JP4497764B2 - Color processing apparatus and method, and storage medium - Google Patents

Description

この結果により、γ値が「２．０」の場合に最も分散値（４．７３１０）が低いという結果が得られた。Ｌａｂ色空間において色差平均に対する分散値が低いということは、色分布が均一に近い状態であると考えられる。理想的なＲＧＢ色空間を考えた場合に、色の偏りが少ない方が精度的にも有利になるので、より好適である。よって本実施の形態では、分散値が最も低くなるγ値「２．０」を各カラー処理装置におけるＲＧＢ色空間に採用する。
【００２８】
図５は、γ＝２．０を採用したＲＧＢ色空間であって、上記同様に各１２５の格子点の各色をＬａｂ均等色空間へプロットした様子を示したものである。図５からも明らかなように、前述の図１に比べて、プロットされた点の均一性が高くなっていることのが分かる。
【００２９】
［実施の形態２］
前述の実施の形態１では、各色のＢ軸方向の色差しか計算しなかったが、Ｒ、Ｇ、Ｂの各軸方向の各々の平均色差の分散を計算し、各軸の分散値の平均によってγ値を決定しても良い。
【００３０】
図６は、本発明の実施の形態２に係る処理を説明するフローチャートで、ここではγ値を「１．０」から「２．２」まで「０．１」単位で変化させながら、Ｒ，Ｇ，Ｂの各軸方向の色差を計算し、色空間全体での色差の平均と、その分散値を求める処理の概要を示している。
【００３１】
まずステップＳ２１で、平均色差を求める変数（メモリ１０２に設けられる）のリセット等、演算用の変数（メモリ１０２に設けられる）の初期化処理を行う。次にステップＳ２２に進み、最初はγ値を初期値「１．０」にセットし、繰り返し処理によってその値を「１．０」から「２．２」まで「０．１」毎に変更するよう構成しておく。そしてステップＳ２３では、ＲＧＢ色空間における格子点データを順次生成し、そのデータをＲＧＢ値からＸＹＺ値に変換し、更にＬａｂ値に変換する。そしてステップＳ２４に進み、各軸方向に並んでいる各格子点の色差を計算する。そしてステップＳ２５に進み、全体での平均値と、分散を求めるための計算処理を実行する。そしてステップＳ２６に進み、全ての格子点のデータ処理が終了したか否かを判定し、終了していない場合はステップＳ２３に戻り、前述の処理を実行する。
【００３２】
ステップＳ２６で、全ての格子点データに対する処理が終了するとステップＳ２７に進み、γ値が終了値（ここでは「２．２」）を越えたかどうかを判定し、越えていない時はステップＳ２９に進み、γ値を＋０．１してステップＳ２２に戻り、新たにγ値を設定して前述の処理を実行する。一方、ステップＳ２７で、γ値が「２．２」を越えた場合はステップＳ２８に進み、各変数に保持されたデータ、平均色差やその分散値等を記録し、全体の処理を終了させる。
【００３３】
以下は、本実施の形態２において、ＲＧＢ色空間においてγ値を変化させた場合の各軸方向の各色の色差の平均と、その分散値を計算した結果である。
【００３４】

この結果により、γ値が「１．９０」の場合に最も分散値（５．７３３４）が最も低いという結果が得られた。Ｌａｂ色空間において各色の色差平均に対する分散値が低いということは、各色の分布が均一に近い状態であると考えられる。理想的なＲＧＢ色空間を考えた場合に色の偏りが少ない方が精度的にも有利になる。
【００３５】
本実施の形態１，２では、色の均一性を考慮したＲＧＢ色空間を定義するために、γ値を変化させながら均等色空間等にＲＧＢの各色をプロットし、該プロットしたデータ間の色差の平均及びその分散を求めることにより、その分散が最も低くなるγ値を求めることを特徴としている。より理想的なγ値を求めるために、均一性を評価する関数が本実施の形態以外のもの、例えば各色軸方向の分散値に重み係数等を適用した場合でも同様な結果を得ることができる。更には、計算で求められたγ値に近い値を予め設定（例えばデフォルト設定等）している場合も、本実施の形態と同じ効果が得られる。
【００３６】
図７は、本実施の形態に係るγ値の決定及びその設定処理を説明するフローチャートである。
【００３７】
まずステップＳ３１で、例えば前述の図４或いは図６のフローチャートで示すように、所定の範囲のγ値に対して、基準ＲＧＢ色空間の色を均等色空間にプロットする。次にステップＳ３２に進み、各γ値に対する分散値を求める。そしてステップＳ３３に進み、これら各γ値に対して求められた分散値の中で最も小さい分散値を求め、その分散値に対応するγ値を求める。次にステップＳ３４に進み、そのγ値を、前述の画像処理装置に接続されている表示部１０３やプリンタ１０８、スキャナ（不図示）等のＲＧＢ色空間におけるγ値として設定する。
【００３８】
以上説明したように本実施の形態１，２によれば、色空間変換処理を行う色処理装置のＲＧＢ色空間に用いるγ値をより高精度に設定することが可能となり、従来よりも高精度の色変換処理を提供できる。
【００３９】
［実施の形態３］
次に本発明の実施の形態３に係る処理について説明する。ここでは前述の実施の形態１と同様に、γ値を変化させながらＢ軸方向の各色の色差を計算し、色空間全体での色差の平均と、その分散値を求める。この処理のフローチャートは前述の図４に示すフローチャートと同じである。
【００４０】
但し、この実施の形態３では、ステップＳ１４において、Ｂ軸方向に並んでいる各格子点の色差（ｄＥ９４）を以下の式により求める。
【００４１】
［色さ(dE94)を求める式］
２点の色をＬａｂ値１(ｘ1,ｙ1,ｚ1)、Ｌａｂ値２(ｘ2,ｙ2,ｚ2)として、
dE＝√((ｘ1−ｘ2)2＋(ｙ1−ｙ2)2＋(ｚ1−ｚ2)2)；
E1＝√(ｙ22＋ｚ22)；
dL＝ｘ1−ｘ2；
dC＝√(ｙ12＋ｚ12)−√(ｙ22＋ｚ22)；
dH＝dE2−dL2L−dC2；
dH＝(dH＜0.0) ? (0.0): (dH)；
dH＝√(dH)；
Hcmp＝dH/(1.0＋0.015×E1)；
Ccmp＝dC/(1.0＋0.045×E1)；
Lcmp＝dL；
dE94＝√(Lcmp2＋Ccmp2＋Hcmp2)；
...式（４）
同時に全体での平均値と、分散を求める為の計算処理を実行する。そしてステップＳ１５では、全ての格子点データに対する処理が終了したか否かを判定し、そうでない時はステップＳ１３へ戻る。全ての格子点データに対する処理が終了するとステップＳ１６に進み、γ値が終了値（ここでは「２．５」）を越えたか否かを判定し、越えていない時はステップＳ１８に進んでγ値を＋０．１してステップＳ１２へ戻り、越えた場合にはステップＳ１７へ進み、各変数に保持されたデータ、平均色差やその分散値等を記録し、全体の処理を終了させる。
【００４２】
以下は、本実施の形態３において、ＲＧＢ色空間においてγ値を変化させた場合のＢ軸方向の各色の色差の平均と、その分散値を計算した結果例である。
【００４３】

この結果により、γ値が「１．６」の場合に最も低い分散値（6.1512）が得られた。Ｌａｂ色空間において色差平均に対する分散値が低いということは、色分布が均一に近い状態であると考えられる。理想的なＲＧＢ色空間を考えた場合に色の偏りが少ない方が精度的にも有利になるので好適である。
【００４４】
よって本実施の形態３では、分散値が最も低くなるγ値「１．６」を各カラー処理ユニットのＲＧＢ色空間として採用する。
【００４６】
［実施の形態４］
前述の実施野形態３では、各色のＢ軸方向の色差しか計算しなかったが、この実施の形態４では、Ｒ，Ｇ，Ｂ各々の平均色差の分散を計算し、各軸の分散値の平均によってγ値を決定する。この場合は前述の図６のフローチャートで示すように、γ値を変化させながら、Ｒ，Ｇ，Ｂ各軸方向の色差を計算させ、色空間全体での色差の平均と、その分散値を求めればよい。
【００４７】
但し、この実施の形態４において、ステップＳ２４で各軸方向に並んでいる各格子点の色差(dE94)を計算し、同時に全体での平均値と、分散を求めるための計算処理を実行する。そしてステップＳ２５に進み、全体での平均値と、分散を求めるための計算処理を実行する。そしてステップＳ２６に進み、全ての格子点のデータ処理が終了したか否かを判定し、終了していない場合はステップＳ２３に戻り、前述の処理を実行する。
【００４８】
ステップＳ２６で、全ての格子点データに対する処理が終了するとステップＳ２７に進み、γ値が終了値（ここでは「２．２」）を越えたかどうかを判定し、越えていない時はステップＳ２９に進み、γ値を＋０．１してステップＳ２２に戻り、新たにγ値を設定して前述の処理を実行する。一方、ステップＳ２７で、γ値が「２．２」を越えた場合はステップＳ２８に進み、各変数に保持されたデータ、平均色差やその分散値等を記録し、全体の処理を終了させる。
【００４９】
以下は本実施の形態４により、ＲＧＢ色空間においてγ値を変化させた場合の各軸方向の各色の色差の平均と、その分散値を計算した結果例である。
【００５０】

この結果により、γ値が「１．６６」の場合に最も低い分散値（5.4734）が得られた。Ｌａｂ色空間において各色の色差平均に対する分散値が低いということは、各色の分布が均一に近い状態であると考えられる。理想的なＲＧＢ色空間を考えた場合に色の偏りが少ない方が精度的にも有利になる。
【００５１】
本実施の形態４では、色の均一性を考慮したＲＧＢ色空間を定義するために、γ値を変化させながら均等色空間等にＲＧＢの各色をプロットし、該プロットしたデータ間の色差の平均、およびその分散を求めることにより、分散が最も低くなるγ値をより正確に求めることを特徴としている。尚、より理想的なγ値を求めるために、均一性を評価する関数が本実施の形態以外のもの、例えば各色軸方向の分散値に重み係数等を適用したものでも同様な結果を得ることが出来る。更には、計算で求められたγ値に近い値を予め設定（例えばデフォルト設定等）している場合も、同じ効果が得られる。図８は、本実施の形態３において、γ＝１．６６を採用したＲＧＢ色空間であって、上記同様に各点１２５色をＬａｂ均等色空間へプロットした様子を示した図である。図からも明らかなように点の均一性が高くなっていることがわかる。
【００５２】
以上説明したように本実施の形態３，４によれば、色空間変換処理を行う色処理装置のＲＧＢ色空間に用いるγ値をより高精度に設定することが可能となり、従来よりも高精度の色変換処理を提供できる。
【００５３】
［実施の形態５］
ＲＧＢ色空間はＸＹＺ色空間の線形写像であるので、以下の式などにより相互に変換することができる。例えば、色度点が以下の係数で定義されているＲＧＢ色空間の場合、
赤の色度 （0.7018, 0.2873）
緑の色度 （0.2742, 0.6591）
青の色度 （0.0507, 0.0209）
白色点の色度 （0.3127, 0.3290）
以下の変換式より、ＸＹＺ値とＲＧＢ値の対応が決定される。
【００５４】
［ＸＹＺからＲＧＢへの変換式］
ｒ ＝ 3.2408100×ｘ − 1.5373070×ｙ − 0.498586×ｚ
ｇ ＝ -0.969243×ｘ ＋ 1.8759670×ｙ ＋ 0.041555×ｚ
ｂ ＝ 0.0556380×ｘ − 0.2040070×ｙ ＋ 1.057126×ｚ
また、ＲＧＢ値で表示される色データは上記行列式の逆行列式により、前述の実施の形態１（式（２））で説明したようにして求めることができる。
【００５５】
［ＲＧＢからＸＹＺへの変換式］
Ｘ ＝ 0.5893×ｒ ＋ 0.3962×ｇ ＋ 0.0510×ｂ
Ｙ ＝ 0.2412×ｒ ＋ 0.7362×ｇ ＋ 0.0226×ｂ
Ｚ ＝ 0.0092×ｒ ＋ 0.0745×ｇ ＋ 1.0053×ｂ
上記変換式により、ＲＧＢの色データをＸＹＺ値へ変換することができ、更にはＬａｂ等の均等色空間へ変換することも可能である。このように，赤、緑、青、及び白のＸＹ色度点が定義されていればＲＧＢ値をＸＹＺ値へ変換する変換式を直ちに求めることができる。しかしながら、この時点では、γ値は決定されておらず、従来では、例えば、一意に「２．２」等のように、適当に決められていただけである。
【００５６】
本実施の形態５では、定義されたＲＧＢ空間それぞれに適当なγ値を決定するために、その値を変化させながら、ＲＧＢの各軸方向の色差を計算し、色空間全体での色差の平均と、その分散値を求め、更にその分散値を正規化した後、その平均値を算出し、最も分散値が低い時のγ値を最適なγ値として決定するようにしている。
【００５７】
図９は、本発明の実施の形態４に係る画像処理装置における処理を説明するフローチャートで、ここではγ（γ＝ｌｏｇX Ｙ）値を変化させながら、Ｂ軸方向の各色の色差を計算し、色空間全体での色差の平均と、その分散値を求める処理の概要を示している。尚、この処理を実行するプログラムは、予めＣＤ−ＲＯＭ等から外部記憶装置１０５にインストールされており、実行に際してはメモリ１０２にロードされて実行される。
【００５８】
まずステップＳ４１では、平均色差を求める変数（メモリ１０２に設けられる）のリセット等、演算用の変数（メモリ１０２に設けられる）の初期化処理を行う。次にステップＳ４２に進み、γ値を設定し、繰り返し処理によってその値を、「１．０」から「２．５」まで「０．１」ごとに変更するよう構成しておく。そしてステップＳ４３に進み、ＲＧＢ空間における格子点データを順次生成し、その格子点におけるＲＧＢ値をＸＹＺ値へ変換し、更にＬａｂ値に変換する。
【００５９】
次にステップＳ４４に進み、各軸方向に並んでいる各格子点の色差（ｄＥ９４）を上述した計算式（４）により求める。そしてステップＳ４５に進み、全体での平均色差値と、分散を求めるための計算処理を実行する。次にステップＳ４６に進み、全ての格子点（１２５点）に対するデータ処理が全て終了したか否かを判定し、終了していない場合はステップＳ４３に戻り、前述の処理を実行する。
【００６０】
こうしてステップＳ４６で、あるγ値における、全ての格子点のデータ処理が終了したかを調べ、終了した場合にはステップＳ４７に進み、γ値が終了値（ここでは「２．５」）を越えたか否かを判定し、「２．５」以下であればステップＳ５０に進み、γ値を＋０．１してステップＳ４２に進み、新たに次のγ値をセットする。こうしてステップＳ４７でγ値が「２．５」を越えるとステップＳ４８に進み、γ値が「１．０」の時の分散値を基に、各分散値の正規化処理を実行する。そしてステップＳ４９に進み、各変数に保持されたデータ、平均色差やその分散値等を記録し、全体の処理を終了させる。
【００６１】
この計算処理の結果により、上記の色度点の係数に対してはγ値が「２．１０９」の場合に最も低い分散値が得られた。Ｌａｂ色空間において色差平均に対する分散値が低いということは、色分布が均一に近い状態であると考えられる。理想的なＲＧＢ色空間を考えた場合に色の偏りが少ない方が精度的にも有利になるので、好適である。よって、本実施の形態４では、分散値が最も低くなるγ値「２．１０９」を基準色空間のγ値として採用する。
【００６２】
［実施の形態６］
前述の実施の形態４では、定義された各ＲＧＢ色空間それぞれに最適なγ値を求めるために、ＲＧＢ各軸方向の色差を計算し、色空間全体での色差の平均とその分散を求め、更に、その分散値を正規化し、最も分散値が低い時のγ値を最適なγ値として決定していた。この処理を応用した実施の形態５として、色度プロファイルを入力プロファイルとし、前記決定されたγ値を持つ基準ＲＧＢ色空間を出力プロファイルとして設定し、前記画像データの色を適宜変換するシステムなどが挙げられる。
【００６３】
図１０は、本実施の形態５に係る処理の概要を示すフローチャートである。
【００６４】
まずステップＳ６１で、平均色差を求める変数（メモリ１０２に設けられる）のリセット等、演算用の変数（メモリ１０２に設けられる）の初期化処理を行う。次にステップＳ６２に進み、赤、緑、青、及び白の各色度点の入力を行う。そしてステップＳ６３に進み、ステップＳ６２で入力された各色度点から行列のパラメータ等を生成する。次にステップＳ６４に進み、前述したようにして、各軸方向に並んでいる各格子点の色差（ｄＥ９４）を計算し、全体での平均値と分散を求めるための計算処理を実行し、最適なγ値を決定する。
【００６５】
次にステップＳ６５に進み、画像データに埋め込まれている（又はシステムで設定されているデフォルトプロファイル等）を読み出し、それを入力プロファイルとして設定する。そしてステップＳ６６に進み、ステップＳ６３で計算された行列と、ステップＳ６４で得られたγ値を持つＲＧＢ色空間から出力側プロファイルを生成し、入力プロファイルとこれを合成し、その合成されたプロファイルを用いて色変換処理を実行する。そしてステップＳ６７では、作業のために使用した資源を解放し、全体の処理を終了させる。
【００６６】
以上説明したように本実施の形態５によれば、色空間変換処理を行う色処理装置のＲＧＢ色空間に用いるγ値をより高精度に設定することが可能となり、従来よりも高精度の色変換処理を提供できる。
【００６７】
なお本発明は、複数の機器（例えばホストコンピュータ、インターフェース機器、リーダ、プリンタなど）から構成されるシステムに適用しても、一つの機器からなる装置（例えば、複写機、ファクシミリ装置など）に適用してもよい。
【００６８】
また本発明の目的は、前述した実施形態の機能を実現するソフトウェアのプログラムコードを記録した記憶媒体（又は記録媒体）を、システム或いは装置に供給し、そのシステム或いは装置のコンピュータ（又はＣＰＵやＭＰＵ）が記憶媒体に格納されたプログラムコードを読み出し実行することによっても達成される。この場合、記憶媒体から読み出されたプログラムコード自体が前述した実施形態の機能を実現することになり、そのプログラムコードを記憶した記憶媒体は本発明を構成することになる。また、コンピュータが読み出したプログラムコードを実行することにより、前述した実施形態の機能が実現されるだけでなく、そのプログラムコードの指示に基づき、コンピュータ上で稼働しているオペレーティングシステム（ＯＳ）などが実際の処理の一部又は全部を行い、その処理によって前述した実施形態の機能が実現される場合も含まれる。
【００６９】
さらに、記憶媒体から読み出されたプログラムコードが、コンピュータに挿入された機能拡張カードやコンピュータに接続された機能拡張ユニットに備わるメモリに書込まれた後、そのプログラムコードの指示に基づき、その機能拡張カードや機能拡張ユニットに備わるＣＰＵなどが実際の処理の一部又は全部を行い、その処理によって前述した実施形態の機能が実現される場合も含まれる。
【００７０】
以上説明したように本実施の形態によれば、色空間変換処理を行う色処理装置のＲＧＢ色空間に用いるγ値をより高精度に設定することが可能となり、従来よりも高精度の色変換処理を提供できるようになった。
【００７２】
【発明の効果】
以上説明したように本発明によれば、色空間の変換処理を行うＲＧＢ色空間に用いるγ値を、より高精度に設定できるという効果がある。
【図面の簡単な説明】
【図１】ＲＧＢ色空間におけるＲＧＢ軸のそれぞれを５分割した格子１２５点（γ＝１．０）の色をＬａｂ均等色空間上にプロットした様子を示した図である。
【図２】本実施の形態に係る画像処理装置のハードウェア構成を説明するブロック図である。
【図３】ＲＧＢ色空間におけるＲＧＢ軸のそれぞれを５分割した格子点を説明する図である。
【図４】本発明の実施の形態１に係るγ値の決定処理を説明するフローチャートである。
【図５】本実施の形態１における結果として、ＲＧＢ色空間における格子１２５点（γ＝２．０）をＬａｂ均等色空間にプロットした様子を示した図である。
【図６】本発明の実施の形態２に係るγ値の決定処理を説明するフローチャートである。
【図７】本発明の実施の形態に係る、γ値をＲＧＢ色空間への設定処理を説明するフローチャートである。
【図８】本実施の形態３における結果として、ＲＧＢ色空間における格子１２５点（γ＝１．６８）をＬａｂ均等色空間にプロットした様子を示した図である。
【図９】本発明の実施の形態４に係るγ値の決定処理を説明するフローチャートである。
【図１０】本発明の実施の形態５に係るγ値の決定処理を説明するフローチャートである。[0001]
BACKGROUND OF THE INVENTION
  The present invention provides an RGB color space defined by chromaticity points.AmongIt relates to what determines the γ value.
[0002]
[Prior art]
In a color processing apparatus that handles color data, such as a color printer apparatus, for example, when color matching is performed between a display color of a color monitor and a reproduction color in a printer, a reference color space (for example, a standard RGB color space) In general, a method is adopted in which the color space of color data in each color processing apparatus is matched with the color space serving as a reference. Each color processing apparatus has a local color space (device RGB (luminance space), device CMYK (density space)) due to its unique characteristics. It is necessary to perform correction for each device in accordance with the color characteristics of the device.
[0003]
The color characteristics unique to such a color processing apparatus are structurally held by the apparatus itself. For example, in the case of a color printer apparatus, color materials such as ink and toner, transfer characteristics to paper, It varies depending on each device due to fluctuation characteristics inherent in the electric circuit. Although each of these characteristics is corrected as appropriate, the characteristics of each element are complicated and each element has a complex influence. It is difficult to express the correlation of
[0004]
In general, an accurate output value for an arbitrary input is unknown, but in controlling a color in a color processing device, the color processing device has a unique characteristic from a reference color space (standard RGB color space, etc.). For the purpose of accurately performing color conversion into the color space, 125 sample data (for example, 5 × 5 × 5 =) that are evenly distributed in the input color signal space are prepared, the sample data is measured, and from the measured value The output values for all input signal combinations in a specific area are obtained by interpolation calculation, and data such as a memory table is created by selecting the appropriate data for the color conversion process from the calculated output values in a timely manner. ing.
[0005]
[Problems to be solved by the invention]
In the reference color space described above, the chromaticity point is appropriately determined from the viewpoint of the color reproduction range of each device, but the γ value has no clear basis and is in an appropriately defined state. Was used mechanically. Therefore, since it was used without fully examining whether or not the defined value is appropriate, some problems also occurred. For example, for an RGB color system (eg, color monitor, color printer, etc.) designed with a γ value of about “1.4” to “1.5”, the γ value is “2.2”. When the standard RGB color space is applied, the image itself may have a dark color tone, and the gradation reproducibility in the dark portion has deteriorated. In the opposite case, in the case of a color system designed with a γ value of “2.2”, it becomes brighter overall when connected to a color system designed with a γ value of “1.4”. There were problems such as poor color reproduction in the highlight area.
[0007]
  The present inventionIs made in view of the above conventional example,The γ value used in the RGB color space for color space conversion can be set with higher accuracy.Aims to.
[0008]
[Means for Solving the Problems]
  In order to achieve the above object, the color processing apparatus of the present invention comprises the following arrangement. That is,
  A color processing device for determining a γ value of an RGB color space defined by chromaticity points,
  For each of a plurality of preset γ values,Corresponds to a plurality of lattice points obtained by dividing the R, G, and B axes at equal intervals.Conversion means for converting RGB data into uniform color space data using a conversion formula corresponding to the chromaticity point;
  A color difference of each of a plurality of pairs of uniform color space data selected from a plurality of uniform color space data corresponding to each of the plurality of γ values is calculated, and the calculated corresponding to each of the plurality of γ values Computing means for computing a variance value for each of the plurality of γ values from a plurality of color differences;
  Determining means for determining, as the γ value of the RGB color space, a γ value corresponding to the lowest dispersion value among the calculated dispersion values for each of the plurality of γ values.
[0011]
  In order to achieve the above object, the color processing method of the present invention comprises the following steps. That is,
  A color processing method for determining a γ value in an RGB color space defined by chromaticity points,
  For each of a plurality of preset γ values,Corresponds to a plurality of lattice points obtained by dividing the R, G, and B axes at equal intervals.A conversion step of converting RGB data into uniform color space data using a conversion formula corresponding to the chromaticity point;
  A color difference of each of a plurality of pairs of uniform color space data selected from a plurality of uniform color space data corresponding to each of the plurality of γ values is calculated, and the calculated corresponding to each of the plurality of γ values A calculation step of calculating a variance value for each of the plurality of γ values from a plurality of color differences;
  And determining a γ value corresponding to the lowest dispersion value among the calculated dispersion values for each of the plurality of γ values as a γ value in the RGB color space.
[0014]
DETAILED DESCRIPTION OF THE INVENTION
Preferred embodiments of the present invention will be described below in detail with reference to the accompanying drawings.
[0015]
For example, an RGB color space representing a color image signal obtained from a scanner or the like is a linear mapping of the XYZ color space, and can be converted into each other by the following equation. For example, in the case of an RGB color space where chromaticity points are defined by the following coefficients:
Red chromaticity Xr = 0.64 Yr = 0.33
Green chromaticity Xg = 0.30 Yg = 0.60
Blue chromaticity Xb = 0.15 Yb = 0.06
Chromaticity of white point Xw = 0.3127 Yw = 0.3290
The correspondence between XYZ values and RGB values is determined from the following conversion formula.
[0016]
[Conversion formula from XYZ to RGB]
r = 3.2408100 × x−1.5373070 × y−0.498586 × z
g = -0.969243 × x + 1.8759670 × y + 0.041555 × z
b = 0.0556380 × x−0.2040070 × y + 1.057126 × z
... Formula (1)
Further, the color data displayed as RGB values can be obtained by the inverse determinant of the above determinant. By substituting the parameters of the above equation (1) into the variables (a11,..., A33) of the following equations, the inverse matrix parameter variables (x11,..., X33) can be obtained.
[0017]
[Formula for inverse matrix]
detA = 1 / (a11 * a22 * a33 + a12 * a23 * a31 + a13 * a21 * a32
-(A13 x a22 x a31)-(a12 x a21 x a33)-(a11 x a23 x a32));
x11 = detA × (a22 × a33−a23 × a32);
x21 = detA × (a21 × a33−a23 × a31) × (−1.0);
x31 = detA × (a21 × a32−a22 × a31);
x12 = detA × (a12 × a33−a13 × a32) × (−1.0);
x22 = detA × (a11 × a33−a13 × a31);
x32 = detA × (a11 × a32−a12 × a31) × (−1.0);
x13 = detA × (a12 × a23−a13 × a22);
x23 = detA × (a11 × a23−a13 × a21) × (−1.0);
x33 = detA × (a11 × a22−a12 × a21);
... Formula (2)
[Conversion formula from RGB to XYZ]
X = 0.412411 x r + 0.357584 x g + 0.180454 x b
Y = 0.212649 x r + 0.715169 x g + 0.072182 x b
Z = 0.019332 × r + 0.119195 × g + 0.950393 × b
... Formula (3)
With the conversion equation shown in Equation (3), RGB color data can be converted into XYZ values, and further converted into a uniform color space such as Lab. In the Lab color space, a specified white point is mapped to (100, 0, 0) (= L * a * b *), and the spatial distance in each region can be treated as a color difference. This is one convenient color space for analyzing the color distribution.
[0018]
FIG. 1 shows a state in which 125 colors of a grid obtained by dividing each of the R axis, G axis, and B axis in the RGB color space into five at equal intervals are plotted in the Lab uniform color space, as shown in FIG. (Here, the γ value applied to the RGB color space is “1.0”). In this state, as can be seen from FIG. 1, it can be seen that the dots of each color are not uniformly distributed in this Lab space. Therefore, it is necessary to apply an exponential function (Y = Xγ) to the RGB values, change the γ values as appropriate, and investigate the relationship between the distribution state of each color and the γ value.
[0019]
Here, first, the configuration of the image processing apparatus according to the present embodiment will be described.
[0020]
FIG. 2 is a block diagram showing the configuration of the image processing apparatus according to this embodiment.
[0021]
In the figure, reference numeral 101 denotes a CPU that controls the operation of the entire image processing apparatus, and executes various processes described later according to a program stored in the memory 102. The memory 102 is composed of a RAM and stores the control program of the CPU 101 as described above, and is also used as a work area for temporarily holding various data when the CPU 101 executes various processes. . Reference numeral 103 denotes a display unit, which is composed of a liquid crystal, a CRT, or a plasma display, for example, and can display a color image. A network interface 104 controls an interface with a communication line or a LAN. Reference numeral 105 denotes an external storage device such as a hard disk or an MO, in which various application programs and drivers are installed. When these programs are executed, they are loaded into the memory 102 and executed. The external storage device 105 also stores various data such as color image data. An input unit 106 includes a pointing device such as a keyboard and a mouse, and is used by a user to input various commands and data. Reference numeral 107 denotes a printer interface, and reference numeral 108 denotes a color printer, such as an ink jet printer or a laser beam printer. With such a configuration, the color image data input via the network and the color image data stored in the external storage device 105 are displayed on the display unit 103 or based on the control of the printer driver in the memory 102. Color recording is performed by the printer 108 via the printer interface 107.
[0022]
FIG. 4 is a flowchart for explaining processing in the image processing apparatus according to Embodiment 1 of the present invention. Here, the color difference of each color in the B-axis direction is calculated while changing the γ (γ = logX Y) value, An overview of the process of obtaining the average of color differences in the entire color space and the dispersion value thereof is shown. Note that a program for executing this processing is installed in advance in the external storage device 105 from a CD-ROM or the like, and is loaded into the memory 102 for execution.
[0023]
First, in step S11, initialization processing is performed for variables for calculation (provided in the memory 102), such as resetting a variable for obtaining the average color difference (provided in the memory 102). In step S12, a γ value is set, and the value is changed by “0.1” from “1.0” to “2.5” by repetition processing. In step S13, grid point data in the RGB space is sequentially generated, the RGB values at the grid points are converted into XYZ values, and further converted into Lab values.
[0024]
In step S14, the color difference between the lattice points arranged in the B-axis direction is calculated, and at the same time, a calculation process for obtaining the average value and the variance is executed. In step S15, it is determined whether or not all data processing for all grid points (125 points) has been completed. If not, the process returns to step S13 to execute the above-described processing.
[0025]
Thus, in step S15, it is checked whether or not the data processing of all grid points at a certain γ value has been completed. If completed, the process proceeds to step S16, where the γ value exceeds the end value (here, “2.5”). If it is “2.5” or less, the process proceeds to step S18, the γ value is incremented by +0.1, and the process proceeds to step S12 to newly set the next γ value. In this way, when the γ value exceeds “2.5” in step S16, the process proceeds to step S17, where the data, average color difference, dispersion value, etc. held in each variable are recorded, and the entire process is terminated.
[0026]
The following is an example of the result of calculating the average of the color difference of each color in the B-axis direction and the variance value when the γ value is changed in the RGB color space of the present embodiment.
[0027]

As a result, when the γ value is “2.0”, the dispersion value (4.7310) is the lowest. A low dispersion value for the color difference average in the Lab color space is considered to be a state in which the color distribution is nearly uniform. In the case of an ideal RGB color space, a smaller color deviation is advantageous in terms of accuracy, and is more preferable. Therefore, in the present embodiment, the γ value “2.0” at which the variance value is the lowest is employed in the RGB color space in each color processing apparatus.
[0028]
FIG. 5 shows an RGB color space employing γ = 2.0, and shows a state in which each color of 125 grid points is plotted in the Lab uniform color space in the same manner as described above. As can be seen from FIG. 5, it can be seen that the uniformity of the plotted points is higher than in FIG.
[0029]
[Embodiment 2]
In the first embodiment, only the color difference in the B-axis direction of each color was calculated, but the variance of the average color difference in each of the R, G, and B axis directions was calculated, and the average of the variance values of each axis was calculated. The γ value may be determined.
[0030]
  FIG. 6 illustrates processing according to Embodiment 2 of the present invention.flowchartHere, while changing the γ value from “1.0” to “2.2” in units of “0.1”, the color difference in the R, G, B axis directions is calculated, and the total color space is calculated. An overview of processing for obtaining an average of color differences and a dispersion value thereof is shown.
[0031]
First, in step S21, initialization processing of variables for calculation (provided in the memory 102) such as resetting of variables for obtaining the average color difference (provided in the memory 102) is performed. In step S22, the γ value is initially set to an initial value “1.0”, and the value is changed by “0.1” from “1.0” to “2.2” by repetition processing. It is configured as follows. In step S23, grid point data in the RGB color space is sequentially generated, the data is converted from RGB values to XYZ values, and further converted to Lab values. Then, the process proceeds to step S24, and the color difference between the lattice points arranged in the respective axial directions is calculated. Then, the process proceeds to step S25, and a calculation process for obtaining an average value and variance is executed. Then, the process proceeds to step S26, where it is determined whether or not the data processing for all grid points has been completed. If not, the process returns to step S23 and the above-described processing is executed.
[0032]
In step S26, when the processing for all the grid point data is completed, the process proceeds to step S27, where it is determined whether or not the γ value exceeds the end value (here, “2.2”), and if not, the process proceeds to step S29. Then, the γ value is incremented by +0.1, and the process returns to step S22 to newly set the γ value and execute the above-described processing. On the other hand, if the γ value exceeds “2.2” in step S27, the process proceeds to step S28 where the data held in each variable, the average color difference, its variance value, etc. are recorded, and the entire process is terminated.
[0033]
The following is the result of calculating the average of the color difference of each color in each axial direction and the variance value when the γ value is changed in the RGB color space in the second embodiment.
[0034]

As a result, when the γ value is “1.90”, the dispersion value (5.7334) is the lowest. A low dispersion value for the color difference average of each color in the Lab color space is considered to be a state where the distribution of each color is nearly uniform. When an ideal RGB color space is considered, it is advantageous in terms of accuracy to have less color deviation.
[0035]
In the first and second embodiments, in order to define an RGB color space in consideration of color uniformity, RGB colors are plotted in a uniform color space or the like while changing the γ value, and the color difference between the plotted data It is characterized in that the γ value at which the variance becomes the lowest is obtained by obtaining the average of and the variance thereof. In order to obtain a more ideal γ value, the same result can be obtained even when the function for evaluating uniformity is other than that of the present embodiment, for example, when a weighting coefficient or the like is applied to the dispersion value in each color axis direction. . Furthermore, even when a value close to the γ value obtained by calculation is set in advance (for example, default setting), the same effect as in the present embodiment can be obtained.
[0036]
FIG. 7 is a flowchart for explaining the determination of γ value and the setting process thereof according to the present embodiment.
[0037]
First, in step S31, for example, as shown in the flowchart of FIG. 4 or FIG. 6, the colors in the reference RGB color space are plotted in the uniform color space with respect to the γ values in a predetermined range. In step S32, a variance value for each γ value is obtained. In step S33, the smallest dispersion value among the dispersion values obtained for each of these γ values is obtained, and the γ value corresponding to the dispersion value is obtained. In step S34, the γ value is set as a γ value in the RGB color space of the display unit 103, printer 108, scanner (not shown) connected to the image processing apparatus.
[0038]
As described above, according to the first and second embodiments, it is possible to set the γ value used in the RGB color space of the color processing apparatus that performs the color space conversion processing with higher accuracy, and higher accuracy than before. Color conversion processing can be provided.
[0039]
[Embodiment 3]
Next, processing according to Embodiment 3 of the present invention will be described. Here, as in the first embodiment, the color difference of each color in the B-axis direction is calculated while changing the γ value, and the average of the color difference in the entire color space and its variance value are obtained. The flowchart of this process is the same as the flowchart shown in FIG.
[0040]
However, in the third embodiment, in step S14, the color difference (dE94) of each lattice point arranged in the B-axis direction is obtained by the following equation.
[0041]
[Formula for obtaining color (dE94)]
Assuming that the two colors are Lab value 1 (x1, y1, z1) and Lab value 2 (x2, y2, z2),
dE = √ ((x1-x2) 2+ (y1-y2) 2+ (z1-z2) 2);
E1 = √ (y22 + z22);
dL = x1-x2;
dC = √ (y12 + z12) −√ (y22 + z22);
dH = dE2-dL2L-dC2;
dH = (dH <0.0)? (0.0): (dH);
dH = √ (dH);
Hcmp = dH / (1.0 + 0.015 × E1);
Ccmp = dC / (1.0 + 0.045 × E1);
Lcmp = dL;
dE94 = √ (Lcmp2 + Ccmp2 + Hcmp2);
... Formula (4)
At the same time, a calculation process for obtaining the average value and variance is executed. In step S15, it is determined whether or not the processing for all grid point data has been completed. If not, the process returns to step S13. When the processing for all the grid point data is completed, the process proceeds to step S16, where it is determined whether or not the γ value exceeds the end value (here, “2.5”). If not, the process proceeds to step S18 and the γ value is determined. +0.1 and return to step S12. If it exceeds, the process proceeds to step S17, where the data, average color difference, dispersion value, etc. held in each variable are recorded, and the whole process is terminated.
[0042]
The following is an example of a result of calculating the average of the color difference of each color in the B-axis direction and the variance value when the γ value is changed in the RGB color space in the third embodiment.
[0043]

As a result, the lowest dispersion value (6.1512) was obtained when the γ value was “1.6”. A low dispersion value for the color difference average in the Lab color space is considered to be a state in which the color distribution is nearly uniform. When an ideal RGB color space is considered, a smaller color deviation is advantageous in terms of accuracy, which is preferable.
[0044]
  Therefore, in the third embodiment, the γ value “1.6Is adopted as the RGB color space of each color processing unit.
[0046]
[Embodiment 4]
In the above-described third embodiment, only the color difference in the B-axis direction of each color is calculated, but in this fourth embodiment, the variance of the average color difference of each of R, G, and B is calculated, and the variance value of each axis is calculated. The γ value is determined by averaging. In this case, as shown in the flowchart of FIG. 6, the color difference in each of the R, G, and B directions is calculated while changing the γ value, and the average of the color difference in the entire color space and its dispersion value can be obtained. That's fine.
[0047]
However, in the fourth embodiment, the color difference (dE94) of each grid point arranged in the direction of each axis is calculated in step S24, and at the same time, a calculation process for obtaining the average value and the variance is executed. Then, the process proceeds to step S25, and a calculation process for obtaining an average value and variance is executed. Then, the process proceeds to step S26, where it is determined whether or not the data processing for all grid points has been completed. If not, the process returns to step S23 and the above-described processing is executed.
[0048]
In step S26, when the processing for all the grid point data is completed, the process proceeds to step S27, where it is determined whether or not the γ value exceeds the end value (here, “2.2”), and if not, the process proceeds to step S29. Then, the γ value is incremented by +0.1, and the process returns to step S22 to newly set the γ value and execute the above-described processing. On the other hand, if the γ value exceeds “2.2” in step S27, the process proceeds to step S28 where the data held in each variable, the average color difference, its variance value, etc. are recorded, and the entire process is terminated.
[0049]
The following is an example of the result of calculating the average of the color difference of each color in each axial direction and the variance value when the γ value is changed in the RGB color space according to the fourth embodiment.
[0050]

As a result, the lowest dispersion value (5.4734) was obtained when the γ value was “1.66”. A low dispersion value for the color difference average of each color in the Lab color space is considered to be a state where the distribution of each color is nearly uniform. When an ideal RGB color space is considered, it is advantageous in terms of accuracy to have less color deviation.
[0051]
  In the fourth embodiment, in order to define an RGB color space in consideration of color uniformity, each color of RGB is plotted in a uniform color space or the like while changing the γ value, and the average of the color differences between the plotted data , And its dispersion, the γ value at which the dispersion is lowest is obtained more accurately. In addition, in order to obtain a more ideal γ value, the same result can be obtained even when the function for evaluating uniformity is other than that of the present embodiment, for example, a weighting coefficient or the like is applied to the dispersion value in each color axis direction. I can do it. Further, the same effect can be obtained when a value close to the γ value obtained by calculation is set in advance (for example, default setting or the like).FIG. 8 is an RGB color space employing γ = 1.66 in the third embodiment, and shows a state where 125 colors of each point are plotted in the Lab uniform color space in the same manner as described above. As can be seen from the figure, the uniformity of the points is high.
[0052]
As described above, according to the third and fourth embodiments, it is possible to set the γ value used in the RGB color space of the color processing apparatus that performs color space conversion processing with higher accuracy, which is higher accuracy than before. Color conversion processing can be provided.
[0053]
[Embodiment 5]
Since the RGB color space is a linear mapping of the XYZ color space, they can be converted into each other by the following equations. For example, in the case of an RGB color space where chromaticity points are defined by the following coefficients:
Red chromaticity (0.7018, 0.2873)
Green chromaticity (0.2742, 0.6591)
Blue chromaticity (0.0507, 0.0209)
Chromaticity of white point (0.3127, 0.3290)
The correspondence between XYZ values and RGB values is determined from the following conversion formula.
[0054]
[Conversion formula from XYZ to RGB]
r = 3.2408100 × x−1.5373070 × y−0.498586 × z
g = -0.969243 × x + 1.8759670 × y + 0.041555 × z
b = 0.0556380 × x−0.2040070 × y + 1.057126 × z
Further, the color data displayed in RGB values can be obtained as described in the first embodiment (formula (2)) by the inverse determinant of the determinant.
[0055]
[Conversion formula from RGB to XYZ]
X = 0.5893 x r + 0.3962 x g + 0.0510 x b
Y = 0.2412 x r + 0.7362 x g + 0.0226 x b
Z = 0.0002 × r + 0.0745 × g + 1.0053 × b
With the above conversion formula, RGB color data can be converted into XYZ values, and further converted into a uniform color space such as Lab. As described above, if the XY chromaticity points of red, green, blue, and white are defined, a conversion formula for converting RGB values into XYZ values can be immediately obtained. However, at this point in time, the γ value has not been determined, and conventionally, it has been determined appropriately, for example, uniquely “2.2” or the like.
[0056]
In the fifth embodiment, in order to determine an appropriate γ value for each defined RGB space, the color difference in each of the RGB axis directions is calculated while changing the value, and the average of the color differences in the entire color space is calculated. The dispersion value is obtained, the dispersion value is normalized, the average value is calculated, and the γ value when the dispersion value is the lowest is determined as the optimum γ value.
[0057]
FIG. 9 is a flowchart for explaining processing in the image processing apparatus according to Embodiment 4 of the present invention. Here, the color difference of each color in the B-axis direction is calculated while changing the γ (γ = logX Y) value, An overview of the process of obtaining the average of color differences in the entire color space and the dispersion value thereof is shown. Note that a program for executing this processing is installed in advance in the external storage device 105 from a CD-ROM or the like, and is loaded into the memory 102 for execution.
[0058]
First, in step S41, initialization processing of variables for calculation (provided in the memory 102) such as resetting of variables for obtaining the average color difference (provided in the memory 102) is performed. In step S42, a γ value is set, and the value is changed by “0.1” from “1.0” to “2.5” by repetition processing. In step S43, grid point data in the RGB space is sequentially generated, and the RGB values at the grid points are converted into XYZ values and further converted into Lab values.
[0059]
Next, proceeding to step S44, the color difference (dE94) of each grid point arranged in the direction of each axis is obtained by the above-described calculation formula (4). In step S45, the average color difference value and the calculation process for obtaining the variance are executed. Next, the process proceeds to step S46, where it is determined whether or not the data processing for all the grid points (125 points) has been completed. If not, the process returns to step S43 and the above-described processing is executed.
[0060]
Thus, in step S46, it is checked whether or not the data processing of all grid points at a certain γ value has been completed. If completed, the process proceeds to step S47, where the γ value exceeds the end value (here, “2.5”). If it is “2.5” or less, the process proceeds to step S50, the γ value is incremented by +0.1, the process proceeds to step S42, and the next γ value is newly set. Thus, when the γ value exceeds “2.5” in step S47, the process proceeds to step S48, and normalization processing of each variance value is executed based on the variance value when the γ value is “1.0”. In step S49, the data held in each variable, the average color difference, the variance value, and the like are recorded, and the entire process is terminated.
[0061]
As a result of this calculation process, the lowest dispersion value was obtained for the coefficient of the chromaticity point when the γ value was “2.109”. A low dispersion value for the color difference average in the Lab color space is considered to be a state in which the color distribution is nearly uniform. Considering an ideal RGB color space, a smaller color deviation is advantageous in terms of accuracy, and thus is preferable. Therefore, in the fourth embodiment, the γ value “2.109” having the lowest variance value is adopted as the γ value of the reference color space.
[0062]
  Embodiment6]
  In the above-described fourth embodiment, in order to obtain the optimum γ value for each defined RGB color space, the color difference in each of the RGB axis directions is calculated, the average of the color differences in the entire color space and the variance thereof are obtained, Further, the variance value is normalized, and the γ value when the variance value is the lowest is determined as the optimum γ value. As a fifth embodiment to which this process is applied, there is a system that sets a chromaticity profile as an input profile, sets a reference RGB color space having the determined γ value as an output profile, and appropriately converts the color of the image data. Can be mentioned.
[0063]
FIG. 10 is a flowchart showing an outline of processing according to the fifth embodiment.
[0064]
First, in step S61, initialization processing of variables for calculation (provided in the memory 102) such as resetting of variables for obtaining the average color difference (provided in the memory 102) is performed. In step S62, red, green, blue, and white chromaticity points are input. In step S63, matrix parameters and the like are generated from the chromaticity points input in step S62. Next, in step S64, as described above, the color difference (dE94) of each grid point arranged in the direction of each axis is calculated, and the calculation process for obtaining the average value and the variance is executed to obtain the optimum. Determine the correct γ value.
[0065]
In step S65, the image data embedded in the image data (or the default profile set in the system) is read and set as an input profile. In step S66, an output profile is generated from the matrix calculated in step S63 and the RGB color space having the γ value obtained in step S64, and this is combined with the input profile. To perform color conversion processing. In step S67, the resources used for the work are released, and the entire process is terminated.
[0066]
As described above, according to the fifth embodiment, it is possible to set the γ value used in the RGB color space of the color processing apparatus that performs the color space conversion process with higher accuracy, so that the color with higher accuracy than before can be obtained. A conversion process can be provided.
[0067]
Note that the present invention can be applied to a system (for example, a copier, a facsimile machine, etc.) composed of a single device even if it is applied to a system composed of a plurality of devices (for example, a host computer, interface device, reader, printer, etc.). May be.
[0068]
Another object of the present invention is to supply a storage medium (or recording medium) on which a program code of software for realizing the functions of the above-described embodiments is recorded to a system or apparatus, and to perform computer (or CPU or MPU) of the system or apparatus. ) Is also achieved by reading and executing the program code stored in the storage medium. In this case, the program code itself read from the storage medium realizes the functions of the above-described embodiments, and the storage medium storing the program code constitutes the present invention. Further, by executing the program code read by the computer, not only the functions of the above-described embodiments are realized, but also an operating system (OS) running on the computer based on the instruction of the program code. A case where part or all of the actual processing is performed and the functions of the above-described embodiments are realized by the processing is also included.
[0069]
Furthermore, after the program code read from the storage medium is written into a memory provided in a function expansion card inserted into the computer or a function expansion unit connected to the computer, the function is determined based on the instruction of the program code. The case where the CPU of the expansion card or the function expansion unit performs part or all of the actual processing and the functions of the above-described embodiments are realized by the processing is also included.
[0070]
As described above, according to the present embodiment, it is possible to set the γ value used in the RGB color space of the color processing apparatus that performs color space conversion processing with higher accuracy, and color conversion with higher accuracy than before. Processing can now be provided.
[0072]
【The invention's effect】
  As explained aboveAccording to the present invention, the γ value used in the RGB color space for performing the color space conversion process can be set with higher accuracy.
[Brief description of the drawings]
FIG. 1 is a diagram showing a state in which colors of 125 lattice points (γ = 1.0) obtained by dividing each of the RGB axes in an RGB color space into five are plotted on a Lab uniform color space.
FIG. 2 is a block diagram illustrating a hardware configuration of the image processing apparatus according to the present embodiment.
FIG. 3 is a diagram illustrating lattice points obtained by dividing each of the RGB axes in the RGB color space into five parts.
FIG. 4 is a flowchart for explaining γ value determination processing according to Embodiment 1 of the present invention;
FIG. 5 is a diagram showing a state in which 125 grid points (γ = 2.0) in the RGB color space are plotted in the Lab uniform color space as a result of the first embodiment.
FIG. 6 is a flowchart illustrating a γ value determination process according to the second embodiment of the present invention.
FIG. 7 is a flowchart illustrating processing for setting a γ value to an RGB color space according to an embodiment of the present invention.
FIG. 8 is a diagram showing a state where 125 grid points (γ = 1.68) in the RGB color space are plotted in the Lab uniform color space as a result of the third embodiment.
FIG. 9 is a flowchart illustrating γ value determination processing according to Embodiment 4 of the present invention;
FIG. 10 is a flowchart for explaining γ value determination processing according to Embodiment 5 of the present invention;

Claims (5)

A color processing device for determining a γ value of an RGB color space defined by chromaticity points,
For each of a plurality of preset γ values, RGB data corresponding to a plurality of grid points obtained by dividing the R axis, the G axis, and the B axis in the RGB color space at equal intervals, Conversion means for converting to uniform color space data using a conversion formula corresponding to the degree point;
A color difference of each of a plurality of pairs of uniform color space data selected from a plurality of uniform color space data corresponding to each of the plurality of γ values is calculated, and the calculated corresponding to each of the plurality of γ values Computing means for computing a variance value for each of the plurality of γ values from a plurality of color differences;
Determining means for determining a γ value corresponding to the lowest variance value among the calculated variance values for each of the plurality of γ values as the γ value of the RGB color space;
A color processing apparatus comprising: For each of the plurality of preset γ values, the conversion means converts a plurality of RGB data arranged on each RGB axis into a plurality of uniform colors using a conversion formula corresponding to the chromaticity point. Convert to spatial data,
The computing means computes each color difference of a plurality of uniform color space data pairs selected from the plurality of uniform color space data corresponding to each of the plurality of γ values for each of the RGB axes, A variance value is calculated for each of the plurality of γ values for each of the RGB axes from the calculated plurality of color differences corresponding to each of a plurality of γ values,
The deciding means decides a γ value corresponding to the lowest variance value among the average values of variance values calculated for each of the RGB axes as the γ value of the RGB color space. The color processing apparatus according to 1.   The determining means multiplies the variance value calculated for each axis of RGB by a weighting factor, and calculates a γ value corresponding to the lowest variance value among the average values of the variance values multiplied by the weighting factor as the RGB color. The color processing apparatus according to claim 2, wherein the color processing apparatus determines the γ value of the space. A color processing method for determining a γ value in an RGB color space defined by chromaticity points,
For each of a plurality of preset γ values, RGB data corresponding to a plurality of grid points obtained by dividing the R axis, the G axis, and the B axis in the RGB color space at equal intervals, A conversion step of converting to uniform color space data using a conversion formula according to the degree point;
A color difference of each of a plurality of pairs of uniform color space data selected from a plurality of uniform color space data corresponding to each of the plurality of γ values is calculated, and the calculated corresponding to each of the plurality of γ values A calculation step of calculating a variance value for each of the plurality of γ values from a plurality of color differences;
A determination step of determining a γ value corresponding to the lowest dispersion value among the calculated dispersion values for each of the plurality of γ values as a γ value of the RGB color space;
A color processing method characterized by comprising:   A computer-readable storage medium storing a program for causing a computer to function as the color processing apparatus according to any one of claims 1 to 3.

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