ＲＧＢ座標軸における成分値（Ｒp,Ｇp,Ｂp ）についての変換関係ではなかった。しかしながら、（２）式の変換式は、このＲＧＢの成分値（Ｒp,Ｇp,Ｂp ）との対応関係においても当てはめることができる。すなわち、変換前の成分値（ｒ，ｇ，ｂ）に対して変換後の成分値（Ｒ，Ｇ，Ｂ）は、
Ｒ＝ａ・ｒ＋ｂ …（１６）
Ｇ＝ａ・ｇ＋ｂ …（１７）
Ｂ＝ａ・ｂ＋ｂ …（１８）
として求めることもできる。これは（２）式と（４）式とがともに線形の対応関係を示していることから明らかである。また、輝度ｙ，Ｙが階調「０」〜階調「２５５」であるのに対応してＲＧＢの各成分値（ｒ，ｇ，ｂ），（Ｒ,Ｇ,Ｂ ）も同じ範囲となっており、上述した輝度ｙ，Ｙの変換テーブルをそのまま利用すればよいといえる。
【０１３０】
従って、ステップＳ２１０では全画素の画像データ（ｒ，ｇ，ｂ）について（１６）〜（１８）式に対応する変換テーブルを参照し、変換後の画像データ（Ｒ,Ｇ,Ｂ ）を得るという処理を繰り返すことになる。
【０１３１】
次に、上記構成からなる本実施形態の動作を順を追って説明する。
【０１３２】
スキャナ１１などで写真を撮像したとすると、同写真をＲＧＢの階調データで表した画像データがコンピュータ２１に取り込まれ、ＣＰＵは図５及び図６に示す画像処理のプログラムを実行して画像データのコントラストを強調する処理を実行する。
【０１３３】
まず、ステップＳ１０２では画像データを所定の誤差内となる範囲で間引き、選択した画素についての輝度ｙを求めて分布を取る。このままの分布を使用することはできず、まず、画像が白黒のような二値画像でないかステップＳ１０４にて判断するとともに、ステップＳ１０８では自然画か否かを判断する。二値画像である場合や自然画でない場合などを除き、ステップＳ１１０では画像データに枠部がないか判断し、枠部があれば除いて得られた輝度分布について上端と下端の０．５％の範囲を除去して分布の両端ｙmax ，ｙmin を取得する。
【０１３４】
輝度分布の両端ｙmax ，ｙmin が得られたら、
Ｙ＝ａｙ＋ｂ …（２）
ａ＝２４５／（ｙmax−ｙmin） …（８）
ｂについては、以下のいずれか
ｂ＝５−ａ・ｙmin …（９）
ｂ＝ｙmin｛(255−ａ・ｙdif)／(255−ｙdif)−ａ｝ …（１０）
ｂ＝255−(255−ｙmax)(255−ａ・ｙdif)／(255−ｙdif)−ａ・ｙmax …（１１）
なる関係式より、ステップＳ２０４にてパラメータａ，ｂを求めるとともに、ステップＳ２０６では輝度ｙから輝度Ｙへの変換関係をテーブルに記憶する。ステップＳ２０８では必要に応じてγ補正を実行し、完成した変換テーブルを参照してステップＳ２１０では全画素についての画像データを変換する。
【０１３５】
むろん、上述したように二値画像や自然画でない場合においてはかかる画像処理は行われないが、本発明の画像処理が行われた場合には、写真の状態では非常にコントラストが弱かったにもかかわらず、輝度の範囲を広げるように補正することにより、明暗がはっきりして鮮明な画像を得られるようになる。
【０１３６】
なお、上述した実施形態においては、拡大率の制限などを一定としているが、コンピュータ２１上では所定のＧＵＩを介してユーザーが選択できるようにしても良い。また、ユーザーが画像データの一部を指定して当該範囲内でのみかかるコントラストの強調処理を実行するようにすることも可能である。
【０１３７】
このように、ステップＳ１０２で間引きするなどしながら画像データの画素について輝度ｙを求めた後、上端と下端において所定の分布割合だけ内側に入った端部を当該輝度分布の端部をみなすことにより（ステップＳ１１６）、輝度の再現可能な範囲内での拡大率に対応するパラメータａと、オフセット量に対応するパラメータｂとを得ることができるようになるため、変換元の輝度ｙに対して変換先輝度ＹをＹ＝ａｙ＋ｂなる関係式などを利用して自動的に変換可能となる。
【０１３８】
なお、ビデオカメラ１４の入力画像については、演算速度が間に合わないこともあり得る。従って、そのような場合には撮影のシーンごとに輝度の検出を行って輝度の分布を検知し、そのシーンにおいては同様の傾向であるものと想定して輝度の拡大テーブルを作成しておくとともに、フレームごとに同拡大テーブルに対応して輝度分布を拡大するようにすればよい。むろん、十分な演算速度があれば各フレームごとに変換するようにしても良い。むろん、同様の変換は受像機の側で行うことも可能である。
【図面の簡単な説明】
【図１】本発明の一実施形態にかかる画像処理装置が適用される画像処理システムのブロック図である。
【図２】同画像処理装置の具体的ハードウェア構成例を示すブロック図である。
【図３】本発明の画像処理装置の他の適用例を示す概略ブロック図である。
【図４】本発明の画像処理装置の他の適用例を示す概略ブロック図である。
【図５】本発明の画像処理装置における輝度の分布抽出処理部分を示すフローチャートである。
【図６】本発明の画像処理装置における輝度変換処理部分を示すフローチャートである。
【図７】変換元の画像における座標を示す図である。
【図８】サンプリング周期を示す図である。
【図９】サンプリング画素数を示す図である。
【図１０】変換元の画像とサンプリングされる画素の関係を示す図である。
【図１１】白黒の画像を示す図である。
【図１２】白黒の画像の輝度分布を示す図である。
【図１３】枠部のある画像を示す図である。
【図１４】枠部のある画像の輝度分布を示す図である。
【図１５】輝度分布の端部処理と端部処理にて得られる端部を示す図である。
【図１６】輝度分布の拡大と再現可能な範囲を輝度の範囲を示す図である。
【図１７】輝度分布の拡大率に制限を与える場合を示す図である。
【図１８】輝度分布を拡大する際の変換テーブルを示す図である。
【図１９】γ補正で明るくする概念を示す図である。
【図２０】γ補正で暗くする概念を示す図である。
【図２１】γ補正で変更される輝度の対応関係を示す図である。
【符号の説明】
１０…画像入力装置
１１…スキャナ
１１ｂ…スキャナ
１２…デジタルスチルカメラ
１２ａ…デジタルスチルカメラ
１２ｂ…デジタルスチルカメラ
１３ｂ…モデム
２０…画像処理装置
２１…コンピュータ
２２…ハードディスク
３０…画像出力装置
３１…プリンタ
３１ａ…プリンタ
３１ｂ…プリンタ
３２…ディスプレイ
３２ａ…ディスプレイ[0001]
BACKGROUND OF THE INVENTION
The present invention relates to an image processing apparatus, an image processing method, and a medium on which an image processing program is recorded, and more particularly to an image processing apparatus, an image processing method, and a medium on which an image processing program is recorded.
[0002]
[Prior art]
Conventionally, as this type of image processing apparatus, an apparatus disclosed in Japanese Patent Publication No. 7-66318 is known.
[0003]
In the image processing apparatus shown in the same publication, the relationship between the luminance y ′ after conversion and the luminance y ′ after conversion as shown in equation (1), and the image is based on the parameter a or parameter b selected by the operator. The brightness of the data is converted. As a result, an image in which the contrast is enhanced for image data having a low contrast is obtained.
[0004]
y ′ = ay + b (1)
[0005]
[Problems to be solved by the invention]
In the above-described conventional image processing apparatus, a plurality of settings with different levels of contrast strength are prepared in advance and switched. Therefore, the most suitable one corresponding to actual image data cannot be automatically applied. In particular, in the case of changing based on the expression (1), if the whole image is bright, only the brightness is emphasized. If the whole image is dark, the darkness is only emphasized. Sometimes.
[0006]
Further, although the contrast strength can be operated on a television or the like, it has not been possible to automate the execution of optimum enhancement on each image.
[0007]
SUMMARY An advantage of some aspects of the invention is that it provides an image processing apparatus capable of automatically adjusting contrast, an image processing method, and a medium on which an image processing program is recorded.
[0008]
[Means for Solving the Problems]
In order to achieve the above object, an image processing apparatus that performs predetermined image processing on image data of an image composed of a plurality of pixels, totals the luminance distribution based on the luminance at each pixel, and totals The luminance distribution of each pixel in the image data is widely dispersed in an effective luminance range that can be taken by the image data, with the luminance distribution in which the inner portion is regarded as an end portion by a predetermined distribution ratio from the actual edge portion in the obtained luminance distribution. It is good also as a structure which converts information.
[0009]
In the above configuration, the luminance distribution as a whole is totaled based on the luminance at each pixel, and the luminance distribution in which the inner portion is regarded as the end portion by a predetermined distribution ratio from the actual end portion in the totaled luminance distribution is the image. The luminance information of each pixel in the image data is converted so as to be widely distributed in an effective luminance range that the data can take. That is, when the image data represents the information of each pixel by decomposing the image into dot matrix pixels, the brightness distribution in the image data is obtained, so that the width of the contrast in the image data is to some extent The distribution can be quantified, and the distribution can be expanded corresponding to the reproducible range.
[0010]
Needless to say, the numerical value is not necessarily a specific numerical value, and may be handled as a numerical value in the process, or may be handled as a signal magnitude.
The invention according to claim 1 is an image processing apparatus that performs predetermined image processing on image data of an image composed of a plurality of pixels, and totalizes the luminance distribution based on the luminance at each pixel, A luminance distribution detecting means for detecting a luminance distribution in which the inner portion is regarded as an end portion from the actual end portion in the aggregated luminance distribution as an end portion, and the detected luminance distribution is effective for taking the image data. An image data conversion unit that detects an enlargement factor that expands in the luminance range and converts luminance information of each pixel in the image data using the enlargement factor is provided.
[0011]
In order to obtain the luminance distribution in handling the image data of the predetermined image, the luminance distribution detecting means adds up the luminance distribution as a whole based on the luminance at each pixel, and determines the predetermined luminance from the actual end of the tabulated luminance distribution. A luminance distribution in which the inner portion is regarded as an end portion by the distribution ratio is detected. Then, using the detected luminance distribution, the image data conversion means detects an enlargement ratio for expanding the luminance distribution in an effective luminance range that can be taken by the image data, and uses the enlargement ratio to detect the image data. The luminance information of each pixel is converted.
[0012]
That is, the so-called contrast width from the brightest brightness to the darkest brightness can be determined by obtaining the brightness distribution of the image data in pixel units, and the contrast magnification can be determined by comparing with the reproducible brightness width range. Therefore, after that, it is sufficient to enlarge the luminance distribution so that the enlargement ratio is obtained. For example, after the luminance distribution as a whole is aggregated based on the luminance at each pixel, the luminance distribution is the same when the aggregated luminance distribution is not widely dispersed in the effective luminance range that the image data can take. The luminance information of each pixel in the image data is converted so as to be widely distributed in the luminance range.
[0013]
Any number of methods are possible for enlarging the luminance distribution, and the luminance distribution of the image data may be enlarged within a possible range. To explain the main point, the statistical width of the detected luminance distribution is compared with the width of the reproducible luminance range to obtain the degree of enlargement as the enlargement ratio, and the upper and lower ends of the enlarged luminance distribution. Is to obtain an adjustment value so as to be within the luminance range, and to individually correct the luminance of each pixel. As a specific example, the invention according to claim 2 is the image processing apparatus according to claim 1, wherein the image data converting means has a luminance before conversion when the reproducible luminance range is yrange. The luminance Y of the conversion destination is obtained from the maximum value ymax and the minimum value ymin of the distribution range of y and the luminance based on the following equation.
[0014]
Y = ay + b (2)
However,
a = yrange / (ymax−ymin) (3)
b = −a · ymin (4)
In the above conversion equation, if Y <0, Y = 0, and if Y> yrange, Y = yrange.
[0015]
This conversion is so-called linear expansion, and although the conversion formula itself is the same as the conventional one, it is significant that the parameters are selected by the image data conversion means. Regardless of the selection of b, Y = 0 when y = ymin, and Y = yrange when y = ymax. Then, the luminance distribution is uniformly spread within a range of yrange which is a reproducible luminance range. In this example, it is a so-called narrow-sense linear transformation, and of course, it is not necessary to be limited to this, and a broad-sense nonlinear transformation can also be implemented. Further, it goes without saying that the conversion formula is merely an example and can be applied even if the conversion formula has the same meaning.
[0016]
Further, it may not be possible to deal with a case where the entire image is bright or dark just by increasing the contrast, and the image data conversion means obtains the maximum distribution luminance of the conversion source luminance y, and the maximum distribution luminance of the same. A configuration may be adopted in which the luminance Y of the conversion destination is obtained by γ correction based on the affiliation range.
[0017]
As a method for determining whether or not the image data is generally bright, the maximum distribution luminance of the conversion source luminance y is used, and if this maximum distribution luminance is on the bright side, the entire distribution is darkened by γ correction. If the brightness is on the dark side, the entire brightness is automatically corrected, which cannot be obtained only by contrast enhancement, by making the entire screen brighter by γ correction. Here, the maximum distribution luminance of the luminance y of the conversion source may be obtained by median or may be obtained by an average value.
[0018]
When converting the luminance by various methods, the invention according to claim 3 calculates the luminance Y of the conversion destination for each luminance y within the range that the luminance y of the conversion source can take, The correspondence relationship with each luminance Y of the conversion destination is stored, and the conversion is performed by referring to the correspondence relationship at the time of conversion.
[0019]
Although it is not impossible to calculate the luminance every time based on the conversion formula, the range of values that the luminance distribution can take is determined. For this reason, if the conversion destination luminance Y is calculated and stored in advance based on the conversion source luminance y, it is possible to perform conversion only by invoking the correspondence at the time of conversion.
[0020]
In converting the luminance, the image data may be included as luminance data, or may be indirect only including luminance data. Of course, if direct luminance data is included, it may be converted, and even if it is indirect luminance data, it may be converted into luminance data before performing predetermined luminance conversion. However, the luminance conversion does not have to be very accurate, and it can be said that it is sufficient to know roughly.
[0021]
In that sense, since strict accuracy is not required, the invention according to claim 4 is the image processing apparatus according to any one of claims 1 to 3, wherein the image data corresponds to a plurality of brightness levels. In this case, the luminance is calculated by linear addition of the same component values.
[0022]
When the image data is represented by so-called RGB (red, green, and blue) gradation data, it can be said that each component value for red, green, and blue corresponds to the luminance. For this reason, it can be said that linear addition of the same component values sufficiently represents luminance, and can be an extremely easy conversion method.
[0023]
The luminance distribution as an image does not necessarily have to be obtained for all the pixels of the image data as the luminance for each pixel is obtained. For example, the luminance distribution is obtained by thinning out the image data corresponding to a predetermined extraction rate. It is good.
[0024]
For the purpose of obtaining the distribution, it is possible to obtain a luminance distribution with a certain degree of certainty according to the extraction rate even if thinning is performed at a predetermined extraction rate without obtaining luminance for all pixels.
[0025]
Here, although there are various thinning methods, a predetermined number of extractions may be secured on the short side in the vertical and horizontal ranges.
[0026]
Since the image is flat, the image data is also naturally distributed in the vertical and horizontal directions, but in determining a certain extraction rate, by ensuring a certain number of extractions at least on the short side, the extraction rate is reduced. The certainty corresponding to it is held.
[0027]
Furthermore, when obtaining the luminance distribution, a configuration may be adopted in which the inner portion is regarded as the end portion by a predetermined distribution ratio from the actual end portion.
[0028]
Considering the luminance distribution of the image data statistically, it is reasonable to consider that the distribution is up to both end portions within a reproducible luminance range although it is extremely small. Therefore, both ends of the actual luminance distribution are always at both ends in a reproducible luminance range. If these both ends are employed, the enlargement ratio is substantially “1”, and thus the original effect cannot be achieved.
[0029]
However, if the predetermined distribution ratios at both ends are excluded, the bottom portion having a very small distribution is statistically ignored and appropriately ignored. For this reason, this range is used as a reference for determining the degree of enlargement.
[0030]
The predetermined distribution ratio only needs to be capable of ignoring the bottom part with extremely small distribution, and may be a certain number of pixels of the total number of pixels, or the distribution is less than a certain number. Sometimes it can be considered as an end.
[0031]
Furthermore, the invention according to claim 5 is the image processing apparatus according to any one of claims 1 to 4, wherein the range of the brightness distribution to be enlarged is a predetermined amount from the end of the actual reproducible range. The configuration is set inside.
[0032]
In an actual image, there are a highlight portion and a high shadow portion, and the human eye is likely to realize a subtle difference in these portions. Therefore, if the enlargement is applied to the end of the so-called reproducible luminance range, the highlight portion will appear white and the high shadow portion will appear black.
[0033]
However, by setting the range of the luminance distribution to be enlarged inward by a predetermined amount from the end part of the actual reproducible range, no intentional enlargement is performed at both end parts.
[0034]
Furthermore, in the invention according to claim 1, the image data conversion means limits the enlargement ratio.
[0035]
It may be natural that the contrast is narrow. For example, in the evening scenery, it is natural that the width of the luminance distribution is narrow, and if it is enlarged more than necessary, it becomes a daytime scenery. A similar example is possible in other cases, and this phenomenon is avoided by setting a limit on the expansion range of the luminance distribution.
[0036]
Furthermore, a configuration in which the corresponding position of the luminance distribution range before conversion within the reproducible range and the corresponding position of the luminance distribution range after conversion may be held.
[0037]
If the luminance distribution is used as much as possible within a reproducible range, there is no remaining range in which the luminance distribution can be expanded. However, if the expansion range is limited, there remains a remaining range in which the luminance distribution can be expanded. In other words, there remains a degree of freedom in which range is enlarged around, and the atmosphere of the image may change depending on the center. Therefore, the corresponding position of the luminance distribution range before conversion within the reproducible range and the corresponding position of the luminance distribution range after conversion are held so that the center does not change.
[0038]
Various ways of capturing the center of the luminance distribution of the image in this sense are possible. The invention according to claim 1 is characterized in that the image data conversion means is a ratio of the remaining area remaining on the upper end side of the luminance distribution and the remaining area remaining on the lower end side within the effective luminance range before the conversion. In the effective range, the conversion is performed so that the ratio between the remaining area remaining on the upper end side of the luminance distribution after the conversion and the remaining area remaining on the lower end side coincides.
[0039]
That is, since it is only necessary to hold the center substantially, it is not always necessary to directly hold and hold the same center, but conversely, the enlargement remaining at the upper and lower ends of the luminance distribution range before conversion A possible range is captured, and the luminance distribution is expanded so that the ratio of this range is maintained even after conversion, so that the center is substantially maintained.
[0040]
Further, the invention according to claim 6 is the image processing apparatus according to any one of claims 1 to 5, wherein the binary image data is determined based on the luminance distribution, and if the binary image data is used, the luminance is determined. The distribution is not expanded.
[0041]
Since it can be said that there is no substantial luminance distribution for the binary image, if the binary image data is determined from the luminance distribution, the luminance distribution is not expanded.
[0042]
Since the binary image data can also have a certain color, it can have two luminance levels corresponding to the presence or absence of the color. Although it is possible to determine whether or not the brightness of the color, if there is no information suggesting it, the invention according to claim 7 can be reproduced in the image processing apparatus according to claim 6. It is configured to determine that the binary image data is black and white when the luminance distribution is concentrated at both ends in the range.
[0043]
That is, for a black and white image, it can be determined that the luminance distribution is concentrated at both ends within the reproducible range.
[0044]
Further, the frame portion of the image data may be determined based on the protruding luminance distribution, and if there is a frame portion, the frame data may not be used for expanding the luminance distribution.
[0045]
When an image is processed, it can frequently occur that it has a frame. If it exists as a single-color frame, only the luminance distribution corresponding to that color naturally protrudes. Accordingly, if such a prominent luminance distribution is used as a criterion for the enlargement determination, an effective determination cannot be made. Therefore, it is determined that the frame portion is not used for expanding the luminance distribution.
[0046]
Furthermore, as an example, a configuration may be adopted in which the luminance distribution concentrated at the end within the reproducible range is determined to be the frame portion.
[0047]
A white frame or a black frame is frequently used and may be generated depending on a trimming result, and corresponds to an end portion within a reproducible range. Therefore, the luminance distribution concentrated on the end portion is determined as the frame portion.
[0048]
By the way, the invention according to claim 8 is configured such that in the image processing apparatus according to any one of claims 1 to 7, the luminance distribution is not enlarged when the image data is not a natural image.
[0049]
The narrowness of contrast tends to be a problem for natural pictures such as photographs, and it can be said that it is almost unnecessary for things like business graphs. Conversely, modifying something like a business graph can lead to a difference from the creator's image. Therefore, the luminance distribution is expanded only in the case of such a natural image.
[0050]
As an example of determining whether or not the image is a natural image, the invention according to claim 9 is the image processing device according to claim 8, wherein the image data is determined not to be a natural image when the luminance distribution exists in a line spectrum shape. The natural image determining means is provided.
[0051]
It can be said that the brightness distribution has a smooth width as a feature of the natural image. Therefore, if the luminance distribution appears in a line spectrum, it can be determined that the image is not a natural image. In the invention according to claim 9 configured as described above, the natural image determination means determines the state of the luminance distribution, and determines that the image data is not a natural image when it exists in the form of a line spectrum. Will not be expanded.
[0052]
As described above, the method for converting the image data so as to expand the distribution corresponding to the reproducible range by obtaining the luminance distribution in the image data is not necessarily limited to a substantial apparatus. It can be easily understood that it also functions as a method. For this reason, the invention according to claim 10 is an image processing method for performing predetermined image processing on image data of an image composed of a plurality of pixels, and the overall luminance distribution is based on the luminance at each pixel. An effective luminance range in which the image data can be taken by detecting the luminance distribution obtained by counting and detecting the luminance distribution in which the inner portion is regarded as the end portion by a predetermined distribution ratio from the actual end portion in the aggregated luminance distribution In the image data, the enlargement ratio is detected within a predetermined limit, and the information on the luminance of each pixel in the image data is converted using the enlargement ratio. The ratio of the remaining area remaining on the upper end side of the luminance distribution and the remaining area remaining on the lower end side within the range, and the remaining area and lower end remaining on the upper end side of the luminance distribution after conversion within the effective range. Left on the side And the ratio of the remaining area to be found is a structure to perform conversion so match.
[0053]
That is, it is not necessarily limited to a substantial apparatus, and there is no difference that the method is effective.
[0054]
By the way, such an image processing apparatus may exist alone, or may be used in a state of being incorporated in a certain device. The idea of the invention is not limited to this, and includes various aspects. . Therefore, it can be changed as appropriate, such as software or hardware.
[0055]
As an example, a printer driver that converts image data corresponding to printing ink based on input image data and prints it on a predetermined color printer supports a range that can be reproduced by obtaining a luminance distribution in the image data. Thus, the image data can be converted to expand the distribution.
[0056]
In other words, the printer driver converts the image data input corresponding to the printing ink. At this time, the brightness distribution of the input image data is obtained, and the distribution is expanded corresponding to the reproducible range. The input image is converted so as to obtain a distribution and printed.
[0057]
In the case of software for an image processing apparatus as an embodiment of the idea of the invention, it naturally exists on a recording medium on which such software is recorded and must be used. As an example, the invention according to claim 11 is a medium in which image data of an image composed of a plurality of pixels is input by a computer and an image processing program for performing predetermined image processing is recorded, and the luminance at each pixel A luminance distribution as a whole based on the luminance distribution, detecting a luminance distribution in which the inner portion is regarded as an end portion by a predetermined distribution ratio from an actual end portion in the totaled luminance distribution, and the detected luminance distribution Detecting within a predetermined limit an enlargement ratio that expands in an effective luminance range that can be taken by the image data, and using the enlargement ratio to convert luminance information of each pixel in the image data, The ratio of the remaining area remaining on the upper end side of the luminance distribution and the remaining area remaining on the lower end side within the effective luminance range within the effective luminance range before conversion is changed within the effective range. After the the ratio of the remaining area remaining in the residual area and the lower end side remaining at the upper end of the luminance distribution, it is constituted comprising a step of performing conversion to match.
[0058]
Of course, the recording medium may be a magnetic recording medium, a magneto-optical recording medium, or any software recording medium to be developed in the future. In addition, the duplication stages such as the primary duplication product and the secondary duplication product are equivalent without any question. In addition, even when the communication method is used as a supply method, the present invention is not changed, and the same applies to the case where data is written on a semiconductor chip.
[0059]
Further, even when a part is software and a part is realized by hardware, there is nothing completely different in the idea of the invention, and a part is stored on a recording medium, and it is appropriately changed as necessary. It may be in the form of being read. Furthermore, it goes without saying that the present invention can also be applied to an image processing apparatus built in a color facsimile machine, a color copier, a color scanner, a digital camera, a digital video, or the like.
[0060]
【The invention's effect】
As described above, according to the present invention, by obtaining the luminance distribution, it becomes possible to quantitatively handle the width of the contrast of the image, and the degree of enlargement within a reproducible range can be obtained. Therefore, an image processing apparatus capable of automating contrast enhancement can be provided.
[0061]
According to the invention of claim 2, the luminance distribution can be effectively expanded within a predetermined range of gradations.
[0062]
Furthermore, the degree of brightness that cannot be corrected only by contrast enhancement can be adjusted.
[0063]
Further, according to the invention of claim 3, conversion can be facilitated.
[0064]
Furthermore, according to the fourth aspect of the present invention, the luminance can be easily obtained with a necessary and sufficient accuracy.
[0065]
Furthermore, the amount of processing can be reduced.
[0066]
Furthermore, the bias of the image extraction point is eliminated, and the luminance distribution is likely to be accurate.
[0067]
Furthermore, it is possible to obtain a luminance distribution that is more effective for judgment.
[0068]
Furthermore, according to the fifth aspect of the present invention, the highlight portion and the high shadow portion can be prevented from being crushed.
[0069]
Furthermore, according to the first aspect of the present invention, it is possible to prevent the contrast of the image from being changed too much by changing the atmosphere of the image.
[0070]
Furthermore, according to the first aspect, it is possible to maintain the atmosphere expressed by the brightness of the image, and to easily execute it.
[0071]
Furthermore, according to the invention according to claim 6, it is possible to easily determine an unnecessary condition for expansion of the luminance distribution so as not to perform the expansion, and further, according to the invention according to claim 7, It is possible to efficiently determine a monochrome image having a high frequency.
[0072]
Furthermore, it is possible to prevent the processing from being inaccurate due to the brightness of the frame portion that tends to appear in the image, and it is possible to easily determine a frequently-used black and white frame portion.
[0073]
Furthermore, according to the invention according to claim 8, it can be performed only in the case of a natural image that needs to be expanded in luminance distribution. Further, according to the invention according to claim 9, whether or not it is a natural image. Can be easily determined.
[0074]
According to the invention of claim 10, it is possible to provide an image processing method that can quantitatively handle an image such as the contrast width of an image by obtaining a luminance distribution and automate contrast enhancement. According to the eleventh aspect of the present invention, it is possible to provide a medium on which an image processing program for automating contrast enhancement is recorded.
[0075]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, embodiments of the present invention will be described with reference to the drawings.
[0076]
FIG. 1 is a block diagram illustrating an image processing system according to an embodiment of the present invention, and FIG. 2 is a block diagram illustrating a specific hardware configuration example.
[0077]
In the figure, an image input device 10 captures an image and outputs image data to the image processing device 20. The image processing device 20 performs image processing such as predetermined contrast enhancement and outputs the image data to the image output device 30. Then, the image output device 30 displays an image with enhanced contrast.
[0078]
Here, a specific example of the image input device 10 corresponds to the scanner 11, the digital still camera 12, or the video camera 14, and a specific example of the image processing device 20 corresponds to a computer system including the computer 21 and the hard disk 22. Specific examples of the output device 30 include a printer 31, a display 32, and the like.
[0079]
In the present image processing system, since an optimum contrast is to be given to an image having a low contrast, it is image data obtained by taking a photograph with a scanner 11 as the image input device 10 or a digital still camera 12. The image data having a low contrast taken in step 1 is processed and input to a computer system as the image processing apparatus 20.
[0080]
The image processing apparatus 20 determines at least a luminance distribution detecting unit that extracts a luminance distribution, and an extent that the luminance distribution can be expanded within a reproducible range based on the detected luminance distribution. Image data conversion means for converting data is configured. Of course, the image processing apparatus 20 may also be a color conversion unit that corrects a color difference for each model, or a resolution conversion unit that converts a resolution corresponding to each model. . In this example, the computer 21 executes each image processing program stored in the internal ROM or the hard disk 22 while using a RAM or the like. Such an image processing program is supplied via various recording media such as a CD-ROM, floppy disk, and MO, and connected to an external network via a public communication line by a modem or the like. And downloading and installing data.
[0081]
The execution result of this image processing program is obtained as image data with enhanced contrast as will be described later, and is printed by the printer 31 that is the image output device 30 based on the obtained image data, or by the same image output device 30. It is displayed on a certain display 32. More specifically, this image data is RGB (green, blue, red) gradation data, and the image is dots arranged in a grid pattern in the vertical direction (height) and the horizontal direction (width). It is configured as matrix data. That is, the image data represents information of each pixel by decomposing the image into pixels in a dot matrix.
[0082]
In the present embodiment, a computer system is incorporated between image input / output devices to perform image processing. However, such a computer system is not necessarily required, and a digital still camera as shown in FIG. A system in which an image processing device for emphasizing contrast is incorporated in 12a and the converted image data is displayed on the display 32a or printed on the printer 31a may be used. As shown in FIG. 4, in the printer 31b that inputs and prints image data without using a computer system, the image data input through the scanner 11b, the digital still camera 12b, the modem 13b, or the like is automatically received. It is also possible to configure so as to enhance contrast.
[0083]
Of the image processing executed by the computer 21, luminance distribution detection processing corresponding to luminance distribution detection means and luminance conversion processing corresponding to image data conversion means are shown in FIGS. 5 and 6, respectively.
[0084]
FIG. 5 mainly corresponds to luminance distribution detection processing. First, the luminance distribution detection processing will be described.
[0085]
Before describing how to represent luminance, the pixels to be distributed will be described. As shown in step S102 of FIG. 5, a thinning process for thinning out the target pixels is executed. As shown in FIG. 7, in the case of a bitmap image, it is formed as a two-dimensional dot matrix consisting of predetermined dots in the vertical direction and predetermined dots in the horizontal direction, and all pixels can be obtained if an accurate luminance distribution is obtained. It is necessary to examine the brightness. However, this distribution extraction process is intended to determine the width of the distribution and need not be accurate. Therefore, it is possible to perform thinning to such an extent that it is within a certain error range. According to the statistical error, the error with respect to the number of samples N can be expressed as 1 / (N ** (1/2)). However, ** represents a power. Therefore, in order to perform processing with an error of about 1%, N = 10000.
[0086]
Here, the bitmap screen shown in FIG. 7 has the number of pixels of (width) × (height), and the sampling period ratio is
ratio = min (width, height) / A + 1 (5)
And Here, min (width, height) is the smaller of width and height, and A is a constant. Further, the sampling period ratio here indicates how many pixels are sampled, and the pixels marked with ◯ in FIG. 8 indicate the case where the sampling period ratio = 2. That is, one pixel is sampled every two pixels in the vertical and horizontal directions, and every other pixel is sampled. The number of sampling pixels in one line when A = 200 is as shown in FIG.
[0087]
As can be seen from the figure, except for the case where the sampling period ratio = 1 in which sampling is not performed, when there is a width of 200 pixels or more, the number of samples is at least 100 pixels. Therefore, in the case of 200 pixels or more in the vertical direction and the horizontal direction, (100 pixels) × (100 pixels) = (10000 pixels) is secured, and the error can be reduced to 1% or less.
[0088]
Here, the reason for using min (width, height) as a reference is as follows. For example, as shown in FIG. 10 (a), when width >> height is satisfied and the sampling period ratio is determined by the longer width, as shown in FIG. 10 (b). In addition, in the vertical direction, only two lines of the upper end and the lower end may be extracted. However, if the sampling period ratio is determined based on the smaller one as min (width, height), thinning is performed so as to include the intermediate portion in the smaller vertical direction as shown in FIG. Will be able to.
[0089]
In this example, thinning is performed with an accurate sampling period for pixels in the vertical and horizontal directions. This is suitable for processing while thinning out pixels that are sequentially input. However, when all the pixels have been input, the pixels may be selected by randomly specifying coordinates in the vertical direction or the horizontal direction. In this way, when the necessary minimum number of pixels, such as 10,000 pixels, is determined, the extraction process is repeated at random until the number of pixels reaches 10,000, and the extraction is stopped when the number of pixels reaches 10,000.
[0090]
If the pixel data for the selected pixel has luminance as its component element, the distribution can be obtained using the luminance value. However, even in the case of image data in which the luminance value is not a direct component value, the component value that indirectly represents the luminance is provided. Accordingly, the luminance value can be obtained by performing conversion from the color space where the luminance value is not a direct component value to the color space where the luminance value is a direct component value.
[0091]
Such color conversion between different color spaces is not uniquely determined by a conversion formula, but a mutual relationship is obtained for each color space having the respective component values as coordinates, and this correspondence is stored. It is necessary to perform sequential conversion with reference to the color conversion table. In relation to the table, the component value is expressed as a gradation value, and in the case of 256 gradations having a three-dimensional coordinate axis, a color conversion table of about 16.7 million (256 × 256 × 256) elements is obtained. Must have. As a result of considering the efficient use of storage resources, correspondence is not prepared for all coordinate values, but usually correspondence is prepared for appropriate discrete lattice points, and interpolation is used together. Like to do. Since this interpolation calculation is possible through several multiplications and additions, the amount of calculation processing becomes enormous.
[0092]
In other words, if a full-size color conversion table is used, the processing amount is reduced, but the table size becomes unrealistic. If the table size is made realistic, the calculation processing amount becomes unrealistic. Many.
[0093]
In view of such a situation, in the present embodiment, as used in the case of a television or the like, the following conversion formula for obtaining luminance from the three primary colors of RGB is adopted. That is, for the luminance yp at the point P, from the RGB component values (Rp, Gp, Bp),
yp = 0.30Rp + 0.59Gp + 0.11Bp (6)
And In this way, the luminance value can be obtained by only three multiplications and two additions.
[0094]
In this embodiment, as a result of targeting the RGB color space, such a conversion formula is adopted, but since each component value indicates the brightness of the color in the background, each component is When the value is viewed alone, it has a property of linearly corresponding to the luminance. Therefore, more roughly speaking, without considering each addition ratio simply
yp = (Rp + Gp + Bp) / 3 (7)
It is not impossible to simplify it, and
yp = Gp (7) '
In this way, it is also possible to use the green component value having the largest proportion in the formula (6) as the luminance value.
[0095]
In the thinning-out process, the luminance is obtained from the RGB image data at the same time for the selected pixel, and the distribution is obtained. Eventually, the width of this distribution is obtained in step S116, but there are matters to consider before that.
[0096]
The first is a case where the image is a binary image such as a black and white image. The concept of contrast enhancement is inappropriate for binary images including black and white images. If there is a black and white image as shown in FIG. 11, the luminance distribution for this image is concentrated at both ends in the reproducible range as shown in FIG. Basically, it concentrates on the gradation “0” and the gradation “255”.
[0097]
Therefore, when performing the black and white check in step S104, it can be determined whether or not the sum of the number of pixels of gradation “0” and gradation “255” matches the number of pixels selected by thinning. In the case of a monochrome image, the non-enlargement process is executed in step S106 in order to interrupt the process without executing the following process. In the present embodiment, the distribution extraction process and the luminance conversion process are roughly divided. Therefore, in this non-enlarging process, a flag is set so that the subsequent luminance conversion process is not executed, and the distribution extraction process is terminated.
[0098]
The binary data is not limited to black and white, and there may be binary data with color. In such a case as well, there is no need to perform the process of enhancing the contrast, and if the distribution state is examined and the distribution is concentrated only on two values (one is approximately “0”), the process is interrupted as binary data. Can be achieved.
[0099]
Second, consider whether the image is like a business graph or a natural picture like a photograph. Although a process of enhancing contrast is sometimes required for natural images, it is often preferred that contrast is not enhanced for business graphs or paintings. Accordingly, in step S108, it is checked whether or not the image is a natural image.
[0100]
Natural images have a very large number of colors including shadows, but business graphs and certain types of paintings often have a limited number of colors. Therefore, if the number of colors is small, it can be determined that the image is not a natural image. In order to accurately determine the number of colors, it is necessary to determine how many of 16.7 million colors are used as described above, but this is not realistic. On the other hand, when the number of colors is extremely small as in the business graph, the probability that the colors are different and have the same luminance is low. That is, the approximate number of colors can be determined based on the luminance. If the number of colors is small, the luminance distribution is sparse, and in a business graph, it appears as a line spectrum. For this reason, in step S108, the number of luminance values whose distribution number is not “0” among the luminances of 256 gradations is counted. If it is less than “25”, which is approximately 10%, it is determined that the image is not a natural image, and the non-enlargement process is executed in step S106 as in the case of binary data. Of course, it is possible to appropriately change whether or not the color is “25” or less as the threshold value.
[0101]
It is also possible to determine whether or not the distribution is a line spectrum by the adjacent ratio of luminance values whose distribution number is not “0”. That is, it is determined whether or not there is a distribution number in the luminance values adjacent to the luminance value that is not “0”. If at least one of the two adjacent luminance values is adjacent, nothing is performed, and if both are not adjacent, the count is performed. As a result, the ratio between the number of luminance values other than “0” and the count value is used. Just judge. For example, if the number of luminance values other than “0” is “20” and the number of non-adjacent ones is “20”, it can be seen that the distribution is a line spectrum.
[0102]
Further, when the image processing program is executed via the operating system, it is possible to make a determination based on the extension of the image file. Of bitmap files, especially photographic images are compressed, and an implicit extension is often used to indicate the compression method. For example, an extension “JPG” indicates that the file is compressed in the JPEG format. Since the operating system manages the file name, if you send an inquiry to the operating system from the printer driver, the extension of the file will be answered. What is necessary is just to judge that there exists and to perform contrast emphasis. In addition, if the extension is unique to a business graph such as “XLS”, it can be determined that contrast enhancement is not performed.
[0103]
A third consideration is whether or not there is a frame around the image as shown in FIG. If such a frame is white or black, as shown in FIG. 14, the luminance distribution appears as a line spectrum at both ends in a range where the influence can be reproduced, and both ends corresponding to the internal natural image. It also appears as a smooth luminance distribution inside
[0104]
Of course, since it is appropriate not to take the frame portion into consideration of the luminance distribution, the sum of the number of pixels of the gradation “0” and the gradation “255” is sufficiently large in the frame check in step S108, and It is determined whether the number of pixels selected by the thinning does not match. If the result is affirmative, it is determined that there is a frame portion, and frame processing is performed in step S112. In this frame processing, the number of pixels of gradation “0” and gradation “255” in the luminance distribution is set to “0” in order to ignore the frame portion. Thereby, in the following process, it can handle like the thing without a frame part.
[0105]
In this example, a white or black frame portion is targeted, but there may be a case where there is a specific color frame. In such a case, a protruding line spectrum appears in the original smooth curve drawn by the luminance distribution. Accordingly, a line spectrum having a large difference between adjacent luminance values may be considered as a frame portion and not subjected to luminance distribution. In this case, since the color may be used other than the frame portion, an average of luminance values on both sides may be assigned.
[0106]
In the case where the luminance distribution is expanded after taking the above-described considerations, both ends of the luminance distribution are obtained in step S116. As shown in FIG. 15, the luminance distribution in the natural image appears approximately in a mountain shape. Of course, there are various positions and shapes. The width of the luminance distribution is determined by where the both ends are determined. However, the point where the base is simply extended and the number of distributions becomes “0” cannot be the both ends. This is because the number of distributions may change around “0” in the base portion, and if it is statistically viewed, it changes while approaching “0” as much as possible.
[0107]
For this reason, in the distribution range, portions that have passed a certain distribution ratio from the side with the highest luminance and the side with the lowest luminance are defined as both ends of the distribution. In the present embodiment, as shown in FIG. 15, this distribution ratio is set to 0.5%. Of course, this ratio can be appropriately changed. In this way, by cutting the upper end and the lower end by a certain distribution ratio, it is possible to ignore white spots and black spots caused by noise or the like. That is, if such a process is not performed, if there is even a single white point or black point, it will be at both ends of the luminance distribution. In many cases, the bottom end is gradation “0”, and the top end is Although the gradation becomes “255”, such a problem is eliminated by setting the portion that is inward by the number of pixels of 0.5% from the upper end portion as the end portion.
[0108]
In actual processing, 0.5% of the number of pixels to be processed (the total number of pixels selected in the thinning process or the total number of pixels corresponding to the frame portion is deleted) is calculated, and the upper end of the reproducible luminance distribution is calculated. The number of distributions is accumulated in order starting from the luminance value and the luminance value at the lower end, and the luminance value at 0.5% is obtained. Hereinafter, this upper end side is called ymax, and the lower end side is called ymin.
[0109]
In the present embodiment, the upper end and the lower end are obtained through such processing on the luminance distribution, but it is also possible to obtain both ends under statistical processing. For example, it is also possible to adopt a method in which the portion where the percentage of the average value of the luminance values is less than or equal to the edge is used.
[0110]
The above processing corresponds to the distribution detection processing, and next, luminance conversion processing for converting image data based on the luminance values ymax and ymin thus determined will be described. As described above, when the non-enlargement process is executed in step S106, the predetermined process is detected with reference to a predetermined flag in step S202, and the image process is terminated without performing the following process.
[0111]
In the basic luminance conversion, when the reproducible luminance range is “0” to “255”, the luminance y of the conversion destination is calculated from the luminance y before conversion and the maximum value ymax and minimum value ymin of the luminance distribution range. Y is obtained based on the following equation.
[0112]
Y = ay + b (2)
However,
a = 255 / (ymax−ymin) (3) ′
b = −a · ymin (4) ′
In the above conversion equation, if Y <0, Y = 0, and if Y> 255, Y = 255. Here, a is an inclination and b can be said to be an offset. According to this conversion equation, as shown in FIG. 16, the luminance distribution having a certain narrow width can be expanded to a reproducible range. Basically, in expanding the luminance distribution range, since the number of pixels does not change, the areas of the histograms match.
[0113]
By the way, when the luminance distribution is expanded by making full use of the reproducible range as described above, the highlight portion may be whitened or the high shadow portion may be blackened. In order to prevent this, the reproducible range is limited in this embodiment. That is, the luminance value “5” is left as a range that does not expand to the upper end and the lower end of the reproducible range. As a result, the parameters of the conversion equation are as follows:
[0114]
a = 245 / (ymax−ymin) (8)
b = 5-a · ymin (9)
In this case, conversion is not performed in the range of y <ymin and y> ymax.
[0115]
In the present embodiment, in order to retain the highlight portion and the high shadow portion, the brightness value is uniformly set from the end portion and the range of “5” is set as the non-enlarged region. If the image output apparatus is relatively easy to reproduce, the range may be narrowed. If the reproducibility is even weaker, the range may be made larger. Further, the enlargement rate may be limited gradually in the border area instead of being uniformly enlarged.
[0116]
Further, FIG. 17A shows a case where the luminance distribution of the image is narrow, but if the enlargement ratio of the luminance distribution (corresponding to a) is applied as described above, the reproducible range is shown. In some cases, a very large enlargement ratio can be obtained. Then, although it is natural that the contrast range from the brightest part to the dark part is narrow in the twilight state like the evening, as a result of trying to enlarge the contrast greatly for this image, it is converted like a daytime image. It can be. Since such conversion is not desired, a restriction is set on the enlargement ratio so that a is not 1.5 (˜2) or more. As a result, twilight is expressed as twilight.
[0117]
A case where no limitation is imposed on the enlargement ratio is shown by a one-dot chain line in FIG. 17A, and no extra portion remains in a reproducible range after conversion. However, when the expansion range is limited, as shown by the two-dot chain line in FIG. 5B, there is a degree of freedom where the distribution after the conversion is brought. It can be too dark or too dark.
[0118]
For this reason, in this embodiment, the ratio (m1: m2) of the remaining areas remaining on the upper end side and the lower end side within the range in which the luminance distribution before the conversion can be reproduced is the upper end side and the lower end side after the conversion. Are converted so as to coincide with the ratio of the remaining area (n1: n2). Hereinafter, how to obtain the parameter b in this case will be described.
[0119]
In the luminance distribution of the image before conversion,
m1 = ymin
m2 = 255−ymax
here,
m1 + m2 = 255− (ymax−ymin)
Therefore, if ydif = ymax−ymin,
m1 + m2 = 255−ydif
In the luminance distribution of the converted image,
n1 = Ymin
n2 = 255-Ymax
Similarly,

It becomes.
[0120]
Since m1: m2 = n1: n2,
n1 = m1 (n1 + n2) / (m1 + m2)
= Ymin (255−a · ydif) / (255−ydif)
On the other hand, since Y = ay + b, b = Y−ay.
b = Ymin−a · ymin
= Ymin {(255−a · ydif) / (255−ydif) −a} (10)
It becomes. Moreover, when it calculates | requires using Ymax,
b = Ymax−a · ymax
= 255− (255−ymax) (255−a · ydif) / (255−ydif) −a · ymax (11)
The parameter b can be obtained as described above, and step S204 is ended.
[0121]
By the way, it is unreasonable to execute the conversion equation (Y = ay + b) every time the luminance is converted. This is because the range that the luminance y can take is only “0” to “255”, and it is possible to obtain the luminance Y after conversion corresponding to all the values that the luminance y can take in advance. It is. Therefore, this correspondence is obtained in step S206 and stored as a table as shown in FIG.
[0122]
Image data can be changed when such a conversion table is formed. However, it is extremely effective to not only enhance the contrast by expanding the luminance range but also adjust the brightness. For example, when the peak of the luminance distribution is closer to the dark side as shown by the solid line in FIG. 19, it is better to move the mountain to the brighter side as shown by the chain line. In the case where the peaks of the luminance distribution are close to the bright side as shown by the solid line in FIG. 20, it is preferable to move the peaks to the dark side as shown by the chain line.
[0123]
As a result of various experiments, in the present embodiment, the median ymed in the luminance distribution is obtained, and when the median ymed is less than “85”, the image is determined to be a dark image and γ correction corresponding to the following γ values is performed. Brighten.
[0124]
γ = ymed / 85 (12)
Or
γ = (ymed / 85) ** (1/2) (13)
And
[0125]
In this case, even if γ <0.7, γ = 0.7. This is because if such a limit is not set, the night image becomes like the daytime. Note that if the image is too bright, the overall image becomes whitish and the contrast tends to be low, so that processing such as enhancement of saturation is suitable.
[0126]
On the other hand, when the median ymed is larger than “128”, the image is judged to be a bright image and darkened by γ correction corresponding to the following γ values.
[0127]
γ = ymed / 128 (14)
Or
γ = (ymed / 128) ** (1/2) (15)
And In this case, even if γ> 1.3, a limit is set so that γ = 1.3 so as not to be too dark. Note that if the image is too dark, the color will be excessive and the image will become darker, so processing such as reducing the saturation enhancement is suitable. However, this darkening process may adversely affect a subject in a bright background. This is because, for example, a landscape image in which the sky is half of the image or a commemorative photo on a sunny day, the face is often darkened by backlighting. In the case of these images, a dark part and a bright part are mixed, and thus the standard deviation ystd of the brightness is often a relatively high value. Therefore, when the standard deviation of brightness ystd> 70, the gamma correction for darkening is not performed.
[0128]
This γ correction may be performed on the luminance distribution before conversion or on the luminance distribution after conversion. FIG. 21 shows the correspondence when γ correction is performed. When γ <1, the curve swells upward, and when γ> 1, the curve swells downward. Of course, the result of such γ correction may be reflected in the table shown in FIG. 18, and the same correction is performed on the table data in step S208.
[0129]
Finally, image data is converted in step S210. Up to this point, a correspondence relationship for luminance conversion has been obtained, and for example, it was not a conversion relationship for component values (Rp, Gp, Bp) on the RGB coordinate axes. However, the conversion formula (2) can also be applied in correspondence with the RGB component values (Rp, Gp, Bp). That is, the component values (R, G, B) after conversion with respect to the component values (r, g, b) before conversion are
R = a · r + b (16)
G = a · g + b (17)
B = a · b + b (18)
Can also be sought. This is clear from the fact that both the equations (2) and (4) show a linear correspondence. In addition, the RGB component values (r, g, b), (R, G, B) are also in the same range corresponding to the luminance y, Y ranging from “0” to “255”. Therefore, it can be said that the above-described conversion table of luminance y and Y may be used as it is.
[0130]
Therefore, in step S210, the image data (R, G, B) after conversion is obtained by referring to the conversion table corresponding to the equations (16) to (18) for the image data (r, g, b) of all pixels. The process will be repeated.
[0131]
Next, the operation of the present embodiment having the above configuration will be described step by step.
[0132]
If a photograph is taken with the scanner 11 or the like, image data representing the photograph as RGB gradation data is taken into the computer 21, and the CPU executes the image processing program shown in FIGS. 5 and 6 to execute the image data. The process of enhancing the contrast is executed.
[0133]
First, in step S102, the image data is thinned out within a predetermined error, and the luminance y for the selected pixel is obtained to obtain a distribution. The distribution as it is cannot be used. First, it is determined in step S104 whether the image is a binary image such as black and white, and in step S108, it is determined whether the image is a natural image. Except when the image is a binary image or not a natural image, in step S110, it is determined whether the image data has a frame portion. If there is a frame portion, 0.5% of the upper and lower ends of the luminance distribution obtained by removing the frame portion is determined. To obtain the two ends ymax and ymin of the distribution.
[0134]
If both ends ymax and ymin of the luminance distribution are obtained,
Y = ay + b (2)
a = 245 / (ymax−ymin) (8)
For b, one of the following
b = 5-a · ymin (9)
b = ymin {(255−a · ydif) / (255−ydif) −a} (10)
b = 255− (255−ymax) (255−a · ydif) / (255−ydif) −a · ymax (11)
In step S204, parameters a and b are obtained from the following relational expression. In step S206, the conversion relationship from luminance y to luminance Y is stored in a table. In step S208, γ correction is performed as necessary, and in step S210, image data for all pixels is converted with reference to the completed conversion table.
[0135]
Of course, as described above, such image processing is not performed when the image is not a binary image or a natural image. However, when the image processing of the present invention is performed, the contrast is very low in the state of the photograph. Regardless, by correcting so as to widen the luminance range, a clear image with clear contrast can be obtained.
[0136]
In the above-described embodiment, the restriction on the enlargement ratio is constant, but the user may be able to select on the computer 21 via a predetermined GUI. It is also possible for the user to designate a part of the image data and execute the contrast enhancement process only within the range.
[0137]
In this way, after obtaining the luminance y for the pixels of the image data while thinning out in step S102, the edge that is inside by a predetermined distribution ratio at the upper end and the lower end is regarded as the end of the luminance distribution. (Step S116) Since the parameter a corresponding to the enlargement ratio within the reproducible range of luminance and the parameter b corresponding to the offset amount can be obtained, conversion is performed on the luminance y of the conversion source. The prior luminance Y can be automatically converted using a relational expression such as Y = ay + b.
[0138]
Note that the calculation speed of the input image of the video camera 14 may not be in time. Therefore, in such a case, the luminance is detected for each shooting scene to detect the luminance distribution, and the luminance enlargement table is created assuming that the scene has the same tendency. The luminance distribution may be enlarged corresponding to the enlargement table for each frame. Of course, if there is a sufficient calculation speed, conversion may be performed for each frame. Of course, the same conversion can be performed on the receiver side.
[Brief description of the drawings]
FIG. 1 is a block diagram of an image processing system to which an image processing apparatus according to an embodiment of the present invention is applied.
FIG. 2 is a block diagram illustrating a specific hardware configuration example of the image processing apparatus.
FIG. 3 is a schematic block diagram illustrating another application example of the image processing apparatus of the present invention.
FIG. 4 is a schematic block diagram illustrating another application example of the image processing apparatus of the present invention.
FIG. 5 is a flowchart showing a luminance distribution extraction processing portion in the image processing apparatus of the present invention.
FIG. 6 is a flowchart showing a luminance conversion processing portion in the image processing apparatus of the present invention.
FIG. 7 is a diagram illustrating coordinates in a conversion source image.
FIG. 8 is a diagram showing a sampling period.
FIG. 9 is a diagram illustrating the number of sampling pixels.
FIG. 10 is a diagram illustrating a relationship between a conversion source image and pixels to be sampled.
FIG. 11 is a diagram illustrating a monochrome image.
FIG. 12 is a diagram illustrating a luminance distribution of a black and white image.
FIG. 13 is a diagram illustrating an image having a frame portion.
FIG. 14 is a diagram illustrating a luminance distribution of an image having a frame portion.
FIG. 15 is a diagram illustrating edge processing of luminance distribution and an edge obtained by the edge processing.
FIG. 16 is a diagram showing a range of luminance with a reproducible range and expansion of the luminance distribution.
FIG. 17 is a diagram illustrating a case where a restriction is imposed on the enlargement ratio of the luminance distribution.
FIG. 18 is a diagram illustrating a conversion table when a luminance distribution is enlarged.
FIG. 19 is a diagram illustrating a concept of brightening by γ correction.
FIG. 20 is a diagram illustrating a concept of darkening by γ correction.
FIG. 21 is a diagram illustrating a correspondence relationship of luminance changed by γ correction.
[Explanation of symbols]
10. Image input device
11 ... Scanner
11b ... Scanner
12 ... Digital still camera
12a ... Digital still camera
12b ... Digital still camera
13b Modem
20 Image processing apparatus
21 ... Computer
22 ... Hard disk
30. Image output device
31 ... Printer
31a ... Printer
31b ... Printer
32 ... Display
32a ... Display

Claims (11)

An image processing apparatus that performs predetermined image processing on image data of an image composed of a plurality of pixels,
A luminance distribution detecting means for totalizing the luminance distribution based on the luminance at each pixel and detecting a luminance distribution in which the inner portion is regarded as an end portion by a predetermined distribution ratio from the actual end portion in the aggregated luminance distribution; ,
Image data conversion for detecting an enlargement factor for enlarging the detected luminance distribution in an effective luminance range that can be taken by the image data, and converting luminance information of each pixel in the image data using the enlargement factor Means,
The image data converting means sets a limit on the enlargement ratio and includes a remaining area remaining on the upper end side of the luminance distribution and a remaining area remaining on the lower end side within the effective luminance range before the conversion. An image processing apparatus that performs conversion so that the ratio and the ratio of the remaining area remaining on the upper end side of the luminance distribution and the remaining area remaining on the lower end side after conversion within the effective range coincide with each other . 2. The image processing apparatus according to claim 1, wherein the image data converting means has a luminance y before conversion, a maximum value ymax and a minimum value ymin of the luminance distribution range when the reproducible luminance range is yrange. An image processing apparatus characterized in that a conversion destination luminance Y is obtained based on the following equation.
Y = ay + b
However, a = yrange / (ymax−ymin)
b = -a · ymin
In the above conversion equation, if Y <0, Y = 0, and if Y> yrange, Y = yrange.   3. The image processing apparatus according to claim 2, wherein a conversion destination luminance Y is calculated for each luminance y within a possible range of the conversion source luminance y, and the conversion source luminance y and the conversion destination luminance Y are calculated. The image processing apparatus is characterized in that the correspondence relationship is stored, and conversion is performed with reference to the correspondence relationship at the time of conversion.   In the image processing device according to any one of claims 1 to 3, when the image data is represented by a plurality of component values corresponding to the luminance, the luminance calculation is obtained by linear addition of the same component values. An image processing apparatus.   5. The image processing apparatus according to claim 1, wherein the range of the luminance distribution to be enlarged is set inward by a predetermined amount from the end of the actual reproducible range. Image processing device.   6. The image processing apparatus according to claim 1, wherein the binary image data is determined based on the luminance distribution, and the luminance distribution is not enlarged if the binary image data. An image processing apparatus.   7. The image processing apparatus according to claim 6, wherein when the luminance distribution is concentrated at both ends within a reproducible range, the image processing apparatus determines that the binary image data is black and white.   8. The image processing apparatus according to claim 1, wherein the luminance distribution is not enlarged when the image data is not a natural image.   9. The image processing apparatus according to claim 8, further comprising a natural image determination unit that determines that the image data is not a natural image when the luminance distribution exists in a spectral form. An image processing method for performing predetermined image processing on image data of an image composed of a plurality of pixels,
The brightness distribution as a whole is counted based on the brightness at each pixel, and the brightness distribution in which the inner portion is regarded as the end portion by a predetermined distribution ratio from the actual end portion in the summed brightness distribution is detected,
An enlargement ratio for enlarging the detected brightness distribution in an effective brightness range that can be taken by the image data is detected within a predetermined limit, and information on the brightness of each pixel in the image data is detected using the enlargement ratio. In the conversion, the ratio between the remaining area remaining on the upper end side of the luminance distribution and the remaining area remaining on the lower end side within the effective luminance range before conversion, and the effective area An image processing method characterized in that the conversion is performed so that the ratio of the remaining area remaining on the upper end side of the luminance distribution after the conversion and the remaining area remaining on the lower end side in the same range matches. A medium in which image data of an image composed of a plurality of pixels is input by a computer and an image processing program for performing predetermined image processing is recorded,
Totaling the luminance distribution as a whole based on the luminance at each pixel, detecting a luminance distribution in which the inner portion is regarded as an end portion by a predetermined distribution ratio from the actual end portion in the tabulated luminance distribution;
An enlargement ratio for enlarging the detected brightness distribution in an effective brightness range that can be taken by the image data is detected within a predetermined limit, and information on the brightness of each pixel in the image data is detected using the enlargement ratio. Converting the ratio of the remaining area remaining on the upper end side and the remaining area remaining on the lower end side of the luminance distribution within the effective luminance range before the conversion, and within the effective range A medium on which an image processing program is recorded, comprising the step of performing conversion so that the ratio of the remaining area remaining on the upper end side of the luminance distribution and the remaining area remaining on the lower end side after the conversion matches .

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