JP2004241893A - Device and method for color conversion - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a color converter and a color converting method capable of suppressing an image from blurring owing to the suppression of the influence of a noise component included in color data without further emphasizing the influence of the noise component even when the color data are processed for higher color saturation and lightness. <P>SOLUTION: The color converter is provided with: a color correction quantity generating means (1, 1b, 1c, and 1d) of finding color correction quantities by carrying out color correction quantity calculations (4, 4c, and 4c) and noise removal (3, 11, and 12) both based upon 1st image data; and an adding means (2) of finding 2nd image data by adding the color correction quantities to the 1st image data. A noise removing means is composed of a low-pass filter which cuts off a high-frequency component in inputted data and transmits a low-frequency component. <P>COPYRIGHT: (C)2004,JPO&NCIPI

Description

【００２０】
式（１）において、Ｒｉ［ｎ］はｎ番目に入力された第一の色データを表し、関数ｆは重み付け加算を表す。
なお、雑音除去手段に最初のデータが入力されたときには、データ格納部５ａ〜５ｈには全て最初のデータと同一のデータが一斉に入力されるものとする。また、雑音除去手段に最後のデータが入力された後には、データ格納部５ａには雑音除去手段に入力された最後のデータと同一のデータが入力され続けるものとする。
【００２１】
図３は、本来の色データＲｓ、Ｇｓ、Ｂｓの一例を表す図である。図において、横軸は各データの存在する画素位置を表し、縦軸は各データの大きさを表す。また、赤を表すデータＲｓに注目するため、Ｒｓの変化は特に太線にて示している。画素位置０から１６、および４３から６３においてはＲｓ＝８０、Ｇｓ＝４８、Ｂｓ＝４８であり、画素位置１７から４２においてはＲｓ＝１６０、Ｇｓ＝４８、Ｂｓ＝４８である。これは、画素位置１７から４２にその両側と比較して彩度、明度の高い赤色が存在することを意味する。ここで、彩度は色信号の最大値と最小値の差を最大値にて除したもので表すことができ、明度は色信号の最大値で表すことができる。これによると、画素位置０から１６、および４３から６３における彩度は０．４、明度は８０と表され、画素位置１７から４２における彩度は０．７、明度は１６０と表される。
【００２２】
図４は、本来の色データＲｓ、Ｇｓ、Ｂｓに雑音Ｒｎ、Ｇｎ、Ｂｎが付加された場合の色データ、すなわち雑音成分が存在する場合の画像表示装置に入力される第一の色データＲｉ、Ｇｉ、Ｂｉの一例を表す図である。図において、横軸は各データの存在する画素位置を表し、縦軸は各データの大きさを表す。また、赤を表すデータＲｉに注目するため、Ｒｉの変化は特に太線にて示している。図中で矢印にて示しているのは、画素位置２６および２７のＲｉであり、それぞれ値は１４６、１７４である。画素位置２６および２７のＲｉは、本来は同じ値になるべきであり、値に相違があるのは雑音成分の影響である。この時、画素位置２６および２７においてＧｉは４０、４６であり、Ｂｉは３８、６０である。
【００２３】
ここで、重み付け加算手段６において、単純平均値が算出されるものとする。
図４に示した第一の色データＲｉ、Ｇｉ、Ｂｉを入力として、図２を参照して説明した雑音除去手段３にて求められる雑音除去された色データＲ２、Ｇ２、Ｂ２を図５に示す。図において、横軸は各データの存在する画素位置を表し、縦軸は各データの大きさを表す。図５より、雑音除去された色データＲ２、Ｇ２、Ｂ２において、データの変化部分の変化も緩やかになっているものの、雑音成分も除去されている。
【００２４】
色補正量演算手段４は、雑音除去された色データＲ２、Ｇ２、Ｂ２を用いて色補正量Ｒ１、Ｇ１、Ｂ１を算出し、算出した色補正量Ｒ１、Ｇ１、Ｂ１を色補正量加算手段２へと出力する。色補正量加算手段２には第一の色データＲｉ、Ｇｉ、Ｂｉも入力され、色補正量加算手段２において、色補正量Ｒ１、Ｇ１、Ｂ１と加算されて第二の色データＲｏ、Ｇｏ、Ｂｏが算出される。
【００２５】
色補正量演算手段４は、例えば少なくとも一つの色の明度及び彩度の少なくとも一つを高めるための補正量を求めるための演算を行うものであり、例えば下記の式（２）に示す線形演算により色補正量Ｒ１、Ｇ１、Ｂ１を求める。
【００２６】
【数２】

【００２７】
下記式（３）に示す係数は、式（２）に対して用いられる係数の一例である。
【００２８】
【数３】

【００２９】
上記式（３）に示す係数は、画像データの一つの特定の色である赤の明度、彩度を高めるような係数である。簡単化のため雑音除去された色データＲ２、Ｇ２、Ｂ２の値が、第一の色データＲｉ、Ｇｉ、Ｂｉと同じ値を持つものとし、例えば、雑音除去された色データがＲ２＝８０、Ｇ２＝４８、Ｂ２＝４８である場合を考える。この時、Ｒ１＝２４、Ｇ１＝０、Ｂ１＝０となり、第二の色データはＲｏ＝Ｒ２＋Ｒ１＝１０４、Ｇｏ＝Ｇ２＋Ｇ１＝４８、Ｂｏ＝Ｂ２＋Ｂ１＝４８となる。また、雑音除去された色データがＲ２＝１６０、Ｇ２＝４８、Ｂ２＝４８の場合、Ｒ１＝４８、Ｇ１＝０、Ｂ１＝０となり、第二の色データはＲｏ＝Ｒ２＋Ｒ１＝２０８、Ｇｏ＝Ｇ２＋Ｇ１＝４８、Ｂｏ＝Ｂ２＋Ｂ１＝４８となる。
雑音除去された色データに対して、前者では彩度は０．４、明度は８０となり、後者では彩度は０．７、明度は１６０となる。一方、第二の色データに対して、前者では彩度は０．５４、明度は１０４となり、後者では彩度は０．７７、明度は２０８となる。よって、色変換を行うことにより画像データの明度、彩度ともに高くなっている。
【００３０】
図６は、第二の色データＲｏ、Ｇｏ、Ｂｏの一例を表す図であり、図４に示した第一の色データＲｉ、Ｇｉ、Ｂｉを入力とし、式（３）に示す係数を用いた場合に算出される第二の色データである。図において、横軸は各データの存在する画素位置を表し、縦軸は各データの大きさを表す。また、赤を表すデータＲｏに注目するため、Ｒｏの変化は特に太線にて示している。図中で矢印にて示しているのは、画素位置２６および２７のＲｏであり、それぞれ値は１９３、２２０である。
【００３１】
ここで、画素位置２６および２７における第二の色データＲｏの値の差は、２２０−１９３＝２７である。一方、画素位置２６および２７における第一の色データＲｉの値の差は、１７４−１４６＝２８である。画素位置２６および２７における第一の色データＲｉの値の差は、雑音成分の影響により生じたものである。
【００３２】
よって、本実施の形態における色変換装置または色変換方法によれば、色データに含まれる雑音成分の影響を更に強調することなく、色データの彩度や明度を高めるような処理が行われている。画像データ中の雑音成分の影響が強調されると、画像表示装置に表示される画像は非常に見づらいものとなるが、本実施の形態における色変換装置または色変換方法においては、第二の色データにおける雑音成分の大きさは第一の色成分におけるものと同等となり、雑音成分の影響の増大を回避している。
【００３３】
なお、上記の例では、色補正量演算手段４が一つの色である赤の明度、彩度を高めるような演算を行っているが、二つ以上の色の明度、彩度を高めるような色補正量演算を行うこととしても良い。また明度や彩度を高めるのではなく、逆に低めるような色補正を行う場合にも本発明を適用することができる。明度や彩度を低める場合には色補正量が負の値となる。
【００３４】
本実施の形態における色変換装置または色変換方法において、その特徴は、色補正量生成手段１に雑音除去手段３を備え、色補正量生成手段１において色補正量算出及び雑音除去を行って色補正量を求めて色補正量加算手段２に供給し、色補正量加算手段２では、上記のような雑音除去を経た色補正量と、上記のような雑音除去を経ない第一の画像データとを加算して第二の画像データを求めることである。以下、その効果について、色補正量生成手段１に雑音除去手段３を備えない場合との比較により、さらに詳しく述べる。
【００３５】
上記図１に示す色変換装置において、雑音除去手段３をＲ２＝Ｒｉ、Ｇ２＝Ｇｉ、Ｂ２＝Ｂｉとなるように構成すれば、雑音除去手段３を備えない場合と同等となる。
【００３６】
先程と同様に、図３にて示される本来の色データＲｓ、Ｇｓ、Ｂｓに雑音成分が付加された図４にて示される第一の色データＲｉ、Ｇｉ、Ｂｉが入力される場合を考える。すなわち、画素位置２６および２７において、本来の色データはともにＲｓ＝１６０、Ｇｓ＝４８、Ｂｓ＝４８である。また、画素位置２６および２７において、第一の色データはＲｉ＝１４６、Ｇｉ＝４０、Ｂｉ＝３８、およびＲｉ＝１７４、Ｇｉ＝４６、Ｂｉ＝６０である。
【００３７】
雑音除去手段３が設けられていないので、Ｒ２＝Ｒｉ、Ｇ２＝Ｇｉ、Ｂ２＝Ｂｉが色補正演算手段４に入力される。色補正量演算手段４では、上記式（３）に示す係数を用いた上記式（２）に示すマトリクス演算により色補正量Ｒ１、Ｇ１、Ｂ１を求める。色補正量加算手段２は、第一の色データＲｉ、Ｇｉ、Ｂｉと色補正量Ｒ１、Ｇ１、Ｂ１を加算して第二の色データＲｏ、Ｇｏ、Ｂｏを算出する。
【００３８】
図７は、雑音除去手段３が設けられておらず、Ｒ２＝Ｒｉ、Ｇ２＝Ｇｉ、Ｂ２＝Ｂｉとなる場合における第二の色データＲｏ、Ｇｏ、Ｂｏを表す図である。図において、横軸は各データの存在する画素位置を表し、縦軸は各データの大きさを表す。雑音除去手段３を備えた場合に得られる第二の色データＲｏ、Ｇｏ、Ｂｏを表す図６と比較すると、色変換処理により雑音成分もまた強調されていることが見て取れる。図中で矢印にて示しているのは、画素位置２６および２７の第二の色データＲｏであり、それぞれ値は１８９、２２６である。一方、画素位置２６および２７の第一の色データＲｉの値はそれぞれ１４６、１７４である。画素位置２６および２７の第一の色データＲｉの値の差は１７４−１４６＝２８であり、この値は本画素位置における第一の色データ中に含まれる雑音成分の大きさを表す。一方、画素位置２６および２７の第二の色データＲｏの値の差は２２６−１８９＝３７となり、値が大きくなっている。これは、第一の色データに含まれる雑音成分が強調されていることを表す。画像データ中の雑音成分Ｒｎ、Ｇｎ、Ｂｎの影響を強調されると、画像表示装置に表示される画像は非常に見づらいものとなる。
【００３９】
ここで、前述の通り、雑音除去手段３を備えた本実施の形態における色変換装置にて得られる画素位置２６および２７における第二の色データＲｏの値の差は２７であり、雑音成分は強調されていない。
【００４０】
第一の色データ中に含まれる雑音成分の影響を軽減する方法としては、雑音除去手段を介して第一の色データを色変換手段へと入力することも考えられる。図８は、雑音除去手段を付加した従来の色変換装置の一例を表すブロック図であり、雑音除去手段３を介して色変換手段７に第一の色データを入力するように構成されている。第一の色データＲｉ、Ｇｉ、Ｂｉは雑音除去手段３へと入力される。雑音除去手段３では、第一の色データ中の雑音成分を除去し、雑音除去された色データＲ２、Ｇ２、Ｂ２を出力する。雑音除去された色データＲ２、Ｇ２、Ｂ２は、色変換手段７へと入力される。色変換手段７は、色補正量演算手段４と色補正量加算手段２を備える。色補正量演算手段４は、上述の場合と同様に、上記式（３）に示す係数を用いた上記式（２）に示すマトリクス演算により、雑音除去された色データＲ２、Ｇ２、Ｂ２より、色補正量Ｒ１、Ｇ１、Ｂ１を算出する。色補正量加算手段２は、雑音除去された色データＲ２、Ｇ２、Ｂ２と色補正量Ｒ１、Ｇ１、Ｂ１をそれぞれ加算し、第二の色データＲｏ、Ｇｏ、Ｂｏを算出する。よって、図８に示す色変換装置において、第二の色データＲｏ、Ｇｏ、Ｂｏは下記式（４）にて表される。
【００４１】
【数４】

【００４２】
雑音除去手段３は、上記図２を参照して説明した構成とすることができる。ここで、雑音除去手段３の重み付け加算手段６において、単純平均値が算出されるものとする。
【００４３】
図８に示す色変換装置により求められる第二の色データＲｏ、Ｇｏ、Ｂｏを図９に示す。このとき、図４に示す本来の色データに雑音成分が付加された第一の色データが入力されるものとする。図において、横軸は各データの存在する画素位置を表し、縦軸は各データの大きさを表す。図９より、第二の色データＲｏ、Ｇｏ、Ｂｏにおける雑音成分は除去されているものの、図中に破線にて囲ったように、データの変化部分の変化も緩やかになっている。データの変化部分の変化も緩やかになると、画像のボケとして認識される。例えば、画像中の文字や境界部分にボケが発生することになる。
【００４４】
特に、雑音の影響を受けていない色データ、すなわち雑音成分が含まれない色データが入力された場合に、この画像中のボケは特に問題となる。すなわち、雑音成分がない色データに対しては、画像のボケという欠点のみが発生することとなる。
【００４５】
一方、本実施の形態の色変換装置（図１）により得られる第二の色データＲｏ、Ｇｏ、Ｂｏ（図６）においては、画像のボケも発生していない。すなわち、画素位置１６から１７へのデータの変化、および画素位置４２から４３へのデータの変化はなだらかになっていない。これは、第一の画像データＲｉ、Ｇｉ、Ｂｉには雑音除去を施すことなく色補正量加算手段２に供給しているからである。
【００４６】
以上より、本実施の形態における色変換装置または色変換方法によれば、色データの彩度や明度を高めるような処理を行った場合においても、色データに含まれる雑音成分の影響を更に強調することなく、また、雑音成分の影響の抑制による画像のボケの発生も抑えることが可能となる。
【００４７】
近年、テレビの画像やＤＶＤに記録したビデオの画像をパーソナルコンピュータにより見るような場合も増えつつある。一般に、ビデオ画像やテレビ画像には大きな雑音成分が含まれる場合が多い。一方、パーソナルコンピュータにて発生されるグラフィック画面は非常に雑音成分が少なく、また文字情報など精細度が要求される表示も多く、画像のボケは非常に問題となる。一方、画像表示装置においては、入力される画像データが、ビデオ画像やテレビ画像を表すものであるか、文字情報を表すものであるかを判別する手段がない場合が一般的である。したがって、パーソナルコンピュータにてビデオ画像やテレビ画像を観賞する際には、ビデオ画像やテレビ画像における雑音成分の強調を抑制でき、しかもパーソナルコンピュータで発生されるグラフィックの画面における画像のボケも抑制可能な本発明の色変換装置または色変換方法は非常に有用である。
【００４８】
なお、上記においては、色補正量演算手段の構成としてはマトリクス演算方式であるものとしたが、テーブル変換方式であっても同様である。テーブル変換方式は、入力される色データの組のそれぞれに対して変換後のデータを指定できるため、様々な変換特性が実現可能である長所があるが、データの時間的、空間的な変化、すなわち雑音の影響に対しては、マトリクス演算方式と比較して特に改善されるものでない。したがって、色補正量生成手段に雑音除去手段を備えることにより、色データに含まれる雑音成分の影響を更に強調することなく色変換処理が行えるという効果は、マトリクス演算方式と同様である。
【００４９】
また、本実施の形態では、ハードウェアで実現する場合について述べたが、同様の処理をソフトウェアによって実現することも可能である。
【００５０】
実施の形態２．
上記実施の形態１においては、色補正量生成手段は、雑音除去手段により雑音を除去されたデータを用いて色補正量を求めるように構成した。このようにする代りに、色補正量生成手段は、画像データから色補正量算出を行った後、雑音除去手段で雑音除去を行って色補正量を求めるように構成することもできる。
【００５１】
図１０は、この発明の実施形態２による色変換装置の構成の一例を示すブロック図である。第一の色データＲｉ、Ｇｉ、Ｂｉは色補正量生成手段１ｂおよび色補正量加算手段２へと入力される。色補正量生成手段１ｂは、色補正量演算手段４ｂおよび雑音除去手段３を備え、第一の色データＲｉ、Ｇｉ、Ｂｉは色補正量演算手段４ｂへと入力される。色補正量演算手段４ｂでは、第一の色データＲｉ、Ｇｉ、Ｂｉより色補正量Ｒ３、Ｇ３、Ｂ３を算出し、雑音除去手段３へと出力する。雑音除去手段３では、色補正量演算手段４ｂから出力される色補正量Ｒ３、Ｇ３、Ｂ３に含まれる雑音成分を除去し、雑音除去された色データを色補正量Ｒ１、Ｇ１、Ｂ１として出力する。色補正量加算手段２には、第一の色データＲｉ、Ｇｉ、Ｂｉおよび色補正量Ｒ１、Ｇ１、Ｂ１が入力され、これらを加算することにより第二の色データＲｏ、Ｇｏ、Ｂｏが算出される。
【００５２】
上記実施の形態１と同様に、第一の色データは、画像生成の際に生成された本来の色データ成分Ｒｓ、Ｇｓ、Ｂｓと、雑音成分Ｒｎ、Ｇｎ、Ｂｎより、Ｒｉ＝Ｒｓ＋Ｒｎ、Ｇｉ＝Ｇｓ＋Ｇｎ、Ｂｉ＝Ｂｓ＋Ｂｎと表すことができる。色補正量演算手段４ｂは、第一の色データＲｉ、Ｇｉ、Ｂｉが入力され、色補正量Ｒ３、Ｇ３、Ｂ３を出力する他は、上記実施の形態１における色補正量演算手段４と同様の構成とすることができる。すなわち、色補正量演算手段４ｂにおいては、上記式（２）と同様の下記式（５）に示す線形演算により色補正量Ｒ３、Ｇ３、Ｂ３を求める。即ち、式（５）の演算も、上記式（２）と同様、例えば少なくとも一つの色の明度及び彩度の少なくとも一つを高めるための補正量を求めるためのものである。
【００５３】
【数５】

【００５４】
上記式（５）における演算係数もまた、上記実施の形態１で用いたのと同一の下記式（３）に示す係数を用いることができる。下記式（３）に示す係数は、画像データの一つの特定の色である赤の明度、彩度を高める係数である。
【００５５】
【数６】

【００５６】
上記実施の形態１と同様に、図４にて示される第一の色データＲｉ、Ｇｉ、Ｂｉが入力される場合を考える。すなわち、画素位置２６および２７において、第一の色データはＲｉ＝１４６、Ｇｉ＝４０、Ｂｉ＝３８、およびＲｉ＝１７４、Ｇｉ＝４６、Ｂｉ＝６０である。
【００５７】
図１１は、色補正量演算手段４ｂから出力される色補正量Ｒ３、Ｇ３、Ｂ３を示す図である。図において、横軸は各データの存在する画素位置を表し、縦軸は各データの大きさを表す。画素位置２６および２７において、色補正量はＲ３＝４３、Ｇ３＝０、Ｂ３＝０、およびＲ３＝５２、Ｇ３＝０、Ｂ３＝０である。
【００５８】
雑音除去手段３は、上記実施の形態１に関し図２を参照して説明したのと同じ構成とすることができる。但し、色補正量演算手段４ｂから出力される色補正量Ｒ３、Ｇ３、Ｂ３を入力とし、雑音除去された色補正量Ｒ１、Ｇ１、Ｂ１を出力とする。
ここで、雑音除去手段３の重み付け加算手段６において、単純平均値が算出されるものとする。
【００５９】
図１２は、雑音除去手段３から出力される色補正量Ｒ１、Ｇ１、Ｂ１を示す図である。図において、横軸は各データの存在する画素位置を表し、縦軸は各データの大きさを表す。画素位置２６および２７において、色補正量はＲ１＝４７、Ｇ１＝０、Ｂ１＝０、およびＲ１＝４６、Ｇ１＝０、Ｂ１＝０である。
【００６０】
雑音除去手段３から出力された色補正量Ｒ１、Ｇ１、Ｂ１は、色補正量加算手段２にて第一の色データＲｉ、Ｇｉ、Ｂｉに加算され、第二の色データＲｏ、Ｇｏ、Ｂｏが算出される。
図１３は、色補正量加算手段２より出力される第二の色データＲｏ、Ｇｏ、Ｂｏの一例を表す図である。図において、横軸は各データの存在する画素位置を表し、縦軸は各データの大きさを表す。また、赤を表すデータＲｏに注目するため、Ｒｏの変化は特に太線にて示している。図中で矢印にて示しているのは、画素位置２６および２７のＲｏであり、それぞれ値は１９３、２２０である。
【００６１】
ここで、画素位置２６および２７における第二の色データＲｏの値の差は、２２０−１９３＝２７である。一方、画素位置２６および２７における第一の色データＲｉの値の差は、１７４−１４６＝２８である。画素位置２６および２７における第一の色データＲｉの値の差は、雑音成分の影響により生じたものである。
【００６２】
このように、本実施の形態における色変換装置または色変換方法においても、色データに含まれる雑音成分の影響を更に強調することなく、色データの彩度や明度を高める処理が行われている。
【００６３】
実施の形態３．
図１４は、この発明の実施形態３による色変換装置の構成の一例を示すブロック図である。第一の色データＲｉ、Ｇｉ、Ｂｉは色補正量生成手段１ｃおよび色補正量加算手段２へと入力される。色補正量生成手段１ｃは、無彩色成分算出手段９、有彩色成分算出手段１０、雑音除去手段３、色補正量演算手段４ｃを備え、第一の色データＲｉ、Ｇｉ、Ｂｉは、無彩色成分算出手段９及び有彩色成分算出手段１０へと入力される。無彩色成分算出手段９では、第一の色データＲｉ、Ｇｉ、Ｂｉの最小値を選択し、無彩色成分データαとして出力する。無彩色成分データαは、第一の色データのうちの無彩色の成分、すなわちグレイ成分を表すデータである。
【００６４】
図１５は、無彩色成分データαを表す図である。図において、縦軸はデータの大きさを表す。無彩色成分データαの大きさは、第一の色データＲｉ、Ｇｉ、Ｂｉが等量で存在する大きさであり、第一の色データに含まれるグレイ成分の大きさを表す。
【００６５】
無彩色成分データαは、有彩色成分算出手段１０及び色補正量演算手段４ｃへと入力される。有彩色成分算出手段１０では、第一の色データＲｉ、Ｇｉ、Ｂｉから無彩色成分データαを減じることにより、即ち、
Ｒ４＝Ｒｉ−α
Ｇ４＝Ｇｉ−α
Ｂ４＝Ｂｉ−α
により、有彩色成分データＲ４、Ｇ４、Ｂ４を算出する。有彩色成分データは、第一の色データから無彩色成分を除いたものであり、第一の色データに含まれる色相、彩度の情報に関わるデータである。また、有彩色成分データは、第一の色データに含まれるグレイ成分には関わらない。
【００６６】
有彩色成分データＲ４、Ｇ４、Ｂ４は雑音除去手段３へと入力され、雑音除去手段３は、雑音除去された有彩色成分データＲ５、Ｇ５、Ｂ５を算出する。雑音除去手段３は、上記実施の形態１に関し図２を参照して説明した構成（図２に示す回路を３つ備えた構成）とすればよく、この場合、第一の画像データＲｉ、Ｇｉ、Ｂｉの代りに、有彩色成分データＲ４、Ｇ４、Ｂ４を入力信号とし、雑音除去された有彩色色成分データＲ５、Ｇ５、Ｂ５を出力信号とする。雑音除去された有彩色成分データＲ５、Ｇ５、Ｂ５は色補正量演算手段４ｃへと入力され、色補正量演算手段４ｃは、色補正量Ｒ１、Ｇ１、Ｂ１を算出する。色補正量演算手段４ｃは、下記の式（６）に示す演算により色補正量Ｒ１、Ｇ１、Ｂ１を算出する。式（６）の演算も、上記式（２）と同様、例えば少なくとも一つの色の明度及び彩度の少なくとも一つを高めるための補正量を求めるためのものである。
【００６７】
【数７】

【００６８】
色補正量加算手段２には、第一の色データＲｉ、Ｇｉ、Ｂｉおよび色補正量Ｒ１、Ｇ１、Ｂ１が入力され、これらを加算することにより第二の色データＲｏ、Ｇｏ、Ｂｏが算出される。
【００６９】
上記実施の形態１と同様に、図４にて示される第一の色データＲｉ、Ｇｉ、Ｂｉが入力される場合を考える。すなわち、画素位置２６および２７において、第一の色データはＲｉ＝１４６、Ｇｉ＝４０、Ｂｉ＝３８、およびＲｉ＝１７４、Ｇｉ＝４６、Ｂｉ＝６０である。
【００７０】
図１６は、有彩色成分データＲ４、Ｇ４、Ｂ４の一例を表す図であり、図４にて示される第一の色データＲｉ、Ｇｉ、Ｂｉに対して求められる有彩色成分データＲ４、Ｇ４、Ｂ４を表す。図において、横軸は各データの存在する画素位置を表し、縦軸は各データの大きさを表す。画素位置２６および２７において、有彩色成分データはＲ４＝１０８、Ｇ４＝２、Ｂ４＝０、およびＲ４＝１２８、Ｇ４＝０、Ｂ４＝１４である。
【００７１】
ここで、雑音除去手段３の重み付け加算手段６において、単純平均値が算出されるものとする。この時、雑音除去された有彩色成分データＲ５、Ｇ５、Ｂ５は、図１７にて示される。
【００７２】
色補正量演算手段４ｃにおいては、上記式（６）に示す演算により色補正量Ｒ１、Ｇ１、Ｂ１を算出するが、係数として上記実施の形態１で用いたのと同一の下記式（３）に示す係数を用いることができる。下記式（３）に示す係数は、画像データの一つの特定の色である赤の明度、彩度を高める係数である。
【００７３】
【数８】

【００７４】
図１８は、上記式（３）に示す係数を用いた場合における第二の色データＲｏ、Ｇｏ、Ｂｏを表す図である。図において、横軸は各データの存在する画素位置を表し、縦軸は各データの大きさを表す。また、赤を表すデータＲｏに注目するため、Ｒｏの変化は特に太線にて示している。図中で矢印にて示しているのは、画素位置２６および２７のＲｏであり、それぞれ値は１８０、２０７である。ここで、画素位置２６および２７における第二の色データＲｏの値の差は、２０７−１８０＝２７である。一方、画素位置２６および２７における第一の色データＲｉの値の差は、１７４−１４６＝２８である。画素位置２６および２７における第一の色データＲｉの値の差は、雑音成分の影響により生じたものである。
【００７５】
また、画素位置２６において、第二の色データはＲｏ＝１８０、Ｇｏ＝４０、Ｂｏ＝３８となり、彩度は０．７９、明度は１８０となる。一方、画素位置２６において、第一の色データはＲｉ＝１４６、Ｇｉ＝４０、Ｂｉ＝３８であり、彩度は０．７４、明度は１４６となる。したがって、第二の色データにおいては、第一の色データと比較して彩度、明度ともに高くなっている。
さらに第一の色データとしてグレイのデータ、例えばＲｉ＝１００、Ｇｉ＝１００、Ｂｉ＝１００が入力された場合、第二の色データもＲｏ＝１００、Ｇｏ＝１００、Ｂｏ＝１００となり、色変換の影響を受けない。
【００７６】
以上より、本実施の形態における色変換装置または色変換方法において、色データに含まれる雑音成分の影響を更に強調することなく、色データの彩度や明度を高める処理が行われる。また、第一の色データを無彩色成分と有彩色成分に分離した後に色補正量を求めているので、無彩色（グレイ）の色再現に影響を与えることなく、色データの色相、彩度や明度の調整が可能となる。
また、有彩色成分に対してのみ雑音除去を行うので、白黒の画像データに対しては雑音除去の影響は生じない。例えば、パーソナルコンピュータにてカラーのビデオ画像を表示するとともに、白黒で表現された文書を表示した場合、ビデオ画像には雑音除去の効果が表れるが、白黒の文書には雑音除去の影響によるボケは生じない。
【００７７】
実施の形態４．
上記実施の形態３においては、第一の色データを無彩色成分と有彩色成分に分離し、有彩色成分に対してのみ雑音除去するように構成した。このようにする代りに、有彩色成分のみでなく、無彩色成分に対しても有彩色成分と個別に雑音除去するように構成することもできる。
【００７８】
図１９は、この発明の実施形態４による色変換装置の構成の一例を示すブロック図である。第一の色データＲｉ、Ｇｉ、Ｂｉは色補正量生成手段１ｄおよび色補正量加算手段２へと入力される。色補正量生成手段１ｄは、無彩色成分算出手段９、有彩色成分算出手段１０、有彩色雑音除去手段１１、無彩色雑音除去手段１２、色補正量演算手段４ｃを備え、第一の色データＲｉ、Ｇｉ、Ｂｉは、無彩色成分算出手段９及び有彩色成分算出手段１０へと入力される。無彩色成分算出手段９では、第一の色データＲｉ、Ｇｉ、Ｂｉの最小値を選択し、無彩色成分データαとして出力する。
【００７９】
無彩色成分データαは、有彩色成分算出手段１０及び無彩色雑音除去手段１２へと入力される。有彩色成分算出手段１０では、第一の色データＲｉ、Ｇｉ、Ｂｉから無彩色成分データαを減じることにより、有彩色成分データＲ４、Ｇ４、Ｂ４を算出する。有彩色成分データＲ４、Ｇ４、Ｂ４は有彩色雑音除去手段１１へと入力され、有彩色雑音除去手段１１は、雑音除去された有彩色成分データＲ５、Ｇ５、Ｂ５を算出する。また、無彩色雑音除去手段１２は、雑音除去された無彩色成分データを算出する。
【００８０】
有彩色雑音除去手段１１は、上記実施の形態１の雑音除去手段３と同じ構成（図２の構成を３つ備えたもの）とすることができる。この場合、有彩色雑音除去手段１１は、有彩色成分算出手段１０から出力される有彩色成分データＲ４、Ｇ４、Ｂ４を入力とし、雑音除去された有彩色成分データＲ５、Ｇ５、Ｂ５を出力する。無彩色雑音除去手段１２は、上記実施の形態１の説明に用いた図２に示す構成とすることができ、無彩色成分算出手段９から出力される無彩色成分としての最小値αを入力とし、雑音成分が除去された無彩色成分を出力する。
雑音除去された有彩色成分データＲ５、Ｇ５、Ｂ５及び雑音除去された無彩色成分データは、色補正量演算手段４ｃへと入力され、色補正量演算手段４ｃは、色補正量Ｒ１、Ｇ１、Ｂ１を算出する。色補正量加算手段２には、第一の色データＲｉ、Ｇｉ、Ｂｉおよび色補正量Ｒ１、Ｇ１、Ｂ１が入力され、これらを加算することにより第二の色データＲｏ、Ｇｏ、Ｂｏが算出される。
【００８１】
本実施の形態における色変換装置においては、有彩色成分データのみでなく、無彩色成分データに対しても有彩色成分データと個別に雑音除去するように構成した点が、上記実施の形態３における色変換装置と異なる。無彩色成分データは、有彩色成分データとの組み合わせで彩度の情報に関わるが、単体では明るさの情報のみを持つデータであり、人間の視覚特性に対しては、有彩色データとは異なる特性を持つことが考えられる。したがって、有彩色成分データと個別に無彩色成分データ雑音除去することにより、それぞれ視覚特性に合わせた雑音除去の影響、効果の大きさを選択することが可能となる。
【００８２】
【発明の効果】
この発明の色変換装置および方法は以上説明したように、複数の第一の色データからなる画素毎の画像情報である第一の画像データを色変換して、複数の第二の色データからなる第二の画像データを求める色変換装置において、上記第一の画像データに基づく色補正量算出及び雑音除去を行って色補正量を求める色補正量生成手段と、上記第一の画像データに上記色補正量を加算して、上記第二の画像データを求める加算手段とを備えるものであるので、色データの彩度や明度を高めるような処理を行った場合においても、色データに含まれる雑音成分の影響を更に強調することなく、また、雑音成分の影響の抑制による画像のボケの発生も抑えることが可能となる。
【図面の簡単な説明】
【図１】この発明の実施の形態１による色変換装置の構成の一例を示すブロック図である。
【図２】この発明の実施の形態１による色変換装置における雑音除去手段３の部分的な構成の一例を表すブロック図である。
【図３】本来の色データＲｓ、Ｇｓ、Ｂｓの一例を表す図である。
【図４】雑音成分が存在する場合の画像表示装置に入力される第一の色データの一例を表す図である。
【図５】雑音除去された色データの一例を表す図である。
【図６】この発明の実施の形態１における第二の色データの一例を表す図である。
【図７】雑音除去手段をＲ２＝Ｒｉ、Ｇ２＝Ｇｉ、Ｂ２＝Ｂｉとなるように構成した場合の第二の色データの一例を表す図である。
【図８】雑音除去手段を付加した従来の色変換装置の一例を表すブロック図である。
【図９】雑音除去手段を付加した従来の色変換装置により求められる第二の色データの一例を表す図である。
【図１０】この発明の実施の形態２による色変換装置の構成の一例を示すブロック図である。
【図１１】この発明の実施の形態２による色変換装置における色補正量演算手段から出力される色補正量の一例を示す図である。
【図１２】この発明の実施の形態２による色変換装置における雑音除去手段から出力される色補正量の一例を示す図である。
【図１３】この発明の実施の形態２による色変換装置における色補正量加算手段より出力される第二の色データの一例を表す図である。
【図１４】この発明の実施の形態３による色変換装置の構成の一例を示すブロック図である。
【図１５】無彩色成分データαを表す図である。
【図１６】この発明の実施の形態３による色変換装置における有彩色成分データの一例を表す図である。
【図１７】この発明の実施の形態３による色変換装置における雑音除去された有彩色成分データの一例を表す図である。
【図１８】この発明の実施の形態３による色変換装置における第二の色データの一例を表す図である。
【図１９】この発明の実施の形態４による色変換装置の構成の一例を示すブロック図である。
【符号の説明】
１、１ｂ、１ｃ、１ｄ 色補正量生成手段、 ２ 色補正量加算手段、 ３ 雑音除去手段、 ４、４ｂ、４ｃ 色補正量演算手段、 ５ データシフト手段、 ５ａ〜５ｈ データ格納部、 ６ 重み付け加算手段、 ７ 色変換手段、１０ 有彩色成分算出手段、 １１ 有彩色雑音除去手段、 １２ 無彩色雑音除去手段。[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention relates to data processing for use in printers, video printers, scanners, and other full-color printing-related devices such as printing-related devices, or display devices such as monitors. The present invention relates to a color conversion device and method for performing color conversion processing in combination.
[0002]
[Prior art]
An example of a conventional color conversion device and method is described in Patent Document 1 below. In this conventional color conversion apparatus and method, means for calculating the minimum value α and the maximum value β of the first color data Ri, Gi, Bi constituting the first image data, and the first color data Ri, Means for calculating hue data using Gi, Bi, the minimum value α and the maximum value β, means for generating first comparison data between hue data using hue data, and use of the first comparison data Means for generating second comparison data between hue data, calculation means for performing calculations based on hue data, means for generating predetermined matrix coefficients, first and second comparison data, and calculation means Means for performing a predetermined matrix operation using the output from the hue data r, g, and b and the matrix coefficient, and outputting the calculated image data; and adding the minimum value α to the output image data. Color-converted second image Means for combining and outputting image data.
(See Patent Document 1)
[0003]
[Patent Document 1]
JP 2000-287074 A
[0004]
[Problems to be solved by the invention]
In a conventional color conversion device or color conversion method, in the case where noise components are included in the color data, if processing is performed to increase the saturation or brightness of the color data, the effect of the noise components will be further emphasized, There is a problem that the image becomes very difficult to see. When the color data is input to the color conversion means via the noise removal means in order to reduce the influence of the noise component included in the color data, the noise component is removed, but the change in the original data is reduced. There has been a problem that the change is gradual and the image is blurred.
[0005]
Hereinafter, the above points will be described in more detail. The color conversion is performed to obtain a desired color reproduction in the image display device, and the desired color reproduction includes “faithful color reproduction” and “preferred color reproduction”. The term "faithful color reproduction" refers to color reproduction faithful to a real color. As a method for realizing the color reproduction, color reproduction using a standard such as NTSC or sRGB or a standard color space can be considered. On the other hand, “preferred color reproduction” is color reproduction that humans feel more preferable in consideration of human visual characteristics and memory colors, and does not always match “faithful color reproduction”. In color reproduction at the time of displaying a moving image such as a television image, “preferred color reproduction” is often performed. Human memory colors tend to be stored as colors with higher saturation and lightness, such as sky colors and grass greens, that are more vivid than actual colors. Therefore, in realizing “preferred color reproduction”, color conversion processing for increasing color saturation and lightness is often performed on input color data. Further, even in faithful color reproduction, there are many cases where color conversion processing that increases the color saturation and lightness is performed on input color data. This is because the color reproduction range of the used image display means is narrow.
[0006]
On the other hand, the first color data input to the image display device or the like does not always match the original color data generated on the color data generation side such as a camera. This is because the process of transmitting color data is affected by various noises. Consider a case where original color data generated by a camera is transmitted via a transmission path and input to an image display device. The original color data output from the camera is Rs, Gs, Bs, and is the color data representing red, green, and blue, respectively. The first color data input to the image display device is defined as Ri, Gi, Bi. Rs = Ri, Gs = Gi, Bs = Bi should be obtained if the transmission and reception procedures at the time of transmission are correctly performed without the influence of noise in the transmission path. However, it is conceivable that the transmission path is actually affected by noise. Assuming that noise components that affect noise on each color data are Rn, Gn, and Bn, the first color data Ri, Gi, and Bi input to the image display device are Ri = Rs + Rn, Gi = Gs + Gn, and Bi = It can be expressed as Bs + Bn.
[0007]
When performing color conversion processing on the first color data Ri, Gi, Bi input to the image display device so as to increase color saturation or lightness in order to realize “preferred color reproduction” As a result, the saturation and brightness of the original color data component are increased, and the saturation and brightness of the noise component are also increased.
[0008]
When the noise component included in the pixel data is white noise, the noise component has a component in a wide frequency range from a low frequency to a high frequency. It is also conceivable that the noise component included in the pixel data has a strong component at a specific frequency. For example, in the case of a noise component due to the influence of a carrier used during transmission, the noise component has a strong component near the frequency of the carrier.
[0009]
When the noise component has a strong component at a specific frequency, the noise removing unit is configured as a filter that removes (attenuates) the frequency component. On the other hand, when the noise component included in the image data is white noise, it is effective to configure a filter that removes (attenuates) a frequency component that is particularly visually noticeable among the noise components. Strictly visible frequency components are strictly specified taking into account the pixel spacing and viewing distance of the display device, but in the current image display device, the pixel frequency of the image data among the noise components is In many cases, high frequency components close to are noticeable. This is considered that, in the current pixel interval of the image display device, if the data of the adjacent pixels frequently and unrelatedly change due to the influence of the noise component, the noise is perceived by humans as a large noise. Here, the pixel frequency means that the image data of each successive pixel is, for example, 0, 1, 0, 1, 0,. . . １ ／ of the clock frequency of the image data.
[0010]
As described above, the configuration of the noise removing unit is determined by the characteristics of the noise component. Hereinafter, a case where a noise component of a high frequency component close to the pixel frequency of the image data is removed will be described as an example. In this case, the high-frequency component in the input data is cut off or largely attenuated, the low-pass filter transmitting the low-frequency component constitutes a noise removing unit, and the first color data is sent to the color conversion unit via the noise removing unit. By inputting, it becomes possible to reduce the emphasis of the noise component. However, in this case, in addition to the noise component included in the first color data, the high-frequency component of the original color component is also cut off, causing a problem that image blur occurs. For example, when the noise removing unit is configured as a filter that removes a noise component having a frequency equal to or more than 1/8 of the pixel frequency of the image data, the original color component is also equal to or greater than 1/8 of the pixel frequency of the image data. The frequency is removed, and the image is blurred.
Generally, of the original color components, the frequency components removed by the noise removing means 3 are removed.
[0011]
An object of the present invention is to suppress the influence of a noise component without further emphasizing the effect of a noise component included in the color data even when performing a process of increasing the saturation or brightness of the color data. It is an object of the present invention to provide a color conversion device and method capable of suppressing occurrence of image blur.
[0012]
[Means for Solving the Problems]
A color conversion device according to the present invention performs color conversion on first image data that is image information for each pixel including a plurality of first color data, and generates second image data including a plurality of second color data. A color conversion device for calculating a color correction amount based on the first image data and removing noise to obtain a color correction amount, and adding the color correction amount to the first image data. And adding means for obtaining the second image data.
[0013]
BEST MODE FOR CARRYING OUT THE INVENTION
Hereinafter, the present invention will be specifically described with reference to the drawings showing the embodiments.
Embodiment 1 FIG.
FIG. 1 is a block diagram showing an example of a configuration of a color conversion device according to an embodiment of the present invention.
[0014]
The first color data Ri, Gi, Bi are input to the color correction amount generating means 1 and the color correction amount adding means 2. The color correction amount generator 1 includes a noise remover 3 and a color correction amount calculator 4, and the first color data Ri, Gi, Bi are input to the noise remover 3. The noise removing unit 3 removes a noise component in the first color data and outputs noise-free color data R2, G2, B2. The color data R2, G2, and B2 from which noise has been removed are input to the color correction amount calculation unit 4, and the color correction amount calculation unit 4 calculates the color correction amounts R1, G1, and B1. The first color data Ri, Gi, Bi and the color correction amounts R1, G1, B1 are input to the color correction amount adding means 2, and the second color data Ro, Go, Bo are calculated by adding these. Is done.
Note that the color correction amount does not always have a positive value and may have a negative value.
[0015]
Since the first color data is affected by various noises in a transmission process, the first color data is composed of an original color data component generated at the time of image generation and a noise component generated by the influence of the subsequent noise. . The original color data is Rs, Gs, and Bs, and the color data represents red, green, and blue, respectively. The first color data is defined as Ri, Gi, Bi. If there is no influence of noise, Rs = Ri, Gs = Gi, and Bs = Bi. However, it is actually affected by noise. Assuming that noise components that affect noise on each color data are Rn, Gn, and Bn, the first color data Ri, Gi, and Bi input to the image display device are Ri = Rs + Rn, Gi = Gs + Gn, and Bi = It can be expressed as Bs + Bn. That is, the first color data Ri, Gi, Bi input to the image display device is represented by the sum of the original color data components Rs, Gs, Bs and the noise components Rn, Gn, Bn. Will be.
[0016]
As described in the related art, the configuration of the noise removing unit 3 is determined according to the characteristics of the noise component. Hereinafter, a case where a noise component of a high frequency component close to the pixel frequency of the image data is removed will be described as an example. In this case, the noise removing unit 3 can be configured by a low-pass filter that blocks or largely attenuates high-frequency components in the input data and transmits low-frequency components.
More specifically, a low-pass filter that cuts off or largely attenuates a frequency component of about 1 / 4.5 or more of the pixel frequency, that is, 1/9 or more of the clock frequency of the pixel data is used.
As a low-pass filter having a simple configuration, a configuration in which a simple average value of image data of a plurality of continuous pixels is calculated can be considered. At this time, the filter characteristic is determined by the number of pixels used for calculating the simple average value.
[0017]
FIG. 2 is a block diagram illustrating an example of a partial configuration of the noise removing unit 3, that is, a configuration of a portion that calculates R2 from the color data Ri. A portion for calculating G2 from Gi and a portion for calculating B2 from Bi can have the same configuration. As shown, the noise removing unit 3 includes a data shift unit 5 including a plurality of data storage units 5a to 5h, and a weighting and adding unit 6.
The first color data Ri input to the noise removing unit 3 is input to the data storage unit 5a. The data storage units 5a to 5h are connected in cascade with each other. Each time the first color data is input, the data in the data storage unit 5a is sent to the data storage unit 5b, and the data in the data storage unit 5b is sent to the data storage unit. Data is simultaneously shifted to 5c.
The data held in the data storages 5a to 5h is input to the weighting and adding means 6.
[0018]
The weighting and adding means 6 performs weighting and addition on the data from the data storage units 5a to 5h and outputs the data as noise-free color data R2. If the weighting performed by the weighting and adding means 6 is made equal, that is, the weighting coefficients are set to the same value, and a simple average value is calculated. Therefore, the noise-removed color data R2 calculated by the noise removing unit 3 is represented by the following equation (1).
[0019]
(Equation 1)

[0020]
In equation (1), Ri [n] represents the nth input first color data, and function f represents weighted addition.
When the first data is input to the noise elimination means, the same data as the first data is all simultaneously input to the data storages 5a to 5h. After the last data is input to the noise removing unit, the same data as the last data input to the noise removing unit is continuously input to the data storage unit 5a.
[0021]
FIG. 3 is a diagram illustrating an example of the original color data Rs, Gs, Bs. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. Further, in order to pay attention to the data Rs representing red, a change in Rs is particularly indicated by a thick line. Rs = 80, Gs = 48, Bs = 48 at pixel positions 0 to 16 and 43 to 63, and Rs = 160, Gs = 48, Bs = 48 at pixel positions 17 to 42. This means that there is a red color having higher saturation and lightness at pixel positions 17 to 42 as compared to both sides thereof. Here, the saturation can be represented by dividing the difference between the maximum value and the minimum value of the color signal by the maximum value, and the lightness can be represented by the maximum value of the color signal. According to this, the saturation at pixel positions 0 to 16 and 43 to 63 is represented by 0.4 and the lightness is 80, and the saturation at pixel positions 17 to 42 is represented by 0.7 and the lightness is 160.
[0022]
FIG. 4 shows color data obtained when noises Rn, Gn, and Bn are added to the original color data Rs, Gs, and Bs, that is, first color data Ri that is input to the image display device when a noise component exists. , Gi, and Bi. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. Further, in order to pay attention to the data Ri representing red, a change in Ri is particularly indicated by a thick line. In the figure, the arrows at the pixel positions 26 and 27 are indicated by arrows, and the values are 146 and 174, respectively. Ri at the pixel positions 26 and 27 should originally have the same value, and the difference between the values is due to the influence of the noise component. At this time, Gi is 40, 46 and Bi is 38, 60 at the pixel positions 26 and 27.
[0023]
Here, it is assumed that the weighting and adding means 6 calculates a simple average value.
With the first color data Ri, Gi, Bi shown in FIG. 4 as input, the noise-free color data R2, G2, B2 obtained by the noise removing means 3 described with reference to FIG. 2 are shown in FIG. Show. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. As shown in FIG. 5, in the color data R2, G2, and B2 from which noise has been removed, the change in the data change portion is also gentle, but the noise component has also been removed.
[0024]
The color correction amount calculating means 4 calculates color correction amounts R1, G1, B1 using the color data R2, G2, B2 from which noise has been removed, and adds the calculated color correction amounts R1, G1, B1 to the color correction amount adding means. Output to 2. The first color data Ri, Gi, Bi are also input to the color correction amount adding means 2, and the color correction amounts are added to the color correction amounts R1, G1, B1 in the color correction amount adding means 2, and the second color data Ro, Go. , Bo are calculated.
[0025]
The color correction amount calculating means 4 performs a calculation for obtaining a correction amount for increasing at least one of the brightness and the saturation of at least one color, for example, a linear calculation represented by the following equation (2). To obtain the color correction amounts R1, G1, and B1.
[0026]
(Equation 2)

[0027]
The coefficient shown in the following equation (3) is an example of a coefficient used for the equation (2).
[0028]
[Equation 3]

[0029]
The coefficient shown in the above equation (3) is a coefficient that increases the brightness and saturation of one specific color, red, of the image data. For simplicity, it is assumed that the values of the color data R2, G2, and B2 from which noise has been removed have the same values as the first color data Ri, Gi, and Bi. For example, if the color data from which noise has been removed is R2 = 80, Consider the case where G2 = 48 and B2 = 48. At this time, R1 = 24, G1 = 0, B1 = 0, and the second color data is Ro = R2 + R1 = 104, Go = G2 + G1 = 48, Bo = B2 + B1 = 48. When the noise-free color data is R2 = 160, G2 = 48, B2 = 48, R1 = 48, G1 = 0, B1 = 0, and the second color data is Ro = R2 + R1 = 208, Go = G2 + G1 = 48 and Bo = B2 + B1 = 48.
For the color data from which noise has been removed, the former has a saturation of 0.4 and the lightness of 80, and the latter has a saturation of 0.7 and a lightness of 160. On the other hand, for the second color data, the former has a saturation of 0.54 and a brightness of 104, and the latter has a saturation of 0.77 and a brightness of 208. Therefore, by performing the color conversion, the brightness and the saturation of the image data are both increased.
[0030]
FIG. 6 is a diagram showing an example of the second color data Ro, Go, Bo. The first color data Ri, Gi, Bi shown in FIG. 4 is input and the coefficient shown in the equation (3) is used. This is the second color data calculated when there is a color. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. Further, in order to pay attention to the data Ro representing red, a change in Ro is particularly indicated by a thick line. In the figure, the arrows at the pixel positions 26 and 27 are indicated by arrows, and their values are 193 and 220, respectively.
[0031]
Here, the difference between the values of the second color data Ro at the pixel positions 26 and 27 is 220-193 = 27. On the other hand, the difference between the values of the first color data Ri at the pixel positions 26 and 27 is 174-146 = 28. The difference between the values of the first color data Ri at the pixel positions 26 and 27 is caused by the influence of the noise component.
[0032]
Therefore, according to the color conversion device or the color conversion method in the present embodiment, a process for increasing the saturation and brightness of the color data is performed without further emphasizing the influence of the noise component included in the color data. I have. When the influence of the noise component in the image data is emphasized, the image displayed on the image display device becomes very difficult to see, but in the color conversion device or the color conversion method according to the present embodiment, the second color The magnitude of the noise component in the data is equal to that of the first color component, thereby avoiding an increase in the influence of the noise component.
[0033]
In the above example, the color correction amount calculating means 4 performs a calculation to increase the brightness and saturation of one color, red. However, the color correction amount calculating means 4 increases the brightness and saturation of two or more colors. The color correction amount calculation may be performed. Also, the present invention can be applied to a case where color correction is performed not to increase brightness or saturation but to decrease it. When the brightness or the saturation is reduced, the color correction amount becomes a negative value.
[0034]
The feature of the color conversion device or the color conversion method according to the present embodiment is that the color correction amount generating means 1 includes a noise removing means 3, and the color correction amount generating means 1 performs color correction amount calculation and noise removal to perform color correction. The correction amount is obtained and supplied to the color correction amount adding means 2. The color correction amount adding means 2 calculates the color correction amount after the noise removal as described above and the first image data without the noise removal as described above. To obtain the second image data. Hereinafter, the effect will be described in more detail by comparison with a case where the color correction amount generating unit 1 does not include the noise removing unit 3.
[0035]
In the color conversion apparatus shown in FIG. 1, if the noise removing means 3 is configured such that R2 = Ri, G2 = Gi, and B2 = Bi, it is equivalent to the case where the noise removing means 3 is not provided.
[0036]
Similar to the above, a case is considered where the first color data Ri, Gi, Bi shown in FIG. 4 in which a noise component is added to the original color data Rs, Gs, Bs shown in FIG. 3 is input. . That is, at the pixel positions 26 and 27, the original color data are both Rs = 160, Gs = 48, and Bs = 48. At the pixel positions 26 and 27, the first color data is Ri = 146, Gi = 40, Bi = 38, and Ri = 174, Gi = 46, Bi = 60.
[0037]
Since the noise removing unit 3 is not provided, R2 = Ri, G2 = Gi, and B2 = Bi are input to the color correction calculating unit 4. The color correction amount calculation means 4 calculates the color correction amounts R1, G1, and B1 by the matrix calculation shown in the above equation (2) using the coefficient shown in the above equation (3). The color correction amount adding means 2 calculates the second color data Ro, Go, Bo by adding the first color data Ri, Gi, Bi and the color correction amounts R1, G1, B1.
[0038]
FIG. 7 is a diagram illustrating the second color data Ro, Go, and Bo when the noise removing unit 3 is not provided and R2 = Ri, G2 = Gi, and B2 = Bi. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. Compared to FIG. 6 showing the second color data Ro, Go, Bo obtained when the noise removing means 3 is provided, it can be seen that the noise component is also enhanced by the color conversion processing. In the figure, the arrows indicate the second color data Ro of the pixel positions 26 and 27, the values of which are 189 and 226, respectively. On the other hand, the values of the first color data Ri at the pixel positions 26 and 27 are 146 and 174, respectively. The difference between the values of the first color data Ri at the pixel positions 26 and 27 is 174-146 = 28, and this value indicates the magnitude of the noise component included in the first color data at the present pixel position. On the other hand, the difference between the values of the second color data Ro at the pixel positions 26 and 27 is 226-189 = 37, which is a large value. This indicates that the noise component included in the first color data is emphasized. When the influence of the noise components Rn, Gn, Bn in the image data is emphasized, the image displayed on the image display device becomes very difficult to see.
[0039]
Here, as described above, the difference between the values of the second color data Ro at the pixel positions 26 and 27 obtained by the color conversion apparatus according to the present embodiment including the noise removing unit 3 is 27, and the noise component is Not emphasized.
[0040]
As a method of reducing the influence of the noise component included in the first color data, it is conceivable to input the first color data to the color conversion means via the noise removing means. FIG. 8 is a block diagram illustrating an example of a conventional color conversion apparatus to which a noise removing unit is added, and is configured to input first color data to the color converting unit 7 via the noise removing unit 3. . The first color data Ri, Gi, Bi are input to the noise removing means 3. The noise removing unit 3 removes a noise component in the first color data and outputs noise-free color data R2, G2, B2. The color data R2, G2, and B2 from which noise has been removed are input to the color conversion means 7. The color conversion unit 7 includes a color correction amount calculation unit 4 and a color correction amount addition unit 2. The color correction amount calculating means 4 calculates the noise from the color data R2, G2, and B2 from which noise has been removed by the matrix calculation shown in the above equation (2) using the coefficient shown in the above equation (3), as in the above case. The color correction amounts R1, G1, and B1 are calculated. The color correction amount adding means 2 calculates the second color data Ro, Go, Bo by adding the color data R2, G2, B2 from which noise has been removed and the color correction amounts R1, G1, B1, respectively. Therefore, in the color conversion device shown in FIG. 8, the second color data Ro, Go, Bo are represented by the following equation (4).
[0041]
(Equation 4)

[0042]
The noise removing means 3 can have the configuration described with reference to FIG. Here, it is assumed that the weighted addition unit 6 of the noise removal unit 3 calculates a simple average value.
[0043]
FIG. 9 shows the second color data Ro, Go, Bo obtained by the color conversion device shown in FIG. At this time, it is assumed that first color data obtained by adding a noise component to the original color data shown in FIG. 4 is input. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. As shown in FIG. 9, although the noise components in the second color data Ro, Go, and Bo have been removed, the change of the changed portion of the data is gentle as indicated by the broken line in the figure. If the change of the change part of the data becomes gentle, it is recognized as blurred image. For example, blurring occurs in characters and boundaries in an image.
[0044]
In particular, when color data that is not affected by noise, that is, color data that does not include a noise component is input, blurring in the image is particularly problematic. In other words, for color data having no noise component, only the drawback of image blur occurs.
[0045]
On the other hand, in the second color data Ro, Go, Bo (FIG. 6) obtained by the color conversion apparatus (FIG. 1) of the present embodiment, no image blur occurs. That is, the change in data from pixel position 16 to 17 and the change in data from pixel position 42 to 43 are not gradual. This is because the first image data Ri, Gi, Bi are supplied to the color correction amount adding means 2 without performing noise removal.
[0046]
As described above, according to the color conversion device or the color conversion method of the present embodiment, even when a process of increasing the saturation or brightness of the color data is performed, the influence of the noise component included in the color data is further emphasized. It is also possible to suppress the occurrence of image blur due to the suppression of the influence of noise components.
[0047]
In recent years, the number of cases in which television images and video images recorded on DVDs are viewed by a personal computer has been increasing. In general, video images and television images often contain large noise components. On the other hand, a graphic screen generated by a personal computer has a very small noise component and many displays such as character information that require a high degree of definition, so that blurring of an image is a serious problem. On the other hand, an image display device generally has no means for determining whether input image data represents a video image or a television image or character information. Therefore, when watching a video image or a television image on a personal computer, it is possible to suppress the enhancement of noise components in the video image or the television image, and also to suppress blurring of an image on a graphic screen generated by the personal computer. The color conversion device or color conversion method of the present invention is very useful.
[0048]
In the above description, the configuration of the color correction amount calculation means is a matrix calculation method, but the same applies to a table conversion method. The table conversion method has an advantage that various conversion characteristics can be realized because the converted data can be specified for each of the input color data sets. That is, the effect of noise is not particularly improved as compared with the matrix operation method. Therefore, the effect that the color conversion processing can be performed without further emphasizing the influence of the noise component included in the color data by providing the color correction amount generating means with the noise removing means is the same as the matrix operation method.
[0049]
Further, in the present embodiment, the case where the processing is realized by hardware has been described, but the same processing can be realized by software.
[0050]
Embodiment 2 FIG.
In the first embodiment, the color correction amount generating means is configured to calculate the color correction amount using the data from which noise has been removed by the noise removing means. Instead of this, the color correction amount generating means may be configured to calculate the color correction amount from the image data and then remove the noise by the noise removing means to obtain the color correction amount.
[0051]
FIG. 10 is a block diagram illustrating an example of a configuration of a color conversion device according to Embodiment 2 of the present invention. The first color data Ri, Gi, Bi are input to the color correction amount generating means 1b and the color correction amount adding means 2. The color correction amount generator 1b includes a color correction amount calculator 4b and a noise remover 3, and the first color data Ri, Gi, Bi are input to the color correction amount calculator 4b. The color correction amount calculating means 4b calculates the color correction amounts R3, G3, B3 from the first color data Ri, Gi, Bi and outputs them to the noise removing means 3. The noise removing unit 3 removes noise components included in the color correction amounts R3, G3, and B3 output from the color correction amount calculating unit 4b, and outputs the noise-removed color data as the color correction amounts R1, G1, and B1. I do. The first color data Ri, Gi, Bi and the color correction amounts R1, G1, B1 are input to the color correction amount adding means 2, and the second color data Ro, Go, Bo are calculated by adding these. Is done.
[0052]
As in the first embodiment, the first color data is Ri = Rs + Rn, Gi from the original color data components Rs, Gs, Bs generated at the time of image generation and the noise components Rn, Gn, Bn. = Gs + Gn, Bi = Bs + Bn. The color correction amount calculating means 4b is the same as the color correction amount calculating means 4 in the first embodiment except that the first color data Ri, Gi, Bi are input and the color correction amounts R3, G3, B3 are output. Configuration. That is, the color correction amount calculation means 4b obtains the color correction amounts R3, G3, and B3 by linear calculation represented by the following equation (5) similar to the above equation (2). That is, similarly to the above equation (2), the calculation of the equation (5) is for obtaining a correction amount for increasing, for example, at least one of brightness and saturation of at least one color.
[0053]
(Equation 5)

[0054]
As the operation coefficient in the above equation (5), the same coefficient shown in the following equation (3) used in the first embodiment can be used. The coefficient shown in the following equation (3) is a coefficient for increasing the brightness and saturation of one specific color, red, of the image data.
[0055]
(Equation 6)

[0056]
As in the first embodiment, a case is considered where the first color data Ri, Gi, Bi shown in FIG. 4 is input. That is, at pixel positions 26 and 27, the first color data is Ri = 146, Gi = 40, Bi = 38, and Ri = 174, Gi = 46, Bi = 60.
[0057]
FIG. 11 is a diagram showing the color correction amounts R3, G3, and B3 output from the color correction amount calculation means 4b. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. At the pixel positions 26 and 27, the color correction amounts are R3 = 43, G3 = 0, B3 = 0, and R3 = 52, G3 = 0, B3 = 0.
[0058]
The noise removing unit 3 may have the same configuration as that described in the first embodiment with reference to FIG. However, the color correction amounts R3, G3, and B3 output from the color correction amount calculating means 4b are input, and the color correction amounts R1, G1, and B1 from which noise has been removed are output.
Here, it is assumed that the weighted addition unit 6 of the noise removal unit 3 calculates a simple average value.
[0059]
FIG. 12 is a diagram illustrating the color correction amounts R1, G1, and B1 output from the noise removing unit 3. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. At the pixel positions 26 and 27, the color correction amounts are R1 = 47, G1 = 0, B1 = 0, and R1 = 46, G1 = 0, B1 = 0.
[0060]
The color correction amounts R1, G1, B1 output from the noise removing unit 3 are added to the first color data Ri, Gi, Bi by the color correction amount adding unit 2, and the second color data Ro, Go, Bo are added. Is calculated.
FIG. 13 is a diagram showing an example of the second color data Ro, Go, Bo output from the color correction amount adding means 2. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. Further, in order to pay attention to the data Ro representing red, a change in Ro is particularly indicated by a thick line. In the figure, the arrows at the pixel positions 26 and 27 are indicated by arrows, and their values are 193 and 220, respectively.
[0061]
Here, the difference between the values of the second color data Ro at the pixel positions 26 and 27 is 220-193 = 27. On the other hand, the difference between the values of the first color data Ri at the pixel positions 26 and 27 is 174-146 = 28. The difference between the values of the first color data Ri at the pixel positions 26 and 27 is caused by the influence of the noise component.
[0062]
As described above, even in the color conversion device or the color conversion method according to the present embodiment, the process of increasing the saturation and brightness of the color data is performed without further emphasizing the influence of the noise component included in the color data. .
[0063]
Embodiment 3 FIG.
FIG. 14 is a block diagram illustrating an example of a configuration of a color conversion device according to Embodiment 3 of the present invention. The first color data Ri, Gi, Bi are input to the color correction amount generating means 1c and the color correction amount adding means 2. The color correction amount generator 1c includes an achromatic component calculator 9, a chromatic component calculator 10, a noise remover 3, and a color correction calculator 4c. The first color data Ri, Gi, and Bi are achromatic. It is input to the component calculation means 9 and the chromatic color component calculation means 10. The achromatic component calculation means 9 selects the minimum value of the first color data Ri, Gi, Bi and outputs it as achromatic component data α. The achromatic component data α is data representing an achromatic component of the first color data, that is, a gray component.
[0064]
FIG. 15 is a diagram illustrating achromatic component data α. In the figure, the vertical axis represents the size of the data. The size of the achromatic color component data α is a size in which the first color data Ri, Gi, Bi are present in equal amounts, and represents the size of the gray component included in the first color data.
[0065]
The achromatic color component data α is input to the chromatic color component calculation means 10 and the color correction amount calculation means 4c. The chromatic component calculating means 10 subtracts the achromatic component data α from the first color data Ri, Gi, Bi, ie,
R4 = Ri-α
G4 = Gi-α
B4 = Bi-α
To calculate the chromatic component data R4, G4, B4. The chromatic component data is obtained by removing the achromatic component from the first color data, and is data relating to hue and saturation information included in the first color data. The chromatic component data does not relate to the gray component included in the first color data.
[0066]
The chromatic component data R4, G4, and B4 are input to the noise removing unit 3, and the noise removing unit 3 calculates the chromatic component data R5, G5, and B5 from which noise has been removed. The noise removing unit 3 may have the configuration described with reference to FIG. 2 in the first embodiment (a configuration including three circuits shown in FIG. 2). In this case, the first image data Ri, Gi , And Bi, chromatic color component data R4, G4, and B4 are used as input signals, and chromatic color component data R5, G5, and B5 from which noise has been removed are used as output signals. The chromatic color component data R5, G5, and B5 from which noise has been removed are input to the color correction amount calculator 4c, and the color correction amount calculator 4c calculates the color correction amounts R1, G1, and B1. The color correction amount calculation means 4c calculates the color correction amounts R1, G1, and B1 by the calculation shown in the following equation (6). Similarly to the above equation (2), the calculation of the equation (6) is for obtaining a correction amount for increasing, for example, at least one of brightness and saturation of at least one color.
[0067]
(Equation 7)

[0068]
The first color data Ri, Gi, Bi and the color correction amounts R1, G1, B1 are input to the color correction amount adding means 2, and the second color data Ro, Go, Bo are calculated by adding these. Is done.
[0069]
As in the first embodiment, a case is considered where the first color data Ri, Gi, Bi shown in FIG. 4 is input. That is, at pixel positions 26 and 27, the first color data is Ri = 146, Gi = 40, Bi = 38, and Ri = 174, Gi = 46, Bi = 60.
[0070]
FIG. 16 is a diagram illustrating an example of the chromatic component data R4, G4, B4, and the chromatic component data R4, G4, and sought for the first color data Ri, Gi, Bi shown in FIG. Represents B4. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. At pixel positions 26 and 27, the chromatic component data is R4 = 108, G4 = 2, B4 = 0, and R4 = 128, G4 = 0, B4 = 14.
[0071]
Here, it is assumed that the weighted addition unit 6 of the noise removal unit 3 calculates a simple average value. At this time, the chromatic component data R5, G5, and B5 from which noise has been removed are shown in FIG.
[0072]
The color correction amount calculation means 4c calculates the color correction amounts R1, G1, and B1 by the calculation shown in the above equation (6). The same equation (3) as that used in the first embodiment is used as a coefficient. Can be used. The coefficient shown in the following equation (3) is a coefficient for increasing the brightness and saturation of one specific color, red, of the image data.
[0073]
(Equation 8)

[0074]
FIG. 18 is a diagram illustrating the second color data Ro, Go, and Bo when the coefficient represented by the above equation (3) is used. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. Further, in order to pay attention to the data Ro representing red, a change in Ro is particularly indicated by a thick line. In the figure, arrows indicated by arrows indicate Ro at pixel positions 26 and 27, and the values are 180 and 207, respectively. Here, the difference between the values of the second color data Ro at the pixel positions 26 and 27 is 207−180 = 27. On the other hand, the difference between the values of the first color data Ri at the pixel positions 26 and 27 is 174-146 = 28. The difference between the values of the first color data Ri at the pixel positions 26 and 27 is caused by the influence of the noise component.
[0075]
At the pixel position 26, the second color data is Ro = 180, Go = 40, Bo = 38, the saturation is 0.79, and the lightness is 180. On the other hand, at the pixel position 26, the first color data is Ri = 146, Gi = 40, Bi = 38, the saturation is 0.74, and the brightness is 146. Therefore, in the second color data, both the saturation and brightness are higher than in the first color data.
Further, when gray data, for example, Ri = 100, Gi = 100, Bi = 100 is input as the first color data, the second color data also becomes Ro = 100, Go = 100, Bo = 100, and the color conversion is performed. Not affected by
[0076]
As described above, in the color conversion device or the color conversion method according to the present embodiment, the process of increasing the saturation and brightness of the color data is performed without further emphasizing the influence of the noise component included in the color data. Also, since the color correction amount is obtained after separating the first color data into an achromatic component and a chromatic component, the hue and saturation of the color data can be obtained without affecting the color reproduction of achromatic (gray). And brightness can be adjusted.
Also, since noise removal is performed only on the chromatic color components, the noise removal does not affect black-and-white image data. For example, when displaying a color video image on a personal computer and displaying a document expressed in black and white, the noise reduction effect appears in the video image, but the blur due to the noise reduction effect is displayed in the black and white document. Does not occur.
[0077]
Embodiment 4 FIG.
In the third embodiment, the first color data is separated into an achromatic color component and a chromatic color component, and noise is removed only from the chromatic color component. Instead of such a configuration, it is also possible to configure so as to remove noise not only for the chromatic color component but also for the achromatic color component separately from the chromatic color component.
[0078]
FIG. 19 is a block diagram illustrating an example of a configuration of a color conversion device according to Embodiment 4 of the present invention. The first color data Ri, Gi, Bi are inputted to the color correction amount generating means 1d and the color correction amount adding means 2. The color correction amount generator 1d includes an achromatic component calculator 9, a chromatic component calculator 10, a chromatic noise remover 11, an achromatic noise remover 12, and a color correction amount calculator 4c. Ri, Gi, and Bi are input to the achromatic color component calculation means 9 and the chromatic color component calculation means 10. The achromatic component calculation means 9 selects the minimum value of the first color data Ri, Gi, Bi and outputs it as achromatic component data α.
[0079]
The achromatic component data α is input to the chromatic component calculating unit 10 and the achromatic noise removing unit 12. The chromatic component calculating means 10 calculates the chromatic component data R4, G4, B4 by subtracting the achromatic component data α from the first color data Ri, Gi, Bi. The chromatic component data R4, G4, and B4 are input to the chromatic noise removing unit 11, and the chromatic noise removing unit 11 calculates the chromatic component data R5, G5, and B5 from which noise has been removed. The achromatic noise removing unit 12 calculates achromatic component data from which noise has been removed.
[0080]
The chromatic noise removing unit 11 can have the same configuration as that of the noise removing unit 3 of the first embodiment (provided with three configurations shown in FIG. 2). In this case, the chromatic noise removing unit 11 receives the chromatic component data R4, G4, B4 output from the chromatic component calculating unit 10, and outputs the chromatic component data R5, G5, B5 from which noise has been removed. . The achromatic noise removing unit 12 can be configured as shown in FIG. 2 used in the description of the first embodiment, and receives the minimum value α as an achromatic component output from the achromatic component calculating unit 9 as an input. And outputs an achromatic component from which a noise component has been removed.
The chromatic color component data R5, G5, B5 from which noise has been removed and the achromatic color component data from which noise has been removed are input to the color correction amount calculation means 4c, and the color correction amount calculation means 4c outputs the color correction amounts R1, G1,. B1 is calculated. The first color data Ri, Gi, Bi and the color correction amounts R1, G1, B1 are input to the color correction amount adding means 2, and the second color data Ro, Go, Bo are calculated by adding these. Is done.
[0081]
The color conversion apparatus according to the present embodiment is configured to remove noise not only for chromatic color component data but also for chromatic color component data separately from chromatic color component data. Different from color conversion device. Achromatic component data is related to saturation information in combination with chromatic component data, but is data that has only brightness information by itself and differs from chromatic data for human visual characteristics. It is possible to have characteristics. Therefore, by removing noise from the chromatic color component data and the achromatic color component data separately, it is possible to select the influence and the magnitude of the effect of the noise removal according to the visual characteristics.
[0082]
【The invention's effect】
As described above, the color conversion device and method of the present invention perform color conversion on first image data, which is image information for each pixel including a plurality of first color data, and convert the first image data from a plurality of second color data. In the color conversion device for obtaining the second image data, a color correction amount generation means for calculating a color correction amount by performing color correction amount calculation and noise removal based on the first image data; An addition unit for adding the color correction amount and obtaining the second image data is included in the color data even when a process of increasing the saturation or brightness of the color data is performed. Without further emphasizing the effect of the noise component, it is possible to suppress the occurrence of image blur due to the suppression of the effect of the noise component.
[Brief description of the drawings]
FIG. 1 is a block diagram illustrating an example of a configuration of a color conversion device according to a first embodiment of the present invention.
FIG. 2 is a block diagram illustrating an example of a partial configuration of a noise removing unit 3 in the color conversion device according to the first embodiment of the present invention.
FIG. 3 is a diagram illustrating an example of original color data Rs, Gs, and Bs.
FIG. 4 is a diagram illustrating an example of first color data input to an image display device when a noise component exists.
FIG. 5 is a diagram illustrating an example of color data from which noise has been removed.
FIG. 6 is a diagram illustrating an example of second color data according to the first embodiment of the present invention.
FIG. 7 is a diagram illustrating an example of second color data when the noise removing unit is configured such that R2 = Ri, G2 = Gi, and B2 = Bi.
FIG. 8 is a block diagram illustrating an example of a conventional color conversion apparatus to which a noise removing unit is added.
FIG. 9 is a diagram illustrating an example of second color data obtained by a conventional color conversion device to which a noise removing unit is added.
FIG. 10 is a block diagram illustrating an example of a configuration of a color conversion device according to a second embodiment of the present invention.
FIG. 11 is a diagram illustrating an example of a color correction amount output from a color correction amount calculation unit in the color conversion device according to the second embodiment of the present invention.
FIG. 12 is a diagram illustrating an example of a color correction amount output from a noise removing unit in the color conversion device according to the second embodiment of the present invention.
FIG. 13 is a diagram illustrating an example of second color data output from a color correction amount adding unit in the color conversion device according to the second embodiment of the present invention.
FIG. 14 is a block diagram illustrating an example of a configuration of a color conversion device according to a third embodiment of the present invention.
FIG. 15 is a diagram showing achromatic component data α.
FIG. 16 is a diagram illustrating an example of chromatic component data in the color conversion device according to the third embodiment of the present invention.
FIG. 17 is a diagram illustrating an example of chromatic component data from which noise has been removed in the color conversion apparatus according to the third embodiment of the present invention.
FIG. 18 is a diagram illustrating an example of second color data in the color conversion device according to the third embodiment of the present invention.
FIG. 19 is a block diagram illustrating an example of a configuration of a color conversion device according to a fourth embodiment of the present invention.
[Explanation of symbols]
1, 1b, 1c, 1d color correction amount generating means, two color correction amount adding means, 3 noise removing means, 4, 4b, 4c color correction amount calculating means, 5 data shift means, 5a to 5h data storage section, 6 weighting Addition means, 7 color conversion means, 10 chromatic color component calculation means, 11 chromatic noise removal means, 12 achromatic noise removal means.

Claims (20)

In a color conversion device that performs color conversion on first image data that is image information for each pixel including a plurality of first color data and obtains second image data including a plurality of second color data,
A color correction amount generation unit that calculates a color correction amount by performing color correction amount calculation and noise removal based on the first image data,
A color conversion device comprising: an addition unit that adds the color correction amount to the first image data to obtain the second image data. The color correction amount generation unit includes a noise removal unit that removes noise from the first image data, and a color correction amount calculation unit that performs the color correction amount calculation using an output of the noise removal unit. The color conversion device according to claim 1, wherein: The color correction amount generating means includes a color correction amount calculating means for calculating a color correction amount from the first image data, and a noise removing means for removing the noise from an output of the color correction amount calculating means. The color conversion device according to claim 1, wherein: The color correction amount generating means includes means for calculating achromatic component data representing an achromatic component of the first image data, and chromatic component data from the first image data and the achromatic component data. , A noise removing unit for removing noise from the chromatic component data, and a color correction amount calculation for obtaining the color correction amount using the achromatic component data and the chromatic component data from which the noise has been removed. The color conversion device according to claim 1, further comprising: The color correction amount generating means includes means for calculating achromatic component data representing an achromatic component of the first image data, and chromatic component data from the first image data and the achromatic component data. And achromatic noise removing means for removing noise from the achromatic color component data and outputting a noise-free achromatic color component data; and removing noise from the chromatic color component data to remove noise. A chromatic noise removing unit that outputs chromatic component data; and a color correction amount calculating unit that obtains the color correction amount using the achromatic component data from which the noise has been removed and the chromatic component data from which the noise has been removed. The color conversion device according to claim 1, wherein: The achromatic component data is calculated as the minimum value of the first color data constituting the first image data, and the chromatic component data is obtained by converting the achromatic component data from the first color data. The color conversion device according to claim 4, wherein the color conversion device calculates the difference. 5. The color conversion apparatus according to claim 2, wherein the noise removing unit is a low-pass filter that blocks high-frequency components in input data and transmits low-frequency components. 6. The apparatus according to claim 5, wherein each of the achromatic noise removing unit and the chromatic noise removing unit is a low-pass filter that blocks high-frequency components in input data and transmits low-frequency components. Color conversion device. 9. The color conversion device according to claim 7, wherein the low-pass filter blocks a frequency component of about 1/9 or more of a clock frequency of the image data. The color conversion apparatus according to claim 1, wherein the color correction amount calculation is to calculate a correction amount for increasing lightness or saturation of at least one color. In a color conversion method for performing color conversion on first image data that is image information for each pixel composed of a plurality of first color data and obtaining second image data composed of a plurality of second color data,
A color correction amount calculation step of calculating a color correction amount by performing color correction amount calculation and noise removal based on the first image data,
Adding the color correction amount to the first image data to obtain the second image data. The color correction amount calculation step includes: a noise removal step of removing noise from the first image data; and a color correction amount calculation step of performing the color correction amount calculation using the data from which the noise has been removed by the noise removal step. The color conversion method according to claim 11, comprising: The color correction amount calculation step includes a color correction amount calculation step of calculating a color correction amount from the first image data, and a noise removal step of removing the noise from the calculation result in the color correction amount calculation step. The color conversion method according to claim 11, wherein: The color correction amount calculating step includes calculating achromatic component data representing an achromatic component of the first image data, and chromatic component data from the first image data and the achromatic component data. , A noise removal step of removing noise from the chromatic color component data, and a color correction amount calculation for obtaining the color correction amount using the achromatic color component data and the chromatic color component data from which the noise has been removed. The color conversion method according to claim 11, further comprising: The color correction amount calculating step includes calculating achromatic component data representing an achromatic component of the first image data, and chromatic component data from the first image data and the achromatic component data. Calculating the achromatic color component data, removing the noise from the achromatic color component data, outputting the achromatic color component data from which the noise has been removed, and removing the noise from the chromatic color component data. A chromatic color noise removing step of outputting chromatic color component data; and a color correction amount calculating step of obtaining the color correction amount using the noise-removed achromatic component data and the noise-removed chromatic color component data. The color conversion method according to claim 11, wherein: The achromatic component data is calculated as the minimum value of the first color data constituting the first image data, and the chromatic component data is obtained by converting the achromatic component data from the first color data. 16. The color conversion method according to claim 14, wherein the color conversion is calculated as a result of subtraction. 15. The color conversion method according to claim 12, wherein the noise removal step performs low-pass filtering for blocking high-frequency components in the input data and transmitting low-frequency components. 16. The method according to claim 15, wherein each of the achromatic noise removing step and the chromatic noise removing step performs low-pass filtering that blocks high-frequency components in input data and transmits low-frequency components. Color conversion method. 19. The color conversion method according to claim 17, wherein the low-pass filtering cuts off a frequency component of about 1/9 or more of a clock frequency of the image data. The color conversion method according to claim 11, wherein the color correction amount calculation is to obtain a correction amount for increasing lightness or saturation of at least one color.

Images