JP4196599B2 - Image evaluation apparatus, image evaluation method, program thereof, and recording medium recording the program - Google Patents

Description

【００６８】
これにより、本実施形態による閾値（Ｌ＊）は、図４（ｄ）に示すようになる。但し、式１に示す閾値（Ｌ＊）は、Ｗｈｉｔｅ（白）からＣｙａｎ（シアン）へ連続変化する階調画像に関する閾値である。即ち、他の色による階調画像に関しては異なる閾値の式となる。
【００６９】
但し、画像評価手段４は、閾値（Ｌ＊）以下又は未満、即ち閾値（Ｌ＊）を減算することで０又はマイナスの値となる階調飛び量（Ｌ＊）に関しては、処理の対象外とする又は減算の値を０に置換することで、評価対象から除外する。これは、上記の条件に当てはまる階調飛び量（Ｌ＊）が人間に知覚されないので、階調評価の対象に含める必要がないためである。
【００７０】
更に、このようにして閾値（Ｌ＊）が減算された階調飛び量（Ｌ＊）を２乗することで階調飛び量（Ｌ＊）の分散を算出し、これに基づいて階調評価を行うように構成してもよい。
【００７１】
また、本実施形態において、図４（ｄ）に示すように、各ポジション毎の階調飛び量（Ｌ＊）が閾値（Ｌ＊）以上であれば人間により知覚可能であるとしているが、その知覚量は明度と明度差とに依存する。従って、画像評価手段４は、人間の視感と合致した階調評価を行うために、各ポジションの階調飛び量（Ｌ＊）をそのポジションの明度情報Ｌ＊で補正する。この補正を行うための式を、以下の式２に示す。尚、この補正により得られた値を第１補正階調飛び量（Ｌ＊）とする。
【数２】

【００７２】
また、式２により得られた第１補正階調飛び量（Ｌ＊）も人間の視感による知覚量と等歩度ではない。このため、画像評価手段４は、以下に例示するような式３により得られた値を補正する。尚、この補正により得られた値を第２補正階調飛び量（Ｌ＊）とする。
【数３】

【００７３】
以上のように補正を行うことで、人間の視感に合致したポジション毎の階調飛びの発生量が算出される。尚、これらの補正は、上述のように算出された階調飛び量（Ｌ＊）に対して行ってもよいし、或いは画像特徴量算出手段３で得られた視感階調に対して行ってもよい。
【００７４】
また、階調評価手段４は、以上のようにして得られた値を以下に例示する式４に従って全てのポジションに関して積算する。尚、ｉ＝０〜Ｎは全てのポジションを意味する。また、積算により得られた値を階調性評価値（階調評価指数ともいう）とし、これに基づいて画像出力装置の階調特性を評価する。
【数４】

【００７５】
尚、階調評価手段４は、上記の式４により得られた階調性評価値に基づいて画像出力装置の階調特性を評価するが、階調飛び量（Ｌ＊）の分散に基づいて処理を行った場合、式４で得られた階調性評価値の平方根を画像出力装置の階調特性を評価するための階調評価指数とする。
【００７６】
また、以上のように構成された画像評価装置の動作を図６に示すフローチャートにまとめる。
【００７７】
図６を参照すると、本実施形態による画像評価装置は、画像入力手段１により入力された入力画像データのＲＧＢ信号（１１）をＸＹＺ表色系（１２）に変換する。その後、Ｙ方向の平均化を算出することで、得られた入力画像データを１次元化（１３）し、１次元化されたＸＹＺ表色系の入力画像データをＬ＊ａ＊ｂ＊表色系（１４）に変換する。尚、この過程を撮像・次元変換処理及び色空間変換処理という。
【００７８】
また、本実施形態による画像評価装置は、Ｌ＊ａ＊ｂ＊表色系に変換した入力画像データを空間周波数変換（フーリエ変換：２１）し、これに人間の視覚系の空間周波数特性を掛け合わせることで視覚補正処理（２２）を行った後、実空間変換（逆フーリエ変換：２３）を施す。尚、この過程を空間周波数領域での視覚補正処理という。
【００７９】
次に、本実施形態による画像評価装置は、空間周波数補正後の入力画像データを所定間隔毎に平均化処理（３１）することで理想階調の階調曲線を求め、この理想階調と視感階調との差分を算出（３２）する。その後、この差分に基づいて閾値（Ｌ＊）を算出する処理（３３）を実行し、この閾値（Ｌ＊）に基づいて分散（階調飛び量（Ｌ＊））を算出（３４）する。尚、この過程を平均化処理及び分散算出処理という。
【００８０】
その後、本実施形態による画像評価装置は、求めた分散を人間の視感に基づいて重み補正（４１）し、補正後の分散値を積分計算する（４２）。これにより、画像出力装置の階調特性を評価する指数が算出される。また、積分計算により得られた値の平方根を算出（４３）し、これを評価する指数としてもよい。
【００８１】
以上のように構成及び動作することで、本実施形態では、画像出力装置から出力された階調画像における階調特性を、人間の知覚に合致して正しく評価することが可能となる。即ち、本実施形態によれば、評価対象となる階調画像から得られた光学情報を利用して階調飛びの評価を定量化することが可能となり、被評価対象そのものの階調飛びを人間の知覚特性に合致した量により評価することが可能となる。また、階調飛びが人間が知覚する許容値以上であった場合の階調飛びの評価の定量化も実現し、被評価画像の許容値以上の階調飛びを人間の知覚特性に合致した量に基づいて評価することが可能となる。これにより、評価対象の階調画像そのものを、従来よりも信頼性高く、より人間の感覚にマッチして評価することが可能となる。
【００８２】
尚、本実施形態による画像評価は、上述のＲＧＢに限定されず、例えばＣＭＹＫの色や色相の階調等、何れの場合でも正しくそれを評価することが可能である。
【００８３】
また、本実施形態による画像評価装置における色彩情報変換手段２と画像特徴量算出手段３と階調評価手段４とは、一般的なパーソナルコンピュータやワークステーション等の情報処理装置を用い、上記各手段を機能させるためのプログラムにより実現されてもよい。図７に、これを実現する情報処理装置の一例を示す。
【００８４】
この情報処理装置は、ＣＰＵ（中央演算処理装置）１０１とＲＡＭ（Ｒａｎｄｏｍ Ａｃｃｅｓｓ Ｍｅｍｏｒｙ）１０２とＲＯＭ（Ｒｅａｄ Ｏｎｌｙ Ｍｅｍｏｒｙ）１０３と内部記録装置１０４と表示装置１０５とユーザインタフェース１０６とＩ／Ｏインタフェース１０７とが内部バス１００を介して通信可能に接続されている。また、内部バス１００にはＩ／Ｏインタフェース１０７及び外部バス１１０を介して外部記録装置１１２及び画像読取装置１１１が通信可能に接続されている。
【００８５】
画像読取装置１１１は、上述の画像入力手段１に対応するもので、ＣＰＵ１０１からの命令に従い所定の階調画像を読み取って入力画像データを生成し、これをＣＰＵ１０１に入力する。
【００８６】
ＣＰＵ１０１は上記のように中央演算処理装置（所謂コンピュータ）で構成されるもので、ＲＯＭ１０３に記憶されたアルゴリズムにしたがって、内部記憶装置１０４又は外部記憶装置１１１に記憶されているプログラムを読み出し、これを実行する。これにより、色彩情報変換手段２，画像特徴量算出手段３，階調評価手段４が実現される。また、この際ＲＡＭ１０２に所定の作業領域が確保される。また、ＲＡＭ１０２は画像読取手段１１から入力された入力画像データをバッファする画像メモリとしても機能する。
【００８７】
内部記憶装置１０４及び外部記憶装置１１２は、例えばハードディスクやＣＤ−ＲＯＭやＤＶＤ―ＲＯＭ等（又はこれらの書込／書換可能なもの）で構成されるものであり、本実施形態による色彩情報変換手段２，画像特徴量算出手段３，階調評価手段４を実現するプログラムを記憶するものである。
【００８８】
表示装置１０５は例えばディスプレイ等で構成されるものである。尚、この表示装置１０５と上述の所定の階調画像が表示される視覚表示装置とが同一のものであってもよい。ユーザインタフェース１０６はキーボードやマウス等を含んで構成されるものである。
【００８９】
以上のように構成することで、本実施形態では、上記で説明した画像評価装置をプログラムで実現することが可能となる。更に、このプログラムをＣＤ−ＲＯＭやＤＶＤ−ＲＯＭやこれらの書込／書換可能なもの等の持ち運びに適した記録媒体に記録することで、この記録媒体を介して広く頒布することが可能となる。
【００９０】
〔第２の実施形態〕
また、第１の実施形態では、人間が知覚可能な光学情報量を階調飛び量（Ｌ＊）の閾値（Ｌ＊）としていたが、この閾値（Ｌ＊）は実際に人間が知覚できるか否かの境界であって、その階調飛び量（Ｌ＊）を製品として許容できるか否かの境界とは異なる。そこで、閾値（Ｌ＊）を、人間の知覚限界でなく、許容限界に基づいて決定する場合の例を第２の実施形態として以下に例示する。
【００９１】
本実施形態の場合、第１の実施形態における式１で示した閾値（Ｌ＊）の式が以下の式５に置き換えられる。尚、この式５は、第１の実施形態と同様に、分母のｓａｍｐｌｉｎｇ＿Ｆは入力画像データの解像度［ｄｐｉ］を示し、分子の４５０は経験則で得られた許容限界に基づいて決定された係数を示す。
【数５】

【００９２】
これにより、本実施形態による閾値（Ｌ＊）は、図４（ｄ）に示すようになる。但し、式１に示す閾値（Ｌ＊）は、Ｗｈｉｔｅ（白）からＣｙａｎ（シアン）へ連続変化する階調画像に関する閾値である。即ち、他の色による階調画像に関しては異なる閾値の式となる。
【００９３】
よって、本実施形態による画像評価装置は、より実用的なレベルで画像出力装置の階調特性の評価を行うことができる。尚、他の構成は、第１の実施形態と同様であるため、ここでは説明を省略する。
【００９４】
〔他の実施形態〕
以上、説明した実施形態は本発明の好適な一実施形態にすぎず、本発明はその趣旨を逸脱しない限り種々変形して実施可能である。
【００９５】
【発明の効果】
以上説明したように、本発明によれば、評価すべき階調画像に対する客観的評価を正しく行うことができ、しかもその評価結果を人間の主観的判断と十分に対応させることが可能となる。
【００９６】
また、光学特性のプロファイルに重畳された不要なノイズを除去し、正確に画像評価を行うことも可能となる。更に、人間の視覚特性により適合した客観的な画像の階調評価を行うことが可能となる。更にまた、前記光学情報又は前記人間の視覚系の空間周波数特性に基づいて周波数変換された光学情報を平滑化することにより得られる理想的な光学情報を基準として前記特徴量を算出することが可能となる。ここで、部分平均又は局所平均又はローパスフィルタ又はマトリクス又は人間の視覚系の空間周波数特性の高周波成分が低い特性のうち何れかを利用することで得られた理想的な光学情報の階調曲線を得ることもできる。
【００９７】
また、理想的な光学情報を基準とする、実測された光学情報又はこれを人間の視覚系の空間周波数特性で変換することで得られた光学情報の分散値が特徴量として算出される。更に、人間が認知できない程度又は許容できる程度の階調飛びを除外し、より有効に階調評価を行うことが可能となる。更にまた、前記階調画像の全体的な階調評価を行うことが可能となる。
【００９８】
また、以上のような効果をプログラムを用いて実現することも可能となる。更に、このプログラムを記録媒体に記録して提供することも可能となる。
【図面の簡単な説明】
【図１】本発明の第１の実施形態による画像評価装置の概略構成を示すブロック図である。
【図２】本発明で使用する階調画像の一例を示す図である。
【図３】図２に示す階調画像が出力されるスクリーンの構造例を示す図である。
【図４】本発明の第１の実施形態における処理により生成されたポジション毎の明度情報のグラフであり、（ａ）は画像入力手段１により得られた明度情報であり、（ｂ）は（ａ）を視覚の空間周波数補正することで得られグラフであり、（ｃ）は（ｂ）を平均化す処理することで得られたグラフであり、（ｄ）は（ｃ）と（ｄ）との差分を示すグラフである。
【図５】本発明の第１の実施形態における平均化処理を説明するためのグラフである。
【図６】本発明の第１の実施形態における処理の流れを示すフローチャートである。
【図７】本発明の第１の実施形態による画像評価装置が実現される情報処理装置の構成例を示すブロック図である。
【符号の説明】
１ 画像入力手段 ２ 色彩情報変換手段
３ 画像特徴量算出手段 ４ 階調評価手段
１１ ＲＧＢ １２ ＲＧＢ→ＸＹＺ
１３ １次元化 １４ ＸＹＺ→Ｌ＊ａ＊ｂ＊
２１ 空間周波数変換 ２２ 視覚補正処理
２３ 実空間変換 ３１ 平均化処理
３２ 差分算出 ３３ 閾値処理
３４ 分散処理 ４１ 重み補正
４２ 積分計算 ４３ 平方根算出[0001]
BACKGROUND OF THE INVENTION
The present invention relates to an image evaluation apparatus, an image evaluation method, a program thereof, and a recording medium on which the program is recorded, and in particular, an image evaluation apparatus, an image evaluation method, and a program for performing objective determination in consideration of human subjective judgment The present invention also relates to a recording medium on which the program is recorded.
[0002]
[Prior art]
Conventionally, generally, in an image forming apparatus such as a printer or a copying machine, tone reproducibility is often evaluated through output and analysis of a tone image. Here, the gradation image refers to a pattern image in which at least one of color information such as brightness, saturation, and hue changes continuously.
[0003]
Originally, when such a gradation image is output to an ideal printer, the gradation of the output result should be reproduced with linear or curvilinear smoothness.
[0004]
However, in an actual printer, there is a case where an image in which the gradation portion is disturbed is output, such as gradation skip or streaky unevenness. This disturbance also occurs when a natural image (an output image in a normal use state) is output, and is a factor that significantly deteriorates the performance of the printer.
[0005]
For these reasons, quantitative evaluation of gradation image disturbance is now an extremely important item for evaluating the drawing performance of an image forming apparatus. The same applies to visual display devices such as CRT (Cathode Ray Tube).
[0006]
Conventionally, for example, human visual comparison evaluation is widely known as a method for analyzing and evaluating a gradation image. This method is called so-called subjective evaluation, and evaluates the quality of a gradation image, the degree of damage, and the like by subjective judgment through human vision. However, such a subjective evaluation has a problem that evaluation results vary from one evaluator to another, and human labor is required.
[0007]
In order to solve such problems, in recent years, it has been proposed to perform so-called objective evaluation that does not depend on human subjective judgment on a gradation image output by an image forming apparatus or the like. As an example, there is an image evaluation method disclosed in, for example, Japanese Patent Laid-Open No. 11-25274. This is hereinafter referred to as prior art 1.
[0008]
Prior art 1 uses at least one optical information of lightness information and density information of an output image and calculates a difference in optical information between adjacent gradation levels, so that it does not depend on human visual evaluation. In addition, the gradation characteristics of the output device are objectively evaluated.
[0009]
However, even when objective evaluation is performed in this way, the evaluation result must be compatible with subjective judgment by humans. For this reason, the conventional technique 1 employs a method for quantifying the magnitude of the gradation characteristics (gradation skipping and gradation collapse) of an image based on a limit value that can be recognized by humans.
[0010]
Similarly, as another example of objective evaluation, there is an image quality evaluation method disclosed in Japanese Patent Laid-Open No. 11-39486. This is hereinafter referred to as Prior Art 2.
[0011]
Prior art 2 uses at least one optical information of density information, lightness information, and chromaticity information to calculate a difference in optical information between adjacent gradation levels, and without depending on human visual evaluation, A method of objectively evaluating the gradation characteristics of the output device is adopted.
[0012]
In addition, Japanese Patent Laid-Open No. 2001-128017 discloses a technique for dividing a gradation image based on an identification threshold obtained according to human visual characteristics and calculating a gradation evaluation value from the image feature amount. It is disclosed. This is hereinafter referred to as Conventional Technology 3.
[0013]
[Problems to be solved by the invention]
However, the objective evaluation according to the above-described conventional techniques 1 to 3 has a problem that the gradation image cannot be correctly evaluated.
[0014]
For example, in the image evaluation method disclosed in the related art 1, evaluation is performed using at least one optical information of lightness information or density information. Generally, a color image has lightness (density) and saturation. , And the three attributes of color such as hue. Therefore, not only the change in lightness (density) but also the change in saturation and hue are important factors in the change in the gradation of the color (including the color tone).
[0015]
For this reason, only one of lightness information and density information is used, and information regarding the saturation and hue constituting the three attributes of color is not taken into account, so that it is not possible to take sufficient measures particularly for color tone changes in color images. . Specifically, for example, if information on saturation and hue is missing for a gradation image in which a change from “blue” to “red” is expressed, this cannot be correctly evaluated.
[0016]
In addition, as described above, objective evaluation of gradation images must be handled with human subjective judgment. Therefore, color images are evaluated by considering not only gradation characteristics but also color characteristics. There is a need to. In other words, the three attributes of lightness, saturation, and hue are more suitable for human visual characteristics. Therefore, in the case of a color image, the correspondence with subjective judgment can be fully obtained only by using these three attributes. It can be said that gradation evaluation becomes possible.
[0017]
On the other hand, the prior art 2 evaluates optical information between adjacent gradation levels in consideration of chromaticity information. However, in the prior art 2, since the evaluation target is a patch, there is a problem that the gradation characteristics of the output device are evaluated and the gradation image that is the image to be evaluated is not evaluated.
[0018]
In the prior art 2, the error due to the in-plane variation is reduced at the arrangement position of the patch. However, in this method, there is a problem of in-plane variation at the time of outputting an actual continuous tone image and stability with time of the output device. Can not be solved. Further, if front-end processing such as rasterization when outputting a gradation image is included, there is a case where the correspondence between the patch and the gradation image may not be achieved. There is also a problem that cannot be handled.
[0019]
Moreover, in the prior art 3, since the comparison is performed with the variance value having a width exceeding the threshold value, there is a problem that this is different from the actual evaluation value. Taking the case of a printer as an example, the printer has a unique gradation curve for performing tone correction on the input color. That is, for example, the human eye is sensitive to changes from highlight to middle, so the printer has a tone curve to make adjustments to reduce color gamut compression between the highlight and middle. .
[0020]
However, if the printer is configured to perform tone correction by itself, there may be a case where a tone skip value (also referred to as a dispersion value) is large even if a continuous tone image looks natural.
[0021]
Furthermore, when the gradation is expressed by the area ratio of the colorant, if the gradation image to be evaluated is measured, the fluctuation may become very large due to the influence of the screen, and the threshold value may be easily exceeded. . In such a case, the width cannot be measured.
[0022]
Here, it is conceivable to increase the reading range at the time of measurement so as not to affect the screen, but in order to realize this, it is necessary to make the reading range very large. There is a problem that it becomes impossible to evaluate the gradation skip.
[0023]
The present invention has been made in view of such a problem, and can objectively evaluate a gradation image to be evaluated correctly, and the evaluation result sufficiently corresponds to human subjective judgment. It is an object to provide an image evaluation apparatus, an image evaluation method, a program thereof, and a recording medium on which the program is recorded.
[0024]
[Means for Solving the Problems]
  To achieve this goal,Claim 1The invention includes an image input means for inputting the output gradation image and outputting image data, and the image dataGenerate brightness information in color informationGenerating means;The lightness information is subjected to Fourier transform, the result is multiplied by the spatial frequency characteristic of the human visual system, and the obtained result is subjected to inverse Fourier transform to obtain a visual gradation, Calculating means for obtaining an ideal gradation by locally averaging the tones and calculating a gradation skip amount from a difference between the visual gradation and the ideal gradation;,A threshold value indicating a brightness limit value perceivable by a human of the following formula (1) is calculated from the gradation skip amount,
Threshold = 300 or 450 / resolution of the image data * the third root of the brightness information of each dot of the image data (1)
Using the following formulas (2) and (3), the gradation jump amount of each dot that matches human vision is calculated,
First correction gradation skip amount = (the gradation skip amount−the threshold value) * the third root of the brightness information of each dot of the image data (2)
Second correction gradation jump amount = 0.9th root of first correction gradation jump amount (3)
A gradation evaluation index of the gradation image is calculated by integrating the second correction gradation jump amount obtained by the expression (3) with respect to all dots.Gradation evaluation means. ThisRealizes quantification of evaluation of gradation skip when the gradation skip is greater than the permissible value perceived by humans, and is based on the amount of gradation jump that exceeds the permissible value of the evaluated image according to human perceptual characteristics Can be evaluated. As a result, the gradation image itself to be evaluated can be evaluated with higher reliability and matching with the human sense.
[0025]
  According to a second aspect of the present invention, there is provided an image input step of inputting the output gradation image and outputting image data, a generation step of generating lightness information in color information from the image data, and a Fourier for the lightness information. The result is multiplied by the spatial frequency characteristic of the human visual system, and the obtained result is subjected to inverse Fourier transform to obtain the luminous gradation, and the luminous gradation is locally averaged. A calculation step of acquiring an ideal gradation and calculating a gradation skip amount from a difference between the visual gradation and the ideal gradation;,A threshold value indicating a brightness limit value perceivable by a human of the following formula (1) is calculated from the gradation skip amount,
Threshold = 300 or 450 / resolution of the image data * the third root of the brightness information of each dot of the image data (1)
Using the following formulas (2) and (3), the gradation jump amount of each dot that matches human vision is calculated,
First correction gradation skip amount = (the gradation skip amount−the threshold value) * the third root of the brightness information of each dot of the image data (2)
Second correction gradation jump amount = 0.9th root of first correction gradation jump amount (3)
A gradation evaluation step of calculating a gradation evaluation index of the gradation image by integrating the second correction gradation jump amount obtained by the expression (3) with respect to all dots.
[0026]
According to a third aspect of the present invention, there is provided an image input means for inputting an output gradation image and outputting image data, a generation means for generating lightness information in color information from the image data,  The lightness information is subjected to Fourier transform, the result is multiplied by the spatial frequency characteristic of the human visual system, and the obtained result is subjected to inverse Fourier transform to obtain a visual gradation, An average gradation is obtained by locally averaging the tones, a calculation means for calculating a gradation skip amount from the difference between the visual gradation and the ideal gradation, and the following equation (1) from the gradation skip amount: Calculating a threshold value that indicates the limit of lightness perceivable by humans,
Threshold = 300 or 450 / resolution of the image data * the third root of the brightness information of each dot of the image data (1)
Using the following formulas (2) and (3), the gradation jump amount of each dot that matches human vision is calculated,
First correction gradation skip amount = (the gradation skip amount−the threshold value) * the third root of the brightness information of each dot of the image data (2)
Second correction gradation jump amount = 0.9th root of first correction gradation jump amount (3)
By integrating the second corrected gradation jump amount obtained by the expression (3) with respect to all the dots, it functions as a gradation evaluation means for calculating a gradation evaluation index of the gradation image..
[0027]
  The invention according to claim 4 is characterized in that the program according to claim 3 is recorded.
[0049]
DETAILED DESCRIPTION OF THE INVENTION
The present invention relates to information input or output in an information society, particularly image information input by an image input device such as a scanner or a digital camera, image information output to an image display device such as a display, a color hard copy party The present invention relates to an image evaluation method for evaluating image information output by an image output device or the like and an image evaluation device for realizing the method. DESCRIPTION OF EMBODIMENTS Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings.
[0050]
[First Embodiment]
Hereinafter, an image evaluation apparatus according to a first embodiment of the present invention will be described in detail with reference to the drawings. Note that the image evaluation apparatus according to the present embodiment is a visual display apparatus that displays an image on a screen such as an image forming apparatus that prints an image on a paper surface such as a printer or a copier, or a CRT (Cathode-Ray Tube) display. The tone image output from the image output device is analyzed, and the tone image is evaluated from the analysis result, thereby evaluating the drawing performance of the image output device.
[0051]
FIG. 1 is a block diagram illustrating a schematic configuration of the image evaluation apparatus according to the present embodiment. Referring to FIG. 1, the image evaluation apparatus includes an image input unit 1, a color information conversion unit 2, an image feature amount calculation unit 3, and a gradation evaluation unit 4.
[0052]
The image input means 1 in FIG. 1 acquires image data of a predetermined gradation image output from the image output apparatus as described above by optically reading a gradation image to be evaluated. The predetermined gradation image is obtained by outputting image data prepared in advance by an image output device. Here, an example of a predetermined gradation image output from the image output apparatus is shown in FIG. For the sake of distinction in the following description, image data to be output to the image output device is assumed to be original image data, and image data acquired by the image input means 1 is assumed to be input image data.
[0053]
Further, the image data acquired by the image input unit 1 includes, for example, R (red), G (green), and B (blue) color component signals. However, the present embodiment is not limited thereto. For example, each color component signal of C (Cyan), M (Magenta), Y (Yellow), and K (black) may be included.
[0054]
For example, a one-dimensional scanning scanner or the like is applied to the image input unit 1. However, in addition to this, even if an image reading device having a spectral sensitivity equivalent to the color matching functions x (λ), y (λ), z (λ) in the XYZ color system is applied, a microdensitometer, A general image reading apparatus such as a color scanner, a monochrome scanner, a drum scanner, a densitometer, or a colorimeter may be applied.
[0055]
Furthermore, a general-purpose image reading device whose output is an RGB signal may be applied, or a so-called digital camera configured with a CCD (Charge Couple Device) may be applied. That is, the configuration for reading an image in the image input means 1 is not limited to the two-dimensional scanning colorimeter.
[0056]
Furthermore, when a predetermined gradation image to be evaluated is output on the screen by the visual display device, an image reading device such as a luminance meter is applied to the configuration for reading the image in the image input means 1.
[0057]
As described above, the image input unit 1 can be modified in any way as long as it can generate input image data by reading a predetermined gradation image output from the image output device. In other words, as long as the image data of the gradation image can be acquired via the predetermined image output device, various modifications can be made. In the following description, an example is given of the case where the tone characteristics of the tone image are obtained using the brightness component of the acquired input image. However, the present invention is not limited to this, and density components and other optical information (input image data) It is also possible to obtain gradation characteristics using a profile, a chromaticity component, a luminance component, etc.).
[0058]
The input image data acquired by the image input unit 1 is stored in a predetermined image memory in such a manner that the two-dimensional position information of each dot is held, and then input to the color information conversion unit 2.
[0059]
The color information conversion unit 2 functions as an optical information generation unit that generates optical information for evaluating the gradation image from the input image data input as described above. In this embodiment, color information is applied as optical information, and color information that is optical information is generated by converting input image data input by the color information conversion unit 2 into color information. Note that the color information is lightness information (also referred to as lightness index: L *), color information (also referred to as color index: a *), and hue information (also referred to as hue index: b *) constituting the three attributes of color. It is comprised including. In addition, the color information conversion unit 2 according to the present embodiment obtains a lightness component by converting a measured physical quantity into a component of an L * a * b * color system, for example, but in addition to this, L * u * v * You may comprise so that a brightness component may be calculated | required by converting into a color system etc. Furthermore, as described above, other optical information such as a density component, a chromaticity component, a luminance component, and a profile of input image data can be used instead of the lightness component.
[0060]
Further, the color information conversion means 2 generates one-dimensional lightness information by averaging the lightness information in the obtained color information in the Y direction in FIG.
[0061]
That is, if the predetermined gradation image shown in FIG. 2 is input by the image input means 1 as an RGB signal having a resolution of 800 dpi and a depth of 16 bits, for example, the color information conversion means 2 outputs the brightness for each dot (for each position). Convert to information. Thereafter, the color information conversion means 2 generates a one-dimensional array of lightness information only in the X direction by averaging the obtained two-dimensional array of lightness information in the Y direction. Note that the conversion to lightness information is performed by calculation using, for example, a multiple regression equation or a matrix.
[0062]
However, for example, in an image having a screen structure as shown in FIG. 3, if the quantization level of the image is too fine, the brightness difference between the portion where the colorant is not present and the portion where the colorant is present becomes larger than necessary. Exists. Therefore, when the gradation image having such a screen structure is L * converted, as shown in FIG. 4A, the profile of the input image data (the profile indicates the condition data describing the color calibration condition). There is a case where large noise is superimposed. This is because sampling (= reading) of each dot forming an image becomes discrete. In FIG. 4, the position on the horizontal axis (Position) indicates the arrangement position of dots in the X direction.
[0063]
In order to avoid this, in the present embodiment, the image feature quantity calculation means 3 performs Fourier transform on the lightness information of the one-dimensional array subjected to the discrete sampling. Thereafter, the image feature quantity calculation means 3 multiplies the information obtained by the Fourier transform by the spatial frequency characteristic of the human visual system, and further performs the inverse Fourier transform on the obtained information. As a result, the gradation of the brightness information of the one-dimensional array subjected to the discrete sampling described above is converted into a gradation observed through a human visual system as shown in FIG. Can be converted to
[0064]
Next, the image feature quantity calculation means 3 smoothes the visual gradation obtained by correcting with the spatial frequency characteristics of the human visual system by a predetermined method. As a smoothing method, for example, a method of calculating a partial average or a local average, a method through a low-pass filter, a method of applying a predetermined matrix, or a characteristic in which a high frequency component of the spatial frequency characteristic of the human visual system is low. The method to utilize etc. are mentioned. In the present embodiment, a case where smoothing is performed by local averaging will be described as an example.
[0065]
By this local average, a smooth gradation curve as shown in FIG. 4C is obtained. In the present embodiment, the gradation obtained by the local average as shown in FIG. 4C is assumed to be the original gradation (referred to as an ideal gradation). As shown in FIG. 5, the local average is calculated by, for example, dividing the X direction into 1 mm ranges and calculating the average of the brightness information within each range.
[0066]
Next, the image feature amount calculation means 3 calculates the difference between the visual gradation (see FIG. 4B) and the ideal gradation (see FIG. 4C), that is, the amount of gradation skip (hereinafter referred to as gradation). A jump amount (referred to as L *) is calculated for each position in the X direction. Thereby, the feature amount (= tone skip amount (L *)) of the gradation image is calculated. An example of the value of the gradation skip amount (L *) for each position is shown in FIG.
[0067]
In addition, by subtracting a brightness difference limit value (hereinafter referred to as a threshold value) that can be perceived by humans from the gradation jump amount (L *) obtained in this way, the image evaluation means 4 is capable of recognizing a floor that humans can recognize. It is possible to obtain a gradation evaluation result that considers the perceptual limit of jumping, that is, more suited to human subjective judgment. Formula 1 shows an example of a threshold value of lightness information L * (hereinafter referred to as a threshold value (L *)). In Equation 1, sampling_F in the denominator indicates the resolution [dpi] of the input image data, and 300 in the numerator indicates a coefficient determined based on the perceptual limit obtained by empirical rules.
[Expression 1]

[0068]
Thereby, the threshold value (L *) according to the present embodiment is as shown in FIG. However, the threshold value (L *) shown in Equation 1 is a threshold value for a gradation image that continuously changes from White (white) to Cyan (cyan). That is, different threshold expressions are used for gradation images of other colors.
[0069]
However, the image evaluation unit 4 does not process the gradation jump amount (L *) which is equal to or less than the threshold value (L *), that is, 0 or a negative value by subtracting the threshold value (L *). Or by subtracting 0 from the value of subtraction. This is because the gradation skip amount (L *) that satisfies the above condition is not perceived by humans and therefore does not need to be included in the gradation evaluation target.
[0070]
Further, the variance of the gradation skip amount (L *) is calculated by squaring the gradation skip amount (L *) from which the threshold value (L *) has been subtracted in this way, and the gradation evaluation is performed based on this. You may comprise so that it may perform.
[0071]
Further, in the present embodiment, as shown in FIG. 4D, if the gradation skip amount (L *) for each position is equal to or greater than the threshold value (L *), it is perceivable by humans. The amount of perception depends on the brightness and the brightness difference. Accordingly, the image evaluation unit 4 corrects the gradation skip amount (L *) of each position with the lightness information L * of each position in order to perform gradation evaluation that matches human visual perception. An equation for performing this correction is shown in Equation 2 below. Note that the value obtained by this correction is the first correction gradation skip amount (L *).
[Expression 2]

[0072]
In addition, the first correction gradation jump amount (L *) obtained by Expression 2 is not equal to the perceptual amount due to human visual perception. For this reason, the image evaluation means 4 corrects the value obtained by Equation 3 as exemplified below. Note that the value obtained by this correction is the second correction gradation skip amount (L *).
[Equation 3]

[0073]
By performing the correction as described above, the generation amount of gradation skip for each position that matches the human visual sense is calculated. These corrections may be performed on the gradation skip amount (L *) calculated as described above, or may be performed on the visual gradation obtained by the image feature amount calculation unit 3. May be.
[0074]
Further, the gradation evaluation unit 4 integrates the values obtained as described above with respect to all the positions in accordance with Expression 4 exemplified below. Note that i = 0 to N means all positions. In addition, a value obtained by integration is used as a gradation evaluation value (also referred to as a gradation evaluation index), and the gradation characteristics of the image output apparatus are evaluated based on the evaluation value.
[Expression 4]

[0075]
The tone evaluation means 4 evaluates the tone characteristics of the image output device based on the tone evaluation value obtained by the above equation 4, but based on the variance of the tone skip amount (L *). When processing is performed, the square root of the tone evaluation value obtained by Expression 4 is used as a tone evaluation index for evaluating the tone characteristics of the image output apparatus.
[0076]
The operation of the image evaluation apparatus configured as described above is summarized in the flowchart shown in FIG.
[0077]
Referring to FIG. 6, the image evaluation apparatus according to the present embodiment converts the RGB signal (11) of the input image data input by the image input means 1 into the XYZ color system (12). After that, by averaging the Y direction, the obtained input image data is made one-dimensional (13), and the one-dimensional input image data of the XYZ color system is converted into the L * a * b * color specification. Convert to system (14). This process is called imaging / dimension conversion processing and color space conversion processing.
[0078]
In addition, the image evaluation apparatus according to the present embodiment performs spatial frequency conversion (Fourier transform: 21) on input image data converted into the L * a * b * color system, and multiplies the input image data by the spatial frequency characteristics of the human visual system. After performing visual correction processing (22) by combining them, real space transformation (inverse Fourier transformation: 23) is performed. This process is called visual correction processing in the spatial frequency domain.
[0079]
Next, the image evaluation apparatus according to the present embodiment obtains a gradation curve of an ideal gradation by averaging (31) the input image data after the spatial frequency correction at a predetermined interval, and obtains the ideal gradation and the view. The difference from the sensitive gradation is calculated (32). Thereafter, a process (33) for calculating a threshold value (L *) based on the difference is executed, and a variance (gradation skip amount (L *)) is calculated (34) based on the threshold value (L *). This process is called an averaging process and a variance calculation process.
[0080]
Thereafter, the image evaluation apparatus according to the present embodiment performs weight correction (41) on the obtained variance based on human visual perception, and integrates and calculates the corrected variance value (42). As a result, an index for evaluating the gradation characteristics of the image output apparatus is calculated. Moreover, it is good also as an index | exponent which calculates the square root of the value obtained by integral calculation (43), and evaluates this.
[0081]
With the configuration and operation as described above, in the present embodiment, it is possible to correctly evaluate the gradation characteristics in the gradation image output from the image output apparatus in accordance with human perception. That is, according to this embodiment, it is possible to quantify the evaluation of gradation skip using optical information obtained from the gradation image to be evaluated, and the gradation skip of the evaluation target itself can be quantified. It is possible to evaluate by an amount that matches the perceptual characteristics of. In addition, it is possible to quantify the evaluation of gradation skip when the gradation skip is greater than or equal to the permissible value perceived by humans. It becomes possible to evaluate based on. As a result, the gradation image itself to be evaluated can be evaluated with higher reliability and matching with the human sense.
[0082]
Note that the image evaluation according to the present embodiment is not limited to the above-described RGB, and for example, it is possible to correctly evaluate the CMYK color or hue gradation in any case.
[0083]
In addition, the color information conversion unit 2, the image feature amount calculation unit 3, and the gradation evaluation unit 4 in the image evaluation apparatus according to the present embodiment use an information processing apparatus such as a general personal computer or a workstation. It may be realized by a program for functioning. FIG. 7 shows an example of an information processing apparatus that realizes this.
[0084]
This information processing apparatus includes a CPU (Central Processing Unit) 101, a RAM (Random Access Memory) 102, a ROM (Read Only Memory) 103, an internal recording device 104, a display device 105, a user interface 106, an I / O interface 107, Are communicably connected via the internal bus 100. An external recording device 112 and an image reading device 111 are communicably connected to the internal bus 100 via an I / O interface 107 and an external bus 110.
[0085]
The image reading device 111 corresponds to the above-described image input unit 1, reads a predetermined gradation image according to a command from the CPU 101, generates input image data, and inputs this to the CPU 101.
[0086]
The CPU 101 is composed of a central processing unit (so-called computer) as described above, and reads out a program stored in the internal storage device 104 or the external storage device 111 in accordance with an algorithm stored in the ROM 103, Execute. Thereby, the color information conversion means 2, the image feature amount calculation means 3, and the gradation evaluation means 4 are realized. At this time, a predetermined work area is secured in the RAM 102. The RAM 102 also functions as an image memory that buffers input image data input from the image reading unit 11.
[0087]
The internal storage device 104 and the external storage device 112 are composed of, for example, a hard disk, a CD-ROM, a DVD-ROM, or the like (or a rewritable / rewritable device thereof), and color information conversion means according to this embodiment. 2, a program for realizing the image feature quantity calculation means 3 and the gradation evaluation means 4 is stored.
[0088]
The display device 105 is composed of, for example, a display. Note that the display device 105 and the visual display device on which the above-described predetermined gradation image is displayed may be the same. The user interface 106 includes a keyboard and a mouse.
[0089]
With the configuration as described above, in the present embodiment, the image evaluation apparatus described above can be realized by a program. Further, by recording this program on a recording medium suitable for carrying such as a CD-ROM, DVD-ROM, or a writable / rewritable one, it becomes possible to distribute the program widely via this recording medium. .
[0090]
[Second Embodiment]
In the first embodiment, the amount of optical information that can be perceived by humans is set as the threshold (L *) of the gradation skip amount (L *). Is this threshold (L *) actually perceivable by humans? This is different from the boundary of whether or not the gradation skip amount (L *) is acceptable as a product. Therefore, an example in which the threshold value (L *) is determined based on an allowable limit instead of a human perceptual limit will be exemplified below as a second embodiment.
[0091]
In the case of the present embodiment, the threshold (L *) equation shown in Equation 1 in the first embodiment is replaced by the following Equation 5. As in the first embodiment, Equation 5 indicates that the denominator sampling_F indicates the resolution [dpi] of the input image data, and the numerator 450 is a coefficient determined based on the allowable limit obtained by empirical rules. Indicates.
[Equation 5]

[0092]
Thereby, the threshold value (L *) according to the present embodiment is as shown in FIG. However, the threshold value (L *) shown in Equation 1 is a threshold value for a gradation image that continuously changes from White (white) to Cyan (cyan). That is, different threshold expressions are used for gradation images of other colors.
[0093]
Therefore, the image evaluation apparatus according to the present embodiment can evaluate the gradation characteristics of the image output apparatus at a more practical level. Since other configurations are the same as those of the first embodiment, description thereof is omitted here.
[0094]
[Other Embodiments]
The embodiment described above is merely a preferred embodiment of the present invention, and the present invention can be variously modified and implemented without departing from the gist thereof.
[0095]
【The invention's effect】
As described above, according to the present invention, it is possible to correctly perform an objective evaluation on a gradation image to be evaluated, and to sufficiently correspond the evaluation result to human subjective judgment.
[0096]
Also, unnecessary noise superimposed on the profile of optical characteristics can be removed, and image evaluation can be performed accurately. Furthermore, it is possible to perform gradation evaluation of an objective image that is more suitable for human visual characteristics. Furthermore, it is possible to calculate the feature amount based on ideal optical information obtained by smoothing the optical information or frequency-converted optical information based on the spatial frequency characteristics of the human visual system. It becomes. Here, a gradation curve of ideal optical information obtained by using any one of a partial average, a local average, a low-pass filter, a matrix, or a low frequency high frequency component of the human visual system is used. It can also be obtained.
[0097]
Further, the measured optical information based on the ideal optical information or the dispersion value of the optical information obtained by converting this with the spatial frequency characteristic of the human visual system is calculated as the feature amount. Furthermore, it is possible to perform gradation evaluation more effectively by excluding gradation skipping that cannot be perceived by humans or acceptable. Furthermore, it is possible to perform overall gradation evaluation of the gradation image.
[0098]
Further, the above effects can be realized using a program. Further, the program can be recorded on a recording medium and provided.
[Brief description of the drawings]
FIG. 1 is a block diagram showing a schematic configuration of an image evaluation apparatus according to a first embodiment of the present invention.
FIG. 2 is a diagram showing an example of a gradation image used in the present invention.
3 is a diagram showing an example of the structure of a screen that outputs the gradation image shown in FIG.
FIG. 4 is a graph of brightness information for each position generated by the processing according to the first embodiment of the present invention, (a) is brightness information obtained by the image input means 1, and (b) is ( a) is a graph obtained by correcting visual spatial frequency, (c) is a graph obtained by averaging (b), and (d) is a graph obtained by (c), (d), and It is a graph which shows the difference of.
FIG. 5 is a graph for explaining an averaging process in the first embodiment of the present invention.
FIG. 6 is a flowchart showing the flow of processing in the first embodiment of the present invention.
FIG. 7 is a block diagram illustrating a configuration example of an information processing apparatus in which the image evaluation apparatus according to the first embodiment of the present invention is realized.
[Explanation of symbols]
1 Image input means 2 Color information conversion means
3 Image feature amount calculation means 4 Tone evaluation means
11 RGB 12 RGB → XYZ
13 One-dimensionalization 14 XYZ → L * a * b *
21 Spatial frequency conversion 22 Visual correction processing
23 Real space transformation 31 Averaging
32 Difference calculation 33 Threshold processing
34 Distributed processing 41 Weight correction
42 Integral calculation 43 Square root calculation

Claims (4)

Image input means for inputting the output gradation image and outputting image data;
Generating means for generating brightness information in color information from the image data;
The lightness information is subjected to Fourier transform, the result is multiplied by the spatial frequency characteristic of the human visual system, and the obtained result is subjected to inverse Fourier transform to obtain a visual gradation, A calculation means for acquiring an ideal gradation by locally averaging the tones, and calculating a gradation jump amount from a difference between the visual gradation and the ideal gradation ;
A threshold value indicating a brightness limit value perceivable by a human of the following formula (1) is calculated from the gradation skip amount,
Threshold = 300 or 450 / resolution of the image data * the third root of the brightness information of each dot of the image data (1)
Using the following formulas (2) and (3), the gradation jump amount of each dot that matches human vision is calculated,
First correction gradation skip amount = (the gradation skip amount−the threshold value) * the third root of the brightness information of each dot of the image data (2)
Second correction gradation jump amount = 0.9th root of first correction gradation jump amount (3)
A gradation evaluation means for calculating a gradation evaluation index of the gradation image by integrating the second corrected gradation jump amount obtained by the expression (3) with respect to all dots ;
An image evaluation apparatus comprising: An image input step of inputting the output gradation image and outputting image data;
Generating step for generating brightness information in the color information from the image data;
The lightness information is subjected to Fourier transform, the result is multiplied by the spatial frequency characteristic of the human visual system, and the obtained result is subjected to inverse Fourier transform to obtain a visual gradation, A calculation step of obtaining an ideal gradation by locally averaging the tones and calculating a gradation jump amount from a difference between the visual gradation and the ideal gradation ;
A threshold value indicating a brightness limit value perceivable by a human of the following formula (1) is calculated from the gradation skip amount,
Threshold = 300 or 450 / resolution of the image data * the third root of the brightness information of each dot of the image data (1)
Using the following formulas (2) and (3), the gradation jump amount of each dot that matches human vision is calculated,
First correction gradation skip amount = (the gradation skip amount−the threshold value) * the third root of the brightness information of each dot of the image data (2)
Second correction gradation jump amount = 0.9th root of first correction gradation jump amount (3)
A gradation evaluation step of calculating a gradation evaluation index of the gradation image by integrating the second correction gradation jump amount obtained by the expression (3) with respect to all dots;
An image evaluation method characterized by comprising: Computer
Image input means for inputting the output gradation image and outputting image data;
Generating means for generating brightness information in color information from the image data ;
The lightness information is subjected to Fourier transform, the result is multiplied by the spatial frequency characteristic of the human visual system, and the obtained result is subjected to inverse Fourier transform to obtain a visual gradation, A calculation means for acquiring an ideal gradation by locally averaging the tones, and calculating a gradation jump amount from a difference between the visual gradation and the ideal gradation; and
A threshold value indicating a brightness limit value perceivable by a human of the following formula (1) is calculated from the gradation skip amount,
Threshold = 300 or 450 / resolution of the image data * the third root of the brightness information of each dot of the image data (1)
Using the following formulas (2) and (3), the gradation jump amount of each dot that matches human vision is calculated,
First correction gradation jump amount = - cube root of (the gradation jump amount the threshold) * Lightness information of each dot of the image data (2)
Second correction gradation jump amount = 0.9th root of first correction gradation jump amount (3)
A program that functions as a gradation evaluation unit that calculates a gradation evaluation index of the gradation image by accumulating the second correction gradation jump amount obtained by the expression (3) with respect to all dots. . A recording medium on which the program according to claim 3 is recorded.

Images