JP4055487B2 - Image signal processing apparatus and processing method, coefficient seed data generating apparatus and generating method used therefor, coefficient data generating apparatus and generating method, program for executing each method, and computer-readable recording of the program Medium - Google Patents

Description

【００５０】
この推定式の係数データＷｉは、（２）式に示すように、パラメータｐを含む生成式によって生成される。情報メモリバンク１２８には、この生成式における係数データである係数種データｗ_i0〜ｗ_i3が、クラス毎に、格納されている。
Ｗｉ＝ｗ_i0＋ｗ_i1ｐ＋ｗ_i2ｐ²＋ｗ_i3ｐ³ ・・・（２）
【００５１】
また、画像信号処理部１１０は、各クラスの係数種データｗ_i0〜ｗ_i3（輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙの各信号分）およびパラメータｐの値とを用い、（２）式によって、クラス毎に、パラメータｐの値に対応した推定式の係数データＷｉ（輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙの各信号分）を生成する係数生成回路１２９を有している。この係数生成回路１２９には、情報メモリバンク１２８より、上述した各クラスの係数種データｗ_i0〜ｗ_i3がロードされる。また、この係数生成回路１２９には、システムコントローラ１０１より、パラメータｐの値が供給される。
【００５２】
この係数生成回路１２９で生成される各クラスの係数データＷｉ（ｉ＝１〜ｎ）は、上述した係数メモリ１２６に格納される。この係数生成回路１２９における各クラスの係数データＷｉの生成は、例えば各垂直ブランキング期間で行われる。これにより、ユーザのリモコン送信機２００の操作によってパラメータｐの値が変更されても、係数メモリ１２６に格納される各クラスの係数データＷｉを、そのパラメータｐの値に対応したものに即座に変更でき、ユーザによる色調の調整がスムーズに行われる。
【００５３】
また、画像信号処理部１１０は、第１のタップ選択回路１２１で選択的に取り出される予測タップの画素データｘｉと、係数メモリ１２６より読み出される係数データＷｉとから、輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙのそれぞれにおける注目位置の画素データｙを演算する推定予測演算回路１２７を有している。
【００５４】
次に、画像信号処理部１１０の動作を説明する。
バッファメモリ１０８に格納されているＮＴＳＣ信号より、第２のタップ選択回路１２２で、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号における注目位置の周辺に位置するクラスタップの画素データが選択的に取り出される。このクラスタップの画素データは特徴量抽出部１２３に供給される。この特徴量抽出部１２３では、クラスタップの画素データに１ビットのＡＤＲＣ等の処理が施されて、ＡＤＲＣ等のクラス情報が生成される。
【００５５】
そして、特徴量抽出部１２３で生成されるクラス情報（ＡＤＲＣコード等）はクラスコード生成部１２５に供給される。このクラスコード生成部１２５では、このクラス情報に基づいて、クラス分類の結果を示すクラスコードＣＬが生成される。
【００５６】
このクラスコードＣＬは、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号における注目位置の画素データが属するクラスの検出結果を表している。このクラスコードＣＬは、係数メモリ１２６に読み出しアドレス情報として供給される。係数メモリ１２６には、例えば各垂直ブランキング期間に、係数生成回路１２９で、ユーザによって調整されたパラメータｐの値に対応して、クラス毎に、係数種データｗ_i0〜ｗ_i3（輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙの各信号分）を用いて推定式の係数データＷｉ（ｉ＝１〜ｎ）が（２）色差信号の生成式に基づいて求められて格納される。
【００５７】
係数メモリ１２６に上述したようにクラスコードＣＬが読み出しアドレス情報として供給されることで、この係数メモリ１２６からクラスコードＣＬに対応した係数データＷｉ（輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙの各信号分）が読み出されて、推定予測演算回路１２７に供給される。また、バッファメモリ１０８に格納されているＮＴＳＣ信号より、第１のタップ選択回路１２１で、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号における注目位置の周辺に位置する予測タップの画素データが選択的に取り出される。
【００５８】
推定予測演算回路１２７では、予測タップの画素データｘｉと、係数メモリ１２６より読み出される係数データＷｉとを用いて、（１）式の推定式に基づいて、輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙのそれぞれにおける注目位置の画素データｙが演算される。したがって、ユーザは、パラメータｐの値を調整することで、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号による画像の色調を良好に調整できる。
【００５９】
次に、係数種データの生成方法の一例について説明する。この例においては、上述した（２）式の生成式における係数データである係数種データｗ_i0〜ｗ_i3を求める例を示すものとする。
【００６０】
ここで、以下の説明のため、（３）式のように、ｔ_j（ｊ＝０〜３）を定義する。
ｔ₀＝１，ｔ₁＝ｐ，ｔ₂＝ｐ²，ｔ₃＝ｐ³ ・・・（３）
この（３）式を用いると、（２）式は、（４）式のように書き換えられる。
【００６１】
【数２】

【００６２】
最終的に、学習によって未定係数ｗ_ijを求める。すなわち、クラス毎に、ＮＴＳＣ信号に対応する生徒信号の画素データと、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号に対応する教師信号の画素データとを用いて、二乗誤差を最小にする係数値を決定する。いわゆる最小二乗法による解法である。学習数をｍ、ｋ（１≦ｋ≦ｍ）番目の学習データにおける残差をｅ_k、二乗誤差の総和をＥとすると、（１）式および（２）式を用いて、Ｅは（５）式で表される。ここで、ｘ_ikは生徒信号のｉ番目の予測タップ位置におけるｋ番目の画素データ、ｙ_kはそれに対応するｋ番目の教師信号の画素データを表している。
【００６３】
【数３】

【００６４】
最小二乗法による解法では、（５）式のｗ_ijによる偏微分が０になるようなｗ_ijを求める。これは、（６）式で示される。
【００６５】
【数４】

【００６６】
以下、（７）式、（８）式のように、Ｘ_ipjq、Ｙ_ipを定義すると、（６）式は、（９）式のように行列を用いて書き換えられる。
【００６７】
【数５】

【００６８】
【数６】

【００６９】
この方程式は一般に正規方程式と呼ばれている。この正規方程式は、掃き出し法（Gauss-Jordanの消去法）等を用いて、ｗ_ijについて解かれ、係数種データが算出される。
【００７０】
図３は、上述した係数種データの生成方法の一例の概念を示している。Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号から、ＮＴＳＣ信号に対応した複数の生徒信号を生成する。例えば、パラメータｐを９段階に可変して、９種類の生徒信号を生成する。このようにして生成された複数の生徒信号と教師信号との間で学習を行って係数種データを生成する。
【００７１】
図４は、上述したデジタル放送受信機１００の情報メモリバンク１２８に格納される係数種データｗ_i0〜ｗ_i3を生成するための係数種データ生成装置１５０の構成を示している。
【００７２】
この係数種データ生成装置１５０は、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号に対応した教師信号ＳＴが入力される入力端子１５１と、この教師信号ＳＴに対して色調変換処理を施す色調変換回路１５２とを有している。この色調変換回路１５２には、パラメータｐの値が入力される。
【００７３】
この色調変換回路１５２では、パラメータｐの値に対応して、色調（色相、彩度等）が変化するように、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号に処理が施される。例えば、Ｒ−Ｙ軸、Ｂ−Ｙ軸の角度を変えて色差信号Ｒ−Ｙ，Ｂ−Ｙの値を変化させることで、青みがかったものから赤みがかったものといった色相の変化を実現できる。
【００７４】
また、係数種データ生成装置１５０は、色調変換回路１５２で処理された後のＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号に対してエンコード処理を施して、生徒信号ＳＳとしてのＮＴＳＣ信号を得るＮＴＳＣエンコーダ１５３を有している。
【００７５】
また、係数種データ生成装置１５０は、ＮＴＳＣエンコーダ１５３より出力される生徒信号ＳＳより、教師信号ＳＴにおける注目位置の周辺に位置する複数の画素データを選択的に取り出して出力する第１のタップ選択回路１５４および第２のタップ選択回路１５５を有している。これら第１、第２のタップ選択回路１５４，１５５は、それぞれ上述した画像信号処理部１１０の第１，第２のタップ選択回路１２１，１２２と同様に構成される。
【００７６】
また、係数種データ生成装置１５０は、第２のタップ選択回路１５５で選択的に取り出されるクラスタップの画素データから特徴量を抽出する特徴量抽出部１５６を有している。この特徴量抽出部１５６は、上述した画像信号処理部１１０の特徴量抽出部１２３と同様に構成され、ＡＤＲＣコード等のクラス情報を生成する。
【００７７】
また、係数種データ生成装置１５０は、特徴量抽出部１５６で生成されるクラス情報（ＡＤＲＣコード等）に基づいて、クラス分類の結果を示すクラスコードＣＬを生成するクラスコード生成部１５８を有している。このクラスコード生成部１５８は、上述した画像信号処理部１１０のクラスコード生成部１２５と同様に構成される。このクラスコードＣＬは、教師信号ＳＴにおける注目位置の画素データｙの属するクラスを示すものである。
【００７８】
また、係数種データ生成装置１５０は、入力端子１５１に供給される教師信号ＳＴの時間調整を行うための遅延回路１５９と、この遅延回路１５９で時間調整された教師信号ＳＴから得られる各注目位置の画素データｙと、この各注目位置の画素データｙにそれぞれ対応して第１のタップ選択回路１５４で選択的に取り出される予測タップの画素データｘｉと、パラメータｐの値と、各注目位置の画素データｙにそれぞれ対応してクラスコード生成部１５８で生成されるクラスコードＣＬとから、クラス毎に、係数種データｗ_i0〜ｗ_i3を得るための正規方程式（（９）式参照）を生成する正規方程式生成部１６０を有している。
【００７９】
この場合、１個の画素データｙとそれに対応するｎ個の予測タップの画素データｘｉとの組み合わせで１個の学習データが生成されるが、パラメータｐの値に応じてＮＴＳＣエンコーダ１５３で複数の生徒信号ＳＳが順次生成されていき、教師信号と各生徒信号との間でそれぞれ学習データの生成が行われる。これにより、正規方程式生成部１６０では、パラメータｐの複数の値に係る多くの学習データが登録された正規方程式が生成され、係数種データｗ_i0〜ｗ_i3を求めることが可能となる。
【００８０】
なお、係数種データｗ_i0〜ｗ_i3として、教師信号ＳＴを構成する輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙのそれぞれに対応したものを得る必要がある。したがって、正規方程式生成部１６０では、クラス毎に、輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙのそれぞれに対応して正規方程式が生成される。この場合、輝度信号Ｙに対応した正規方程式は、注目位置の画素データｙとして輝度信号Ｙの画素データを用いることで生成される。同様に、色差信号Ｒ−Ｙ，Ｂ−Ｙのそれぞれに対応した正規方程式は、注目位置の画素データｙとして色差信号Ｒ−Ｙ，Ｂ−Ｙの画素データを用いることで生成される。
【００８１】
また、係数種データ生成装置１５０は、正規方程式生成部１６０で、クラス毎に生成された正規方程式のデータが供給され、当該正規方程式を解いて、クラス毎に、係数種データｗ_i0〜ｗ_i3を求める係数種データ決定部１６１と、この求められた係数種データを格納する係数種メモリ１６２とを有している。係数種データ決定部１６１では、正規方程式が例えば掃き出し法などによって解かれて、係数種データが求められる。
【００８２】
図４に示す係数種データ生成装置１５０の動作を説明する。
入力端子１５１には、教師信号ＳＴとしてのＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号が供給される。このＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号は色調変換回路１５２に供給される。この色調変換回路１５２には、パラメータｐの値が入力される。色調変換回路１５２は、パラメータｐの値に対応して、色調（色相、彩度等）が変化するように、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号に処理を施す。
【００８３】
このように色調変換回路１５２で処理されたＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号はＮＴＳＣエンコーダ１５３に供給される。ＮＴＳＣエンコーダ１５３は、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号にエンコード処理を施して生徒信号ＳＳとしてのＮＴＳＣ信号を生成する。
【００８４】
また、ＮＴＳＣエンコーダ１５３より出力される生徒信号ＳＳより、第２のタップ選択回路１５５で、教師信号ＳＴにおける注目位置の周辺に位置するクラスタップの画素データが選択的に取り出される。このクラスタップの画素データは特徴量抽出部１５６に供給される。この特徴量抽出部１５６は、クラスタップの画素データに基づいて、ＡＤＲＣコード等のクラス情報を生成する。
【００８５】
そして、特徴量抽出部１５６で生成されるクラス情報（ＡＤＲＣコード等）はクラスコード生成部１５８に供給される。このクラスコード生成部１５８では、クラス情報に基づいて、教師信号ＳＴにおける注目位置の画素データｙが属するクラス（クラス分類の結果）を示すクラスコードＣＬが生成される。
【００８６】
また、ＮＴＳＣエンコーダ１５３より出力される生徒信号ＳＳより、第１のタップ選択回路１５４で、教師信号ＳＴにおける注目位置の周辺に位置する予測タップの画素データが選択的に取り出される。
【００８７】
そして、遅延回路１５９で時間調整された教師信号ＳＴから得られる各注目位置の画素データｙと、この各注目位置の画素データｙにそれぞれ対応して第１のタップ選択回路１５４で選択的に取り出される予測タップの画素データｘｉと、パラメータｐの値と、各注目位置の画素データｙにそれぞれ対応してクラスコード生成部１５８で生成されるクラスコードＣＬとを用いて、正規方程式生成部１６０では、クラス毎に、係数種データｗ_i0〜ｗ_i3を得るための正規方程式（（９）式参照）が生成される。
【００８８】
そして、係数種データ決定部１６１で各正規方程式が解かれ、各クラスの係数種データｗ_i0〜ｗ_i3（輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙの各信号分）が求められ、それらの係数種データｗ_i0〜ｗ_i3は係数種メモリ１６２に格納される。
【００８９】
このように、図４に示す係数種データ生成装置１５０においては、図１の画像信号処理部１１０の情報メモリバンク１２８に格納される、クラス毎の、推定式（（１）式参照）で用いられる係数データＷｉを求めるための生成式（（２）式参照）における係数データである係数種データｗ_i0〜ｗ_i3を生成することができる。
【００９０】
次に、係数種データの生成方法の他の例について説明する。この例においても、上述した（２）式の生成式における係数データである係数種データｗ_i0〜ｗ_i3を求める例を示すものとする。
【００９１】
図５は、この例の概念を示している。教師信号から複数の生徒信号を生成する。例えば、パラメータｐの値を９段階に可変して、９種類の生徒信号を生成する。このようにして生成された各生徒信号と教師信号との間で学習を行って、パラメータｐの各値毎に、（１）式の推定式の係数データＷｉを生成する。そして、パラメータｐの各値に対応して生成された係数データＷｉを使用して、（２）式の生成式における係数データである係数種データｗ_i0〜ｗ_i3を生成する。
【００９２】
まず、推定式の係数データの求め方を説明する。ここでは、（１）式の推定式の係数データＷｉ（ｉ＝１〜ｎ）を最小二乗法により求める例を示すものとする。
【００９３】
上述の、（１）式において、学習前は係数データＷ₁ ，Ｗ₂，‥‥，Ｗ_n は未定係数である。学習は、クラス毎に、複数の信号データに対して行う。学習データ数がｍの場合、（１）式に従って、以下に示す（１２）式が設定される。ｎは予測タップの数を示している。
【００９４】
ｙ_k ＝Ｗ₁ ×ｘ_k1＋Ｗ₂ ×ｘ_k2＋‥‥＋Ｗ_n ×ｘ_kn ・・・（１０）
（ｋ＝１，２，‥‥，ｍ）
ｍ＞ｎの場合、係数データＷ₁ ，Ｗ₂，‥‥，Ｗ_nは、一意に決まらないので、誤差ベクトルｅの要素ｅ_kを、以下の式（１１）で定義して、（１２）式のｅ²を最小にする係数データを求める。いわゆる最小２乗法によって係数データを一意に定める。
ｅ_k＝ｙ_k−｛ｗ₁×ｘ_k1＋ｗ₂×ｘ_k2＋‥‥＋ｗ_n×ｘ_kn｝ ・・・（１１）
（ｋ＝１，２，‥‥ｍ）
【００９５】
【数７】

【００９６】
（１２）式のｅ²を最小とする係数データを求めるための実際的な計算方法としては、まず、（１３）式に示すように、ｅ²を係数データＷｉ(ｉ＝１，２，・・・，ｎ）で偏微分し、ｉの各値について偏微分値が０となるように係数データＷｉを求めればよい。
【００９７】
【数８】

【００９８】
（１３）式から係数データＷｉを求める具体的な手順について説明する。（１４）式、（１５）式のようにＸji，Ｙi を定義すると、（１３）式は、（１６）式の行列式の形に書くことができる。
【００９９】
【数９】

【０１００】
【数１０】

【０１０１】
（１６）式は、一般に正規方程式と呼ばれるものである。この正規方程式を掃き出し法（Gauss-Jordanの消去法）等の一般解法で解くことにより、係数データＷｉ（ｉ＝１，２，・・・，ｎ）を求めることができる。
【０１０２】
次に、各生徒信号に対応して生成された係数データＷｉを使用した、係数種データの求め方を説明する。
パラメータｐの値に対応した生徒信号を用いた学習による、あるクラスの係数データが、ｋ_piになったとする。ここで、ｉは予測タップの番号である。このｋ_piから、このクラスの係数種データを求める。
【０１０３】
係数データＷｉ（ｉ＝１〜ｎ）は、係数種データｗ_i0〜ｗ_i3を使って、上述した（２）式で表現される。ここで、係数データＷｉに対して最小二乗法を使用することを考えると、残差は、（１７）式で表される。
【０１０４】
【数１１】

【０１０５】
ここで、ｔ_jは、上述の（３）式に示されている。（１７）式に最小二乗法を作用させると、（１８）式が得られる。
【０１０６】
【数１２】

【０１０７】
ここで、Ｘ_jk，Ｙ_jをそれぞれ（１９）式、（２０）式のように定義すると、（１８）式は（２１）式のように書き換えられる。この（２１）式も正規方程式であり、この式を掃き出し法等の一般解法で解くことにより、係数種データｗ_i0〜ｗ_i3を算出することができる。
【０１０８】
【数１３】

【０１０９】
図６は、図５に示す概念に基づいて係数種データを生成する係数種データ生成装置１５０Ａの構成を示している。この図６において、図４と対応する部分には同一符号を付し、その詳細説明は省略する。
【０１１０】
係数種データ生成装置１５０Ａは、遅延回路１５９で時間調整された教師信号ＳＴより得られる各注目位置の画素データｙと、この各注目位置の画素データｙにそれぞれ対応して第１のタップ選択回路１５４で選択的に取り出される予測タップの画素データｘｉと、各注目位置の画素データｙにそれぞれ対応してクラスコード生成部１５８で生成されるクラスコードＣＬとから、クラス毎に、係数データＷｉ（ｉ＝１〜ｎ）を得るための正規方程式（（１６）式参照）を生成する正規方程式生成部１６３を有している。
【０１１１】
この場合、１個の画素データｙとそれに対応するｎ個の予測タップの画素データｘｉとの組み合わせで１個の学習データが生成されるが、パラメータｐの値に応じてＮＴＳＣエンコーダ１５３で複数の生徒信号ＳＳが順次生成されていき、教師信号と各生徒信号との間でそれぞれ学習データの生成が行われる。これにより、正規方程式生成部１６３では、パラメータｐの各値にそれぞれに対応して、クラス毎に、係数データＷｉ（ｉ＝１〜ｎ）を得るための正規方程式が生成される。
【０１１２】
なお、係数データＷｉとして、教師信号ＳＴを構成する輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙのそれぞれに対応したものを得る必要がある。したがって、正規方程式生成部１６３では、クラス毎に、輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙのそれぞれに対応して正規方程式が生成される。この場合、輝度信号Ｙに対応した正規方程式は、注目位置の画素データｙとして輝度信号Ｙの画素データを用いることで生成される。同様に、色差信号Ｒ−Ｙ，Ｂ−Ｙのそれぞれに対応した正規方程式は、注目位置の画素データｙとして色差信号Ｒ−Ｙ，Ｂ−Ｙの画素データを用いることで生成される。
【０１１３】
また、係数種データ生成装置１５０Ａは、正規方程式生成部１６３で生成された正規方程式のデータが供給され、その正規方程式を解いて、パラメータｐの各値にそれぞれ対応した、各クラスの係数データＷｉを求める係数データ決定部１６４と、パラメータｐの値および各生徒信号にそれぞれ対応した係数データＷｉとを使用して、クラス毎に、係数種データｗ_i0〜ｗ_i3を得るための正規方程式（（２１）式参照）を生成する正規方程式生成部１６５とを有している。この場合も、輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙのそれぞれに対応して正規方程式が生成される。
【０１１４】
また、係数種データ生成装置１５０Ａは、正規方程式生成部１６５でクラス毎に生成された正規方程式のデータが供給され、クラス毎に正規方程式を解いて、各クラスの係数種データｗ_i0〜ｗ_i3を求める係数種データ決定部１６６と、この求められた係数種データｗ_i0〜ｗ_i3を格納する係数種メモリ１６２とを有している。
【０１１５】
図６に示す係数種データ生成装置１５０Ａのその他は、図４に示す係数種データ生成装置１５０と同様に構成される。
【０１１６】
次に、図６に示す係数種データ生成装置１５０Ａの動作を説明する。
入力端子１５１には、教師信号ＳＴとしてのＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号が供給される。このＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号は色調変換回路１５２に供給される。この色調変換回路１５２には、パラメータｐの値が入力される。色調変換回路１５２は、パラメータｐの値に対応して、色調（色相、彩度等）が変化するように、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号に処理を施す。
【０１１７】
このように色調変換回路１５２で処理されたＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号はＮＴＳＣエンコーダ１５３に供給される。ＮＴＳＣエンコーダ１５３は、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号にエンコード処理を施して生徒信号ＳＳとしてのＮＴＳＣ信号を生成する。
【０１１８】
また、ＮＴＳＣエンコーダ１５３より出力される生徒信号ＳＳより、第２のタップ選択回路１５５で、教師信号ＳＴにおける注目位置の周辺に位置するクラスタップの画素データが選択的に取り出される。このクラスタップの画素データは特徴量抽出部１５６に供給される。この特徴量抽出部１５６は、クラスタップの画素データに基づいて、ＡＤＲＣコード等のクラス情報を生成する。
【０１１９】
そして、特徴量抽出部１５６で生成されるクラス情報（ＡＤＲＣコード等）はクラスコード生成部１５８に供給される。このクラスコード生成部１５８では、クラス情報に基づいて、教師信号ＳＴにおける注目位置の画素データｙが属するクラス（クラス分類の結果）を示すクラスコードＣＬが生成される。
【０１２０】
また、ＮＴＳＣエンコーダ１５３より出力される生徒信号ＳＳより、第１のタップ選択回路１５４で、教師信号ＳＴにおける注目位置の周辺に位置する予測タップの画素データが選択的に取り出される。
【０１２１】
そして、遅延回路１５９で時間調整された教師信号ＳＴから得られる各注目位置の画素データｙと、この各注目位置の画素データｙにそれぞれ対応して第１のタップ選択回路１５４で選択的に取り出される予測タップの画素データｘｉと、パラメータｐの値と、各注目位置の画素データｙにそれぞれ対応してクラスコード生成部１５８で生成されるクラスコードＣＬとを用いて、正規方程式生成部１６３では、パラメータｐの各値にそれぞれ対応した各生徒信号ＳＳについて、クラス毎に、係数データＷｉ（ｉ＝１〜ｎ）を得るための正規方程式（（１６）式参照）が生成される。
【０１２２】
そして、係数データ決定部１６４でその正規方程式が解かれ、パラメータｐの各値にそれぞれ対応した、各クラスの係数データＷｉが求められる。正規方程式生成部１６５では、このパラメータｐの各値にそれぞれ対応した各クラスの係数データＷｉを用いて、クラス毎に、係数種データｗ_i0〜ｗ_i3を得るための正規方程式（（２１）式参照）が生成される。
【０１２３】
そして、係数種データ決定部１６６でその正規方程式が解かれ、各クラスの係数種データｗ_i0〜ｗ_i3（輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙの各信号分）が求められ、その係数種データｗ_i0〜ｗ_i3は係数種メモリ１６２に格納される。
【０１２４】
このように、図６に示す係数種データ生成装置１５０Ａにおいても、図１の画像信号処理部１１０の情報メモリバンク１２８に格納される、各クラスの係数種データｗ_i0〜ｗ_i3を生成することができる。
【０１２５】
なお、図４、図６に示す係数種データ生成装置１５０，１５０Ａにおいては、入力端子１５１に供給される教師信号ＳＴとしてのＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号に対して色調変換回路１５２で、パラメータｐの値に対応して、色調が変化するように処理を施し、ＮＴＳＣエンコーダ１５３からパラメータｐの複数の値にそれぞれ対応した複数の生徒信号ＳＳとしてのＮＴＳＣ信号を得、１つの教師信号ＳＴと複数の生徒信号ＳＳとの間での学習によって係数種データｗ_i0〜ｗ_i3を得るものを示した。
【０１２６】
しかし、これとは逆に複数の教師信号ＳＴと１つの生徒信号ＳＳとの間での学習によっても、同様に係数種データｗ_i0〜ｗ_i3を求めることができる。図７は、その場合における、係数種データ生成装置１５０Ｂの構成を示している。この図７において、図４と対応する部分には同一符号を付し、その詳細説明は省略する。
【０１２７】
この係数種データ生成装置１５０Ｂでは、入力端子１５１に供給されるＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号は直接ＮＴＳＣエンコーダ１５３に供給される。そして、ＮＴＳＣエンコーダ１５３で、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号にエンコード処理が施されて生徒信号ＳＳとしてのＮＴＳＣ信号が生成される。
【０１２８】
また、係数種データ生成装置１５０Ｂでは、入力端子１５１に供給されるＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号は色調変換回路１５７に供給される。この色調変換回路１５７には、パラメータｐの値も供給される。この色調変換回路１５７では、パラメータｐの値に対応して、色調（色相、彩度等）が変化するように、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号に処理が施される。色調変換回路１５７から、パラメータｐの複数の値にそれぞれ対応した複数の教師信号ＳＴとしてのＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号が得られる。
【０１２９】
そして、正規方程式生成部１６０では、色調変換回路１５７より出力される教師信号ＳＴから得られる各注目位置の画素データｙと、この各注目位置の画素データｙにそれぞれ対応して第１のタップ選択回路１５４で選択的に取り出される予測タップの画素データｘｉと、パラメータｐの値と、各注目位置の画素データｙにそれぞれ対応してクラスコード生成部１５８で生成されるクラスコードＣＬとが用いられて、クラス毎に、係数種データｗ_i0〜ｗ_i3を得るための正規方程式（（９）式参照）が生成される。
【０１３０】
その他の構成は図４に示す係数種データ生成装置１５０と同様である。なお、生徒信号ＳＳと教師信号ＳＴとの間の時間調整は、例えば色調変換回路１５７内に設けられる遅延回路（図示せず）により行われる。
【０１３１】
このように、図７に示す係数種データ生成装置１５０Ｂの正規方程式生成部１６０では、複数の教師信号ＳＴと１つの生徒信号ＳＳとの間での学習によって正規方程式が生成され、図４の係数種データ生成装置１５０と同様に、係数種データｗ_i0〜ｗ_i3を生成することができる。
【０１３２】
また、図４、図６、図７に示す係数種データ生成装置１５０，１５０Ａ，１５０Ｂは、色調変換回路１５２あるいは色調変換回路１５７を備えており、これにより複数の生徒信号ＳＳまたは複数の教師信号ＳＴを生成するものであった。しかし、パラメータｐの値に対応した複数の生徒信号ＳＳまたは複数の教師信号ＳＴを外部から入力するようにしてもよい。図８は、その場合における、係数種データ生成装置１５０Ｃの構成を示している。この図７において、図４と対応する部分には同一符号を付し、その詳細説明は省略する。
【０１３３】
この係数種データ生成装置１５０Ｃでは、入力端子１５１Ｔおよび入力端子１５１ＳにＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号が入力される。これら入力端子１５１Ｔまたは入力端子１５１Ｓに入力されるＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号は、パラメータｐの値に対応して色調が変化したものとされる。これら入力端子１５１Ｔ，１５１Ｓの一方に入力されるＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号は、例えば同一のビデオカメラで撮像されて得られたものである。パラメータｐの値に対応して色調が変化したものを得るために、例えばビデオカメラに内蔵する色調変換回路で色調変換処理を行い、あるいは撮像環境を変化させて撮像を行う。
【０１３４】
入力端子１５１Ｔに入力されるＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号は教師信号ＳＴとなる。また、入力端子１５１Ｓに入力されるＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号はＮＴＳＣエンコーダ１５３に供給される。そして、このＮＴＳＣエンコーダ１５３で、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号にエンコード処理が施されて生徒信号ＳＳとしてのＮＴＳＣ信号が生成される。
その他の構成は図４に示す係数種データ生成装置１５０と同様である。
【０１３５】
図８に示す係数種データ生成装置１５０Ｃの正規方程式生成部１６０では、１つの教師信号ＳＴと複数の生徒信号ＳＳとの間での学習によって、あるいは複数の教師信号ＳＴと１つの生徒信号ＳＳとの間での学習によって、正規方程式が生成され、図４の係数種データ生成装置１５０あるいは図７の係数種データ生成装置１５０Ｂと同様に、係数種データｗ_i0〜ｗ_i3を生成することができる。
【０１３６】
なお、図７の係数種データ生成装置１５０Ｂおよび図８の係数種データ生成装置１５０Ｃはそれぞれ図４に示す係数種データ生成装置１５０をベースとしたものであったが、図６に示す係数種データ生成装置１５０Ａをベースとしたものも同様に構成することができる。
【０１３７】
なお、図１に示すテレビ受信機１００の画像信号処理部１１０においては、情報メモリバンク１２８に各クラスの係数種データｗ_i0〜ｗ_i3を格納しておき、係数生成回路１２９でこの各クラスの係数種データｗ_i0〜ｗ_i3を用いてパラメータｐの値に応じた各クラスの係数データＷｉを生成し、これを係数メモリ１２６に格納するものである。
【０１３８】
しかし、情報メモリバンク１２８にクラスおよびパラメータｐの値の各組み合わせに対応した係数データＷｉを格納しておき、この情報メモリバンク１２８からパラメータｐの値に応じた各クラスの係数データＷｉを読み出し、これを係数メモリ１２６に格納するようにしてもよい。この場合、情報メモリバンク１２８に格納されるクラスおよびパラメータｐの値の各組み合わせに対応した係数データＷｉは、上述の図６に示す係数種データ生成装置１５０Ａにおける係数データ決定部１６４より得ることができる。
【０１３９】
なお、図１の画像信号処理部１１０における処理を、例えば図９に示すような画像信号処理装置３００によって、ソフトウェアで実現することも可能である。まず、図９に示す画像信号処理装置３００について説明する。この画像信号処理装置３００は、装置全体の動作を制御するＣＰＵ３０１と、このＣＰＵ３０１の動作プログラムや係数種データ等が格納されたＲＯＭ（read only memory）３０２と、ＣＰＵ３０１の作業領域を構成するＲＡＭ（random access memory）３０３とを有している。これらＣＰＵ３０１、ＲＯＭ３０２およびＲＡＭ３０３は、それぞれバス３０４に接続されている。
【０１４０】
また、画像信号処理装置３００は、外部記憶装置としてのハードディスクドライブ（ＨＤＤ）３０５と、フロッピー（登録商標）ディスク３０６をドライブするドライブ（ＦＤＤ）３０７とを有している。これらドライブ３０５，３０７は、それぞれバス３０４に接続されている。
【０１４１】
また、画像信号処理装置３００は、インターネット等の通信網４００に有線または無線で接続する通信部３０８を有している。この通信部３０８は、インタフェース３０９を介してバス３０４に接続されている。
【０１４２】
また、画像信号処理装置３００は、ユーザインタフェース部を備えている。このユーザインタフェース部は、リモコン送信機２００からのリモコン信号ＲＭを受信するリモコン信号受信回路３１０と、ＬＣＤ（liquid crystal display）等からなるディスプレイ３１１とを有している。受信回路３１０はインタフェース３１２を介してバス３０４に接続され、同様にディスプレイ３１１はインタフェース３１３を介してバス３０４に接続されている。
【０１４３】
また、画像信号処理装置３００は、ＮＴＳＣ信号を入力するための入力端子３１４と、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号を出力するための出力端子３１５とを有している。入力端子３１４はインタフェース３１６を介してバス３０４に接続され、同様に出力端子３１５はインタフェース３１７を介してバス３０４に接続される。
【０１４４】
ここで、上述したようにＲＯＭ３０２に処理プログラムや係数種データ等を予め格納しておく代わりに、例えばインターネットなどの通信網４００より通信部３０８を介してダウンロードし、ハードディスクやＲＡＭ３０３に蓄積して使用することもできる。また、これら処理プログラムや係数種データ等をフロッピー（登録商標）ディスク３０６で提供するようにしてもよい。
【０１４５】
また、処理すべきＮＴＳＣ信号を入力端子３１４より入力する代わりに、予めハードディスクドライブ３０７に記録しておき、あるいはインターネットなどの通信網４００より通信部３０８を介してダウンロードしてもよい。また、処理後のＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号を出力端子３１５に出力する代わり、あるいはそれと並行してディスプレイ３１１に供給して画像表示をしたり、さらにはハードディスクに格納したり、通信部３０８を介してインターネットなどの通信網４００に送出するようにしてもよい。
【０１４６】
図１０のフローチャートを参照して、図９に示す画像信号処理装置３００における、ＮＴＳＣ信号よりＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号を得るため処理手順を説明する。
【０１４７】
まず、ステップＳＴ１で、処理を開始し、ステップＳＴ２で、例えば入力端子３１４より装置内に１フレーム分または１フィールド分のＮＴＳＣ信号を入力する。このように入力端子３１４より入力されるＮＴＳＣ信号を構成する画素データはＲＡＭ３０３に一時的に格納される。なお、このＮＴＳＣ信号が装置内のハードディスクドライブ３０７に予め記録されている場合には、このドライブ３０７からこのＮＴＳＣ信号を読み出し、このＮＴＳＣ信号を構成する画素データをＲＡＭ３０３に一時的に格納する。
【０１４８】
そして、ステップＳＴ３で、ＮＴＳＣ信号の全フレームまたは全フィールドの処理が終わっているか否かを判定する。処理が終わっているときは、ステップＳＴ４で、処理を終了する。一方、処理が終わっていないときは、ステップＳＴ５に進む。
【０１４９】
このステップＳＴ５では、ユーザのリモコン送信機２００の操作によって入力された色調指定値であるパラメータｐの値を、例えばＲＡＭ３０３より取得する。そして、ステップＳＴ６で、パラメータｐの値、各クラスの係数種データを用いて、生成式（例えば（２）式）によって、各クラスの推定式（（１）式参照）の係数データＷｉを生成する。
【０１５０】
次に、ステップＳＴ７で、ステップＳＴ２で入力されたＮＴＳＣ信号より、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号における注目位置に対応して、クラスタップおよび予測タップの画素データを取得する。そして、ステップＳＴ８で、ステップＳＴ２で入力された１フレーム分または１フィールド分のＮＴＳＣ信号の画素データの全領域においてＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号（輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙ）の画素データを得る処理が終了したか否かを判定する。終了しているときは、ステップＳＴ２に戻り、次の１フレーム分または１フィールド分のＮＴＳＣ信号の入力処理に移る。一方、処理が終了していないときは、ステップＳＴ９に進む。
【０１５１】
このステップＳＴ９では、ステップＳＴ７で取得されたクラスタップの画素データからクラスコードＣＬを生成する。そして、ステップＳＴ１０で、そのクラスコードＣＬに対応した係数データＷｉと予測タップの画素データを使用して、推定式により、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号における注目位置の画素データを生成し、その後にステップＳＴ７に戻って、上述したと同様の処理を繰り返す。
【０１５２】
このように、図１０に示すフローチャートに沿って処理をすることで、入力されたＮＴＳＣ信号の画素データを処理して、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号の画素データを得ることができる。上述したように、このように処理して得られたＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号は出力端子３１５に出力されたり、ディスプレイ３１１に供給されてそれによる画像が表示されたり、さらにはハードディスクドライブ３０５に供給されてハードディスクに記録されたりする。
【０１５３】
なお、ＲＯＭ３０２に、各クラスの係数種データではなく、クラスおよびパラメータｐの値の各組み合わせの係数データが格納されている場合には、ステップＳＴ６で、ＲＯＭ３０２からパラメータｐの値に対応した各クラスの推定式（（１）式参照）の係数データＷｉを読み出すことになる。
また、処理装置の図示は省略するが、図４の係数種データ生成装置１５０における処理を、ソフトウェアで実現することも可能である。
【０１５４】
図１１のフローチャートを参照して、係数種データを生成するための処理手順を説明する。
まず、ステップＳＴ２１で、処理を開始し、ステップＳＴ２２で、学習に使われる、色調パターン（パラメータｐの値で特定される）を選択する。そして、ステップＳＴ２３で、全ての色調パターンに対して学習が終わったか否かを判定する。全ての色調パターンに対して学習が終わっていないときは、ステップＳＴ２４に進む。
【０１５５】
このステップＳＴ２４では、教師信号（Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号）を１フレーム分または１フィールド分だけ入力する。そして、ステップＳＴ２５で、教師信号の全フレームまたは全フィールドの処理が終了したか否かを判定する。終了したときは、ステップＳＴ２２に戻って、次の色調パターンの選択を行って、上述したと同様の処理を繰り返す。一方、終了していないときは、ステップＳＴ２６に進む。
【０１５６】
このステップＳＴ２６では、ステップＳＴ２４で入力された教師信号より、ステップＳＴ２２で選択された色調パターンに対応した生徒信号（ＮＴＳＣ信号）を生成する。そして、ステップＳＴ２７で、ステップＳＴ２６で生成された生徒信号より、ステップＳＴ２４で入力された教師信号における注目位置に対応して、クラスタップおよび予測タップの画素データを取得する。そして、ステップＳＴ２８で、ステップＳＴ２４で入力された教師信号の全領域において学習処理を終了しているか否かを判定する。学習処理を終了しているときは、ステップＳＴ２４に戻って、次の１フレーム分または１フィールド分の教師信号の入力を行って、上述したと同様の処理を繰り返す。一方、処理が終了していないときは、ステップＳＴ２９に進む。
【０１５７】
このステップＳＴ２９では、ステップＳＴ２７で取得されたクラスタップの画素データからクラスコードＣＬを生成する。そして、ステップＳＴ３０で、正規方程式（（９）式参照）を生成する。その後に、ステップＳＴ２７に戻る。
【０１５８】
また、ステップＳＴ２３で、全ての色調パターンに対して学習が終わったときは、ステップＳＴ３１に進む。このステップＳＴ３１では、正規方程式を掃き出し法等で解くことによって、各クラスの係数種データを算出し、ステップＳＴ３２で、その係数種データをメモリに保存し、その後にステップＳＴ３３で、処理を終了する。
【０１５９】
このように、図１１に示すフローチャートに沿って処理をすることで、図４に示す係数種データ生成装置１５０と同様の手法によって、係数種データを得ることができる。
【０１６０】
また、処理装置の図示は省略するが、図６の係数種データ生成装置１５０Ａにおける処理も、ソフトウェアで実現可能である。
【０１６１】
図１２のフローチャートを参照して、係数種データを生成するための処理手順を説明する。
まず、ステップＳＴ４１で、処理を開始し、ステップＳＴ４２で、学習に使われる、色調パターン（パラメータｐの値で特定される）を選択する。そして、ステップＳＴ４３で、全ての色調パターンに対して学習が終わったか否かを判定する。全ての色調パターンに対して学習が終わっていないときは、ステップＳＴ４４に進む。
【０１６２】
このステップＳＴ４４では、教師信号（Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号）を１フレーム分または１フィールド分だけ入力する。そして、ステップＳＴ４５で、教師信号の全フレームまたは全フィールドの処理が終了したか否かを判定する。終了していないときは、ステップＳＴ４６で、ステップＳＴ４４で入力された教師信号より、ステップＳＴ４２で選択された色調パターンに対応した生徒信号（ＮＴＳＣ信号）を生成する。
【０１６３】
そして、ステップＳＴ４７で、ステップＳＴ４６で生成された生徒信号より、ステップＳＴ４４で入力された教師信号における注目位置に対応して、クラスタップおよび予測タップの画素データを取得する。そして、ステップＳＴ４８で、ステップＳＴ４４で入力された教師信号の全領域において学習処理を終了しているか否かを判定する。学習処理を終了しているときは、ステップＳＴ４４に戻って、次の１フレーム分または１フィールド分の教師信号の入力を行って、上述したと同様の処理を繰り返す。一方、学習処理を終了していないときは、ステップＳＴ４９に進む。
【０１６４】
このステップＳＴ４９では、ステップＳＴ４７で取得されたクラスタップの画素データからクラスコードＣＬを生成する。そして、ステップＳＴ５０で、係数データを得るための正規方程式（（１６）式参照）を生成する。その後に、ステップＳＴ４７に戻る。
【０１６５】
上述したステップＳＴ４５で、処理が終了したときは、ステップＳＴ５１で、ステップＳＴ５０で生成された正規方程式を掃き出し法などで解いて、各クラスの係数データを算出する。その後に、ステップＳＴ４２に戻って、次の色調パターンの選択を行って、上述したと同様の処理を繰り返し、次の色調パターンに対応した、各クラスの係数データを求める。
【０１６６】
また、上述のステップＳＴ４３で、全ての色調パターンに対する係数データの算出処理が終了したときは、ステップＳＴ５２に進む。この時点では、クラスおよびパラメータｐの値の各組み合わせの係数データが生成された状態にある。ステップＳＴ５２では、全ての色調パターンに対する係数データから、係数種データを求めるための正規方程式（（２１）式参照）を生成する。
【０１６７】
そして、ステップＳＴ５３で、ステップＳＴ５２で生成された正規方程式を掃き出し法等で解くことによって、各クラスの係数種データを算出し、ステップＳＴ５４で、その係数種データをメモリに保存し、その後にステップＳＴ５５で、処理を終了する。
【０１６８】
このように、図１２に示すフローチャートに沿って処理をすることで、図６に示す係数種データ生成装置１５０Ａと同様の手法によって、係数種データを得ることができる。
【０１６９】
なお、図１の画像信号処理部１１０では、係数データＷｉ（ｉ＝１〜ｎ）を生成するために（２）式の生成式を使用したが、次数の異なった多項式や、他の関数で表現される式でも実現可能である。
【０１７０】
また、図１の画像信号処理部１１０ではコンポジット信号であるＮＴＳＣ信号をコンポーネント信号である輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙに変換するものを示した。しかし、コンポジット信号とコンポーネント信号の組み合わせはこれに限定されるものではない。例えば、他のコンポジット信号としては、ＰＡＬ(phase alternating by line)信号等がある。また、他のコンポーネント信号としては赤、緑、青の３原色信号等がある。
【０１７１】
【発明の効果】
この発明によれば、コンポジット信号を直接コンポーネント信号に変換する際に、コンポーネント信号によって得られる画像の色調を定めるパラメータの値に対応して、当該コンポーネント信号における注目位置の画素データを生成する構成とするものであり、コンポーネント信号によって得られる画像の色調を良好に調整できる。
【０１７２】
また、この発明によれば、コンポジット信号を直接コンポーネント信号に変換する際に、パラメータの値に応じてコンポーネント信号によって得られる画像の色調を調整するために使用される係数種データまたは係数データを良好に得ることができる。
【図面の簡単な説明】
【図１】実施の形態としてのテレビ受信機の構成を示すブロック図である。
【図２】予測タップ、クラスタップの一例を示す図である。
【図３】係数種データの生成方法の一例を説明するための図である。
【図４】係数種データ生成装置の構成例を示すブロック図である。
【図５】係数種データ生成方法の他の例を説明するための図である。
【図６】係数種データ生成装置の構成例を示すブロック図である。
【図７】係数種データ生成装置の構成例を示すブロック図である。
【図８】係数種データ生成装置の構成例を示すブロック図である。
【図９】ソフトウェアで実現するための画像信号処理装置の構成例を示すブロック図である。
【図１０】画像信号処理を示すフローチャートである。
【図１１】係数種データ生成処理（その１）を示すフローチャートである。
【図１２】係数種データ生成処理（その２）を示すフローチャートである。
【符号の説明】
１００・・・テレビ受信機、１０１・・・システムコントローラ、１０２・・・リモコン信号受信回路、１０５・・・受信アンテナ、１０６・・・チューナ、１０８・・・バッファメモリ、１１０・・・画像信号処理部、１１１・・・ディスプレイ部、１１２・・・ＯＳＤ回路、１１３・・・合成器、１２１・・・第１のタップ選択回路、１２２・・・第２のタップ選択回路、１２３・・・特徴量抽出部、１２５・・・クラスコード生成部、１２６・・・係数メモリ、１２７・・・推定予測演算回路、１２８・・・情報メモリバンク、１２９・・・係数生成回路、１５０，１５０Ａ，１５０Ｂ，１５０Ｃ・・・係数種データ生成装置、１５１，１５１Ｔ，１５１Ｓ・・・入力端子、１５２，１５７・・・色調変換回路、１５３・・・ＮＴＳＣエンコーダ、１５４・・・第１のタップ選択回路、１５５・・・第２のタップ選択回路、１５６・・・特徴量抽出部、１５８・・・クラスコード生成部、１５９・・・遅延回路、１６０，１６３，１６５・・・正規方程式生成部、１６１，１６６・・・係数種データ決定部、１６２・・・係数種メモリ、１６４・・・係数データ決定部[0001]
BACKGROUND OF THE INVENTION
The present invention is an image signal processing apparatus and processing method suitable for application when converting a composite signal into a component signal, coefficient seed data generating apparatus and generating method used therefor, coefficient data generating apparatus and generating method Further, the present invention relates to a program for executing each method and a computer-readable medium on which the program is recorded.
[0002]
More specifically, the present invention generates pixel data of a target position in a component signal corresponding to the value of a parameter that determines the color tone of an image obtained by the component signal when the composite signal is directly converted into the component signal. Thus, the present invention relates to an image signal processing apparatus or the like that can satisfactorily adjust the color tone of an image obtained by a component signal.
[0003]
[Prior art]
In conventional composite-component conversion, for example, a NTSC (National Television System Committee) signal, which is a composite signal, is first separated into a Y signal (luminance signal) and a C signal (color signal) by a Y / C separation circuit. The C signal is color-demodulated and converted into baseband Y, RY, and BY component signals.
[0004]
For this reason, there has been a problem that the circuit scale of the configuration for processing increases. In addition, due to Y / C separation errors, there has been a problem that image quality deterioration such as dot interference and cross color tends to occur in the edge portion and moving image portion of the image.
[0005]
Accordingly, the present applicant extracts, for example, a plurality of pixel data located around the target position in the Y, RY, BY component signal from the NTSC signal, and based on the level distribution pattern of the plurality of pixel data. Thus, the class of the pixel data of the target position of the Y, RY, BY component signal is determined, and the pixel data of the target position in the Y, RY, BY component signal corresponding to this class is determined. An image information conversion device to be generated has been proposed (see Japanese Patent Application Laid-Open No. 2000-138949).
[0006]
[Problems to be solved by the invention]
This image information conversion device converts, for example, an NTSC signal directly into Y, RY, BY component signals, and does not use a Y / C separation circuit or a color demodulation circuit, and can reduce the circuit scale. In addition, it is possible to reduce image quality degradation such as dot interference and cross color due to Y / C separation errors.
[0007]
However, in this image information conversion apparatus, no consideration has been given to the adjustment of the color tone of the image by the Y, RY, and BY component signals.
[0008]
An object of the present invention is to provide an image signal processing apparatus and the like that can convert a composite signal directly into a component signal and can satisfactorily adjust the color tone of an image obtained by the component signal.
[0009]
[Means for Solving the Problems]
An image signal processing device according to the present invention is an image signal processing device that converts a composite signal made up of a plurality of pixel data into a component signal made up of a plurality of pixel data, and based on the composite signal, Based on the first data selection means for selecting a plurality of first pixel data located around the position of interest and the plurality of first pixel data selected by the first data selection means, the component signal Class detection means for detecting a class to which the pixel data of the target position belongs, parameter input means for inputting a parameter value for determining the color tone of the image obtained by the component signal, First storage means for storing coefficient seed data, which is coefficient data in a generation formula including the parameters, for generating coefficient data used in an estimation formula for calculating pixel data of the target position, and the first storage Coefficient data for generating coefficient data of each class corresponding to the parameter value based on the generation formula using the coefficient seed data stored in the means and the parameter value input by the parameter input means Generating means, second storage means for storing the coefficient data of each class generated by the coefficient data generating means, and the estimation corresponding to the class detected by the class detecting means from the second storage means Coefficient data reading means for reading and outputting the coefficient data of the equation, and the note in the component signal based on the composite signal. Second data selection means for selecting a plurality of second pixel data located around the position; coefficient data output by the coefficient data reading means; and a plurality of second data selection means selected by the second data selection means. Computing means obtained by calculating pixel data of a target position in the component signal based on the estimation formula using the pixel data of 2; Is provided.
[0011]
The image signal processing method according to the present invention is an image signal processing method for converting a composite signal composed of a plurality of pixel data into a component signal composed of a plurality of pixel data, wherein the component signal is based on the composite signal. A first step of selecting a plurality of first pixel data located in the vicinity of the target position in, and a target position in the component signal based on the plurality of first pixel data selected in the first step A second step of detecting a class to which the pixel data belongs, a third step of acquiring a value of a parameter that determines a color tone of an image obtained by the component signal, A fourth step of storing, for each class, coefficient seed data, which is coefficient data in a generation formula including the parameters, for generating coefficient data used in an estimation formula for calculating pixel data of the target position; Using the coefficient type data stored every time and the parameter value obtained in the third step, the coefficient data of each class corresponding to the parameter value is generated and stored based on the generation formula. And a sixth step of reading out and outputting the coefficient data of each class stored in the fifth step and corresponding to the class detected in the second step, And a seventh step of selecting a plurality of second pixel data located around the target position in the component signal based on the composite signal. And the coefficient data output in the sixth step and the plurality of second pixel data selected in the seventh step, and the pixel data of the target position in the component signal based on the estimation formula The eighth step of calculating Is provided.
[0012]
A program according to the present invention is for causing a computer to execute the above-described image signal processing method. A computer-readable medium according to the present invention records the above-described program.
[0013]
In the present invention, a plurality of first pixel data located around the target position in the component signal is selected from the composite signal, and the pixel data of the target position in the component signal is selected based on the plurality of first pixel data. The class to which it belongs is detected. For example, a level distribution pattern of a plurality of first pixel data is detected, and a class to which the pixel data of the target position belongs is detected based on the level distribution pattern.
[0014]
The parameter input means receives a parameter value that determines the color tone of the image obtained from the component signal. The pixel data of the target position is generated corresponding to the detected class and the input parameter value as described above.
[0015]
For example, coefficient seed data that is coefficient data in a generation formula for generating coefficient data used in an estimation formula obtained in advance for each class is stored in the storage unit, and the coefficient seed data and the input parameter Using the values, coefficient data of an estimation formula corresponding to the detected class and the input parameter value is generated. In addition, a plurality of second pixel data located around the target position in the component signal is selected from the composite signal. Using these coefficient data and the plurality of second pixel data, pixel data of the target position is generated based on the estimation formula.
[0016]
Further, for example, coefficient data of an estimation formula generated in advance for each combination of class and parameter values is stored in the storage unit. The coefficient data of the estimation formula corresponding to the detected class and the input parameter value is read from the storage means, and a plurality of second pixel data located around the target position in the component signal are selected from the composite signal Is done. Using these coefficient data and the plurality of second pixel data, pixel data of the target position is generated based on the estimation formula.
[0017]
As described above, when the composite signal is directly converted into the component signal, the pixel data of the target position in the component signal is generated corresponding to the value of the parameter that determines the color tone of the image obtained by the component signal. The color tone of the image obtained by the component signal can be adjusted well.
[0018]
The coefficient seed data generating device according to the present invention is for generating coefficient data used in an estimation formula used when converting a composite signal composed of a plurality of pixel data into a component signal composed of a plurality of pixel data. An apparatus for generating coefficient seed data, which is coefficient data in a generation formula, corresponding to a parameter in the generation formula, and a color tone of an image obtained by a student signal corresponding to the composite signal or a teacher signal corresponding to the component signal Parameter input means for inputting a value of a parameter for determining, a first data selection means for selecting a plurality of first pixel data located around the position of interest in the teacher signal based on the student signal; Based on the plurality of first pixel data selected by the first data selection means, Class detection means for detecting a class to which pixel data of the target position in the signal belongs, and second data for selecting a plurality of second pixel data located around the target position in the teacher signal based on the student signal A selection unit, a class detected by the class detection unit, and a plurality of second pixel data selected by the second data selection unit , Pixel data of the target position in the teacher signal And the value of the parameter input by the parameter input means And calculating means for obtaining the coefficient seed data for each class.
[0019]
The coefficient seed data generation method according to the present invention is for generating coefficient data used in an estimation formula used when converting a composite signal composed of a plurality of pixel data into a component signal composed of a plurality of pixel data. A method of generating coefficient seed data, which is coefficient data in a generation formula, corresponding to the parameters in the generation formula, and the color tone of an image obtained by a student signal corresponding to the composite signal or a teacher signal corresponding to the component signal A second step of selecting a plurality of first pixel data located around a target position in the teacher signal based on the student signal; and The attention in the teacher signal based on the plurality of first pixel data selected in step 2 A third step of detecting a class to which the pixel data of the position belongs, and a fourth step of selecting a plurality of second pixel data located around the target position in the teacher signal based on the student signal; The class detected in the third step, and a plurality of second pixel data selected in the fourth step , Pixel data of the target position in the teacher signal And the value of the parameter obtained in the first step And a fifth step for obtaining the coefficient seed data for each class.
[0020]
A program according to the present invention is for causing a computer to execute the coefficient seed data generation method described above. A computer-readable medium according to the present invention records the above-described program.
[0021]
In the present invention, parameter values that determine the color tone of an image obtained by a student signal corresponding to a composite signal or a teacher signal corresponding to a component signal are input.
[0022]
A plurality of first pixel data located around the target position in the teacher signal is selected from the student signal, and a class to which the pixel data at the target position in the teacher signal belongs is detected based on the plurality of first pixel data. Is done. In addition, a plurality of second pixel data located around the attention position in the teacher signal is selected from the student signal.
[0023]
Then, the value of the input parameter is changed step by step, the class to which the pixel data at the target position related to the teacher signal belongs, and a plurality of selected second pixel data , Pixel data of the target position in the teacher signal And the value of the entered parameter Is used to obtain coefficient seed data for each class.
[0024]
Here, the coefficient seed data is coefficient data in a generation expression including the above parameters, which generates coefficient data used in an estimation expression used when converting from a composite signal to a component signal. By using this coefficient seed data, it is possible to obtain coefficient data corresponding to the value of the parameter arbitrarily adjusted by the generation formula. Thereby, when converting directly from a composite signal to a component signal using an estimation formula, the user can satisfactorily adjust the color tone of an image obtained by the component signal by adjusting the parameter value.
[0025]
The coefficient data generation device according to the present invention is a device that generates coefficient data of an estimation formula used when converting a composite signal composed of a plurality of pixel data into a component signal composed of a plurality of pixel data, the above For generating coefficient data used in the estimation formula Parameter input means for inputting a parameter value for determining a color tone of an image obtained by a student signal corresponding to the composite signal or a teacher signal corresponding to the component signal corresponding to a parameter included in the generation formula; and the student signal And a plurality of first pixels selected by the first data selection means based on the first data selection means for selecting a plurality of first pixel data located around the target position in the teacher signal. Class detection means for detecting a class to which the pixel data at the target position in the teacher signal belongs based on the data, and a plurality of second pixel data located around the target position in the teacher signal based on the student signal The second data selecting means for selecting the class, the class detected by the class detecting means, and the second data selecting means -Option plurality of second pixel data , Pixel data of the target position in the teacher signal And the value of the parameter input by the parameter input means Use the above Every In addition, Coefficient seed data that is coefficient data in the generation formula is obtained and used in the estimation formula by the generation formula including the coefficient seed data and the parameter value. Arithmetic means for obtaining coefficient data.
[0026]
The coefficient data generation method according to the present invention is a method for generating coefficient data of an estimation formula used when converting a composite signal composed of a plurality of pixel data into a component signal composed of a plurality of pixel data, the above For generating coefficient data used in the estimation formula A first step of obtaining a value of a parameter for determining a color tone of an image obtained by a student signal corresponding to the composite signal or a teacher signal corresponding to the component signal corresponding to a parameter included in the generation formula; and the student signal Based on the second step of selecting a plurality of first pixel data located around the target position in the teacher signal, and on the basis of the plurality of first pixel data selected in the second step A third step of detecting a class to which the pixel data of the target position in the teacher signal belongs, and selecting a plurality of second pixel data located around the target position in the teacher signal based on the student signal. A fourth step, a class detected in the third step, and a plurality of second pixel data selected in the fourth step. , Pixel data of the target position in the teacher signal And the value of the parameter input by the parameter input means Use the above Every In addition, Coefficient seed data that is coefficient data in the generation formula is obtained and used in the estimation formula by the generation formula including the coefficient seed data and the parameter value. And a fifth step for obtaining coefficient data.
[0027]
A program according to the present invention is for causing a computer to execute the coefficient data generation method described above. A computer-readable medium according to the present invention records the above-described program.
[0028]
In the present invention, parameter values that determine the color tone of an image obtained by a student signal corresponding to a composite signal or a teacher signal corresponding to a component signal are input.
[0029]
A plurality of first pixel data located around the target position in the teacher signal is selected from the student signal, and a class to which the pixel data at the target position in the teacher signal belongs is detected based on the plurality of first pixel data. Is done. In addition, a plurality of second pixel data located around the attention position in the teacher signal is selected from the student signal.
[0030]
Then, the value of the input parameter is changed stepwise, and the class to which the pixel data of the target position in the teacher signal belongs, the selected second pixel data, and the pixel data of the target position in the teacher signal are used. Thus, coefficient data is obtained for each combination of class and parameter values.
[0031]
As described above, coefficient data of an estimation formula used when converting a composite signal into a component signal is generated. When the composite signal is converted into the component signal, the coefficient data corresponding to the class to which the pixel data at the target position in the component signal belongs and the value of the adjusted parameter is selectively used. Data is calculated. As a result, when the composite signal is directly converted from the composite signal using the estimation formula, the color tone of the image obtained by the component signal can be favorably adjusted by adjusting the parameter value.
[0032]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, embodiments of the present invention will be described with reference to the drawings. FIG. 1 shows a configuration of a television receiver 100 as an embodiment.
The television receiver 100 includes a microcomputer, and includes a system controller 101 for controlling the operation of the entire system, and a remote control signal receiving circuit 102 that receives a remote control signal. The remote control signal receiving circuit 102 is connected to the system controller 101, receives a remote control signal RM output from the remote control transmitter 200 according to a user operation, and supplies an operation signal corresponding to the signal RM to the system controller 101. Is configured to do.
[0033]
Further, the television receiver 100 is supplied with a receiving antenna 105 and a broadcast signal (RF modulated signal) captured by the receiving antenna 105, and performs a channel selection process, an intermediate frequency amplification process, a detection process, and the like to obtain a composite signal. And a buffer memory 108 for temporarily storing the NTSC signal output from the tuner 106.
[0034]
In addition, the television receiver 100 includes an image signal processing unit 110 that converts an NTSC signal into a component signal (Y, RY, BY component signal) including a luminance signal Y and color difference signals RY, BY. The display unit 111 for displaying an image based on the Y, RY, and BY component signals output from the image signal processing unit 110, and a display for displaying a character figure on the screen of the display unit 111 An OSD (On Screen Display) circuit 112 for generating the signal SCH and the display signal SCH are combined with the Y, RY, BY component signals output from the image signal processing unit 110 and displayed. And a synthesizer 113 for supplying to the unit 111.
[0035]
The display unit 111 is configured by a flat panel display such as a CRT (cathode-ray tube) display or an LCD (liquid crystal display). Further, the generation operation of the display signal SCH in the OSD circuit 112 is controlled by the system controller 101.
[0036]
The operation of the television receiver 100 shown in FIG. 1 will be described.
The NTSC signal output from the tuner 106 is supplied to the buffer memory 108 and temporarily stored. The NTSC signal temporarily stored in the buffer memory 108 is supplied to the image signal processing unit 110 and converted into Y, RY, and BY component signals.
[0037]
In other words, the image signal processing unit 110 obtains pixel data constituting each of the luminance signal Y and the color difference signals RY and BY from the pixel data constituting the NTSC signal. The Y, RY, BY component signals output from the image signal processing unit 110 are supplied to the display unit 111, and the Y, RY, BY component signals are displayed on the screen of the display unit 111. The image by is displayed.
[0038]
The user can adjust the color tone of the image displayed on the screen of the display unit 111 as described above by operating the remote control transmitter 200. In the image signal processing unit 110, as will be described later, pixel data of the luminance signal Y and the color difference signals RY and BY are calculated by an estimation formula. The coefficient data of this estimation formula corresponds to the parameter p that determines the color tone of the image based on the Y, RY, and BY component signals adjusted by the user's operation of the remote control transmitter 200. It is generated and used by a generation expression that contains Thereby, the color tone of the image by the Y, RY, BY component signal output from the image signal processing unit 110 corresponds to the adjusted parameter p.
[0039]
Next, details of the image signal processing unit 110 will be described. The image signal processing unit 110 selectively extracts and outputs a plurality of pixel data located around the target position in the Y, RY, and BY component signals from the NTSC signal stored in the buffer memory 108. First and second tap selection circuits 121 and 122 are provided.
[0040]
The first tap selection circuit 121 selectively extracts a plurality of pixel data of prediction taps used for prediction. The second tap selection circuit 122 selectively extracts a plurality of pixel data of class taps used for class classification. The pixel data of the prediction tap and the class tap are pixel data located around the position of interest in the Y, RY, and BY component signals, respectively.
[0041]
Here, the periphery of the target position means a position that is spatially (horizontal and vertical) and temporal (frame direction) close to the target position.
[0042]
FIG. 2A shows an example of the arrangement of prediction taps, and FIG. 2B shows an example of the arrangement of class taps. 2A and 2B, “◯” indicates the position of the tap, and “X” indicates the position of interest. In this example, seven pixel data in the current field (shown with a solid line) and the previous field (shown with a broken line) are extracted as pixel data of a prediction tap, and 13 in the current field (shown with a solid line) and the previous field (shown with a broken line) Pieces of pixel data are taken out as class tap pixel data.
[0043]
Returning to FIG. 1, the image signal processing unit 110 also includes a feature amount extraction unit 123 that extracts a feature amount from the pixel data of the class tap selectively extracted by the second tap selection circuit 122. The feature amount extraction unit 123 generates class information such as an ADRC code by performing processing such as 1-bit ADRC (Adaptive Dynamic Range Coding) on the pixel data of the class tap.
[0044]
ADRC calculates the maximum value and minimum value of a plurality of pixel data of a class tap, calculates a dynamic range that is the difference between the maximum value and the minimum value, and requantizes each pixel value in accordance with the dynamic range. . In the case of 1-bit ADRC, the pixel value is converted to 1 bit depending on whether it is larger or smaller than the average value of the plurality of pixel values of the class tap. The ADRC process is a process for making the number of classes representing the level distribution of pixel values relatively small. Therefore, not limited to ADRC, encoding that compresses the number of bits of a pixel value such as VQ (vector quantization) may be used.
[0045]
Further, the image signal processing unit 110 includes a class code generation unit 125 that generates a class code CL indicating the result of class classification based on the class information (such as ADRC code) generated by the feature amount extraction unit 123. Yes.
[0046]
The image signal processing unit 110 has a coefficient memory 126. The coefficient memory 126 stores, for each class, coefficient data Wi (i = 1 to n) used in an estimation formula used in an estimated prediction calculation circuit 127 described later. The coefficient data Wi is information for converting the NTSC signal into Y, RY, and BY component signals. The coefficient memory 126 includes coefficient data Wi for obtaining each of the luminance signal Y and the color difference signals RY and BY.
[0047]
The class code CL output from the class code generation unit 125 described above is supplied to the coefficient memory 126 as read address information. Coefficient data Wi (the luminance signal Y and the color difference signals RY and BY) corresponding to the class code CL is read from the coefficient memory 126 and supplied to the estimated prediction calculation circuit 127. Is done.
[0048]
The image signal processing unit 110 has an information memory bank 128. In the estimated prediction calculation circuit 127, the luminance signal Y and the color difference signals RY and BY are calculated from the pixel data xi of the prediction tap and the coefficient data Wi read from the coefficient memory 126 according to the estimation formula (1). Pixel data y at the target position in each is calculated. In the equation (1), n represents the number of prediction taps selected by the first tap selection circuit 121.
[0049]
[Expression 1]

[0050]
The coefficient data Wi of this estimation formula is generated by a generation formula including the parameter p as shown in the formula (2). In the information memory bank 128, coefficient seed data w which is coefficient data in this generation formula is stored. _i0 ~ W _i3 Is stored for each class.
Wi = w _i0 + W _i1 p + w _i2 p ² + W _i3 p ^Three ... (2)
[0051]
In addition, the image signal processing unit 110 performs coefficient seed data w for each class. _i0 ~ W _i3 (Each signal of luminance signal Y, color difference signals RY, BY) and the value of parameter p, and the coefficient data of the estimation formula corresponding to the value of parameter p for each class according to equation (2) A coefficient generation circuit 129 that generates Wi (the luminance signal Y and the color difference signals RY and BY) is included. The coefficient generation circuit 129 receives from the information memory bank 128 the coefficient seed data w of each class described above. _i0 ~ W _i3 Is loaded. The coefficient generation circuit 129 is supplied with the value of the parameter p from the system controller 101.
[0052]
The coefficient data Wi (i = 1 to n) of each class generated by the coefficient generation circuit 129 is stored in the coefficient memory 126 described above. The generation of coefficient data Wi for each class in the coefficient generation circuit 129 is performed, for example, in each vertical blanking period. Thereby, even if the value of the parameter p is changed by the user's operation of the remote control transmitter 200, the coefficient data Wi of each class stored in the coefficient memory 126 is immediately changed to the one corresponding to the value of the parameter p. The user can adjust the color tone smoothly.
[0053]
In addition, the image signal processing unit 110 generates a luminance signal Y and a color difference signal R− from the prediction tap pixel data xi selectively extracted by the first tap selection circuit 121 and the coefficient data Wi read from the coefficient memory 126. An estimated prediction calculation circuit 127 that calculates pixel data y at the target position in each of Y and BY is provided.
[0054]
Next, the operation of the image signal processing unit 110 will be described.
From the NTSC signal stored in the buffer memory 108, the second tap selection circuit 122 selectively extracts pixel data of class taps located around the target position in the Y, RY, BY component signals. It is. The pixel data of this class tap is supplied to the feature amount extraction unit 123. In the feature amount extraction unit 123, class tap pixel data is subjected to processing such as 1-bit ADRC, and class information such as ADRC is generated.
[0055]
Then, the class information (such as ADRC code) generated by the feature amount extraction unit 123 is supplied to the class code generation unit 125. The class code generation unit 125 generates a class code CL indicating the result of class classification based on the class information.
[0056]
This class code CL represents the detection result of the class to which the pixel data of the target position in the Y, RY, BY component signal belongs. The class code CL is supplied to the coefficient memory 126 as read address information. The coefficient memory 126 stores, for each class, coefficient seed data w for each class corresponding to the value of the parameter p adjusted by the user by the coefficient generation circuit 129 in each vertical blanking period. _i0 ~ W _i3 Coefficient data Wi (i = 1 to n) of the estimation formula is obtained based on (2) the color difference signal generation formula using (the luminance signal Y and the color difference signals RY and BY). Stored.
[0057]
As described above, the class code CL is supplied to the coefficient memory 126 as read address information, so that the coefficient data Wi (luminance signal Y, color difference signals RY, BY) corresponding to the class code CL is output from the coefficient memory 126. For each signal) is read and supplied to the estimated prediction calculation circuit 127. Also, the pixel data of the prediction tap located around the target position in the Y, RY, BY component signal is selectively selected by the first tap selection circuit 121 from the NTSC signal stored in the buffer memory 108. To be taken out.
[0058]
The estimated prediction calculation circuit 127 uses the pixel data xi of the prediction tap and the coefficient data Wi read from the coefficient memory 126, and based on the estimation formula (1), the luminance signal Y, the color difference signal RY, Pixel data y at the target position in each of BY is calculated. Therefore, the user can satisfactorily adjust the color tone of the image based on the Y, RY, and BY component signals by adjusting the value of the parameter p.
[0059]
Next, an example of a method for generating coefficient seed data will be described. In this example, coefficient seed data w which is coefficient data in the generation formula of the above-described formula (2). _i0 ~ W _i3 The example which calculates | requires is shown.
[0060]
Here, for the following explanation, as shown in equation (3), t _j (J = 0 to 3) is defined.
t ₀ = 1, t ₁ = P, t ₂ = P ² , T _Three = P ^Three ... (3)
Using this equation (3), equation (2) can be rewritten as equation (4).
[0061]
[Expression 2]

[0062]
Finally, the undetermined coefficient w _ij Ask for. That is, for each class, the coefficient value that minimizes the square error using the pixel data of the student signal corresponding to the NTSC signal and the pixel data of the teacher signal corresponding to the Y, RY, and BY component signals. To decide. This is a so-called least square method. The learning number is m, and the residual in the kth learning data (1 ≦ k ≦ m) is e. _k If the sum of squared errors is E, E is expressed by equation (5) using equations (1) and (2). Where x _ik Is the k-th pixel data at the i-th predicted tap position of the student signal, y _k Represents pixel data of the k-th teacher signal corresponding thereto.
[0063]
[Equation 3]

[0064]
In the least square method, w in equation (5) _ij W such that the partial differential due to becomes zero _ij Ask for. This is shown by equation (6).
[0065]
[Expression 4]

[0066]
Hereinafter, as shown in the equations (7) and (8), X _ipjq , Y _ip Is defined, equation (6) can be rewritten using a matrix like equation (9).
[0067]
[Equation 5]

[0068]
[Formula 6]

[0069]
This equation is generally called a normal equation. This normal equation can be calculated using the sweep-out method (Gauss-Jordan elimination method), etc. _ij The coefficient seed data is calculated.
[0070]
FIG. 3 shows an example of the concept of the above-described coefficient seed data generation method. A plurality of student signals corresponding to the NTSC signal are generated from the Y, RY, and BY component signals. For example, the parameter p is varied in nine steps to generate nine types of student signals. The coefficient seed data is generated by performing learning between the plurality of student signals and the teacher signal generated as described above.
[0071]
FIG. 4 shows coefficient seed data w stored in the information memory bank 128 of the digital broadcast receiver 100 described above. _i0 ~ W _i3 2 shows a configuration of a coefficient seed data generation device 150 for generating.
[0072]
The coefficient seed data generation device 150 includes an input terminal 151 to which a teacher signal ST corresponding to Y, RY, and BY component signals is input, and a color tone conversion circuit that performs a color tone conversion process on the teacher signal ST. 152. A value of the parameter p is input to the color tone conversion circuit 152.
[0073]
The tone conversion circuit 152 performs processing on the Y, RY, and BY component signals so that the tone (hue, saturation, etc.) changes in accordance with the value of the parameter p. For example, by changing the values of the color difference signals RY and BY by changing the angles of the RY axis and the BY axis, it is possible to realize a change in hue such as bluish to reddish.
[0074]
The coefficient seed data generation device 150 performs an encoding process on the Y, RY, and BY component signals processed by the color tone conversion circuit 152 to obtain an NTSC signal as the student signal SS. An encoder 153 is included.
[0075]
Further, the coefficient seed data generation device 150 selectively extracts and outputs a plurality of pixel data located around the target position in the teacher signal ST from the student signal SS output from the NTSC encoder 153. A circuit 154 and a second tap selection circuit 155 are included. The first and second tap selection circuits 154 and 155 are configured similarly to the first and second tap selection circuits 121 and 122 of the image signal processing unit 110 described above, respectively.
[0076]
In addition, the coefficient seed data generation device 150 includes a feature amount extraction unit 156 that extracts a feature amount from pixel data of class taps that are selectively extracted by the second tap selection circuit 155. The feature amount extraction unit 156 is configured in the same manner as the feature amount extraction unit 123 of the image signal processing unit 110 described above, and generates class information such as an ADRC code.
[0077]
In addition, the coefficient seed data generation device 150 includes a class code generation unit 158 that generates a class code CL indicating the result of class classification based on the class information (such as ADRC code) generated by the feature amount extraction unit 156. ing. The class code generation unit 158 is configured in the same manner as the class code generation unit 125 of the image signal processing unit 110 described above. The class code CL indicates a class to which the pixel data y at the target position in the teacher signal ST belongs.
[0078]
The coefficient seed data generation device 150 also includes a delay circuit 159 for adjusting the time of the teacher signal ST supplied to the input terminal 151, and each attention position obtained from the teacher signal ST adjusted in time by the delay circuit 159. Pixel data y, prediction tap pixel data xi selectively extracted by the first tap selection circuit 154 in correspondence with the pixel data y of each target position, the value of the parameter p, and each target position Coefficient seed data w for each class from the class code CL generated by the class code generating unit 158 corresponding to each pixel data y. _i0 ~ W _i3 A normal equation generating unit 160 for generating a normal equation (see equation (9)) for obtaining
[0079]
In this case, one piece of learning data is generated by a combination of one piece of pixel data y and pixel data xi of n prediction taps corresponding to the piece of pixel data y. The student signal SS is sequentially generated, and learning data is generated between the teacher signal and each student signal. Accordingly, the normal equation generation unit 160 generates a normal equation in which a lot of learning data relating to a plurality of values of the parameter p is registered, and the coefficient seed data w _i0 ~ W _i3 Can be obtained.
[0080]
Coefficient seed data w _i0 ~ W _i3 As described above, it is necessary to obtain signals corresponding to the luminance signal Y and the color difference signals RY and BY constituting the teacher signal ST. Therefore, the normal equation generation unit 160 generates a normal equation corresponding to each of the luminance signal Y and the color difference signals RY and BY for each class. In this case, the normal equation corresponding to the luminance signal Y is generated by using the pixel data of the luminance signal Y as the pixel data y of the target position. Similarly, the normal equations corresponding to the color difference signals RY and BY are generated by using the pixel data of the color difference signals RY and BY as the pixel data y of the target position.
[0081]
Further, the coefficient seed data generation device 150 is supplied with data of normal equations generated for each class by the normal equation generation unit 160, solves the normal equations, and generates coefficient seed data w for each class. _i0 ~ W _i3 Is provided with a coefficient seed data determining unit 161 and a coefficient seed memory 162 for storing the obtained coefficient seed data. In the coefficient seed data determination unit 161, the normal equation is solved, for example, by a sweeping method, and coefficient coefficient data is obtained.
[0082]
The operation of the coefficient seed data generation device 150 shown in FIG. 4 will be described.
The input terminal 151 is supplied with Y, RY, BY component signals as teacher signals ST. The Y, RY, BY component signals are supplied to the color tone conversion circuit 152. A value of the parameter p is input to the color tone conversion circuit 152. The tone conversion circuit 152 performs processing on the Y, RY, and BY component signals so that the tone (hue, saturation, etc.) changes in accordance with the value of the parameter p.
[0083]
The Y, RY, BY component signals processed by the color tone conversion circuit 152 in this way are supplied to the NTSC encoder 153. The NTSC encoder 153 encodes the Y, RY, and BY component signals to generate an NTSC signal as the student signal SS.
[0084]
Also, from the student signal SS output from the NTSC encoder 153, the second tap selection circuit 155 selectively extracts pixel data of class taps located around the target position in the teacher signal ST. The pixel data of this class tap is supplied to the feature amount extraction unit 156. The feature amount extraction unit 156 generates class information such as an ADRC code based on the pixel data of the class tap.
[0085]
Then, the class information (such as ADRC code) generated by the feature amount extraction unit 156 is supplied to the class code generation unit 158. In this class code generation unit 158, based on the class information, a class code CL indicating the class (class classification result) to which the pixel data y at the target position in the teacher signal ST belongs is generated.
[0086]
Further, from the student signal SS output from the NTSC encoder 153, the first tap selection circuit 154 selectively extracts pixel data of prediction taps located around the target position in the teacher signal ST.
[0087]
Then, the pixel data y of each target position obtained from the teacher signal ST time-adjusted by the delay circuit 159 and the first tap selection circuit 154 selectively corresponding to the pixel data y of each target position. The normal equation generation unit 160 uses the predicted tap pixel data xi, the value of the parameter p, and the class code CL generated by the class code generation unit 158 corresponding to the pixel data y of each target position. Coefficient seed data w for each class _i0 ~ W _i3 A normal equation (see equation (9)) is obtained.
[0088]
Then, the coefficient seed data determination unit 161 solves each normal equation, and the coefficient seed data w of each class. _i0 ~ W _i3 (The luminance signal Y and the color difference signals RY and BY) are obtained and their coefficient seed data w _i0 ~ W _i3 Is stored in the coefficient seed memory 162.
[0089]
As described above, the coefficient seed data generation device 150 shown in FIG. 4 uses the estimation formula (see formula (1)) for each class stored in the information memory bank 128 of the image signal processing unit 110 in FIG. Coefficient data w which is coefficient data in a generation formula (see formula (2)) for obtaining the coefficient data Wi _i0 ~ W _i3 Can be generated.
[0090]
Next, another example of the coefficient seed data generation method will be described. Also in this example, coefficient seed data w which is coefficient data in the generation formula of the above-described formula (2) _i0 ~ W _i3 The example which calculates | requires is shown.
[0091]
FIG. 5 shows the concept of this example. A plurality of student signals are generated from the teacher signal. For example, the value of the parameter p is varied in 9 steps to generate 9 types of student signals. Learning is performed between each student signal and teacher signal generated in this way, and coefficient data Wi of the estimation formula of Formula (1) is generated for each value of the parameter p. Then, using the coefficient data Wi generated corresponding to each value of the parameter p, coefficient seed data w which is coefficient data in the generation expression of the expression (2) _i0 ~ W _i3 Is generated.
[0092]
First, how to obtain coefficient data of the estimation formula will be described. Here, an example is shown in which the coefficient data Wi (i = 1 to n) of the estimation formula (1) is obtained by the least square method.
[0093]
In the above equation (1), before learning, coefficient data W ₁ , W ₂ , ..., W _n Is an undetermined coefficient. Learning is performed on a plurality of signal data for each class. When the number of learning data is m, the following equation (12) is set according to equation (1). n indicates the number of prediction taps.
[0094]
y _k = W ₁ X _k1 + W ₂ X _k2 + ... + W _n X _kn (10)
(K = 1, 2,..., M)
If m> n, coefficient data W ₁ , W ₂ , ..., W _n Is not uniquely determined, so the element e of the error vector e _k Is defined by the following equation (11), and e in equation (12) ² Find coefficient data that minimizes. Coefficient data is uniquely determined by a so-called least square method.
e _k = Y _k -{W ₁ X _k1 + W ₂ X _k2 + ... + w _n X _kn } (11)
(K = 1, 2, ... m)
[0095]
[Expression 7]

[0096]
E in equation (12) ² As a practical calculation method for obtaining coefficient data that minimizes the value, first, as shown in the equation (13), e ² Is partially differentiated with the coefficient data Wi (i = 1, 2,..., N), and the coefficient data Wi may be obtained so that the partial differential value becomes 0 for each value of i.
[0097]
[Equation 8]

[0098]
A specific procedure for obtaining the coefficient data Wi from the equation (13) will be described. If Xji and Yi are defined as in equations (14) and (15), equation (13) can be written in the form of a determinant of equation (16).
[0099]
[Equation 9]

[0100]
[Expression 10]

[0101]
Equation (16) is generally called a normal equation. Coefficient data Wi (i = 1, 2,..., N) can be obtained by solving this normal equation by a general solution method such as a sweep-out method (Gauss-Jordan elimination method).
[0102]
Next, how to obtain coefficient seed data using coefficient data Wi generated corresponding to each student signal will be described.
The coefficient data of a certain class obtained by learning using the student signal corresponding to the value of the parameter p is k _pi Suppose that Here, i is the number of the prediction tap. This k _pi From this, coefficient class data of this class is obtained.
[0103]
The coefficient data Wi (i = 1 to n) is the coefficient seed data w _i0 ~ W _i3 Is expressed by the above-described equation (2). Here, considering that the least square method is used for the coefficient data Wi, the residual is expressed by Equation (17).
[0104]
[Expression 11]

[0105]
Where t _j Is shown in the above equation (3). When the least square method is applied to equation (17), equation (18) is obtained.
[0106]
[Expression 12]

[0107]
Where X _jk , Y _j Are defined as equations (19) and (20), respectively, equation (18) can be rewritten as equation (21). This equation (21) is also a normal equation, and by solving this equation with a general solution such as a sweep-out method, coefficient seed data w _i0 ~ W _i3 Can be calculated.
[0108]
[Formula 13]

[0109]
FIG. 6 shows a configuration of a coefficient seed data generation device 150A that generates coefficient seed data based on the concept shown in FIG. In FIG. 6, parts corresponding to those in FIG. 4 are denoted by the same reference numerals, and detailed description thereof is omitted.
[0110]
The coefficient seed data generation device 150A includes pixel data y at each position of interest obtained from the teacher signal ST time-adjusted by the delay circuit 159, and a first tap selection circuit corresponding to the pixel data y at each position of interest. From the pixel data xi of the prediction tap selectively extracted in 154 and the class code CL generated by the class code generation unit 158 corresponding to the pixel data y of each target position, coefficient data Wi ( A normal equation generating unit 163 that generates a normal equation (see equation (16)) for obtaining i = 1 to n) is provided.
[0111]
In this case, one piece of learning data is generated by a combination of one piece of pixel data y and pixel data xi of n prediction taps corresponding to the piece of pixel data y. The student signal SS is sequentially generated, and learning data is generated between the teacher signal and each student signal. Thus, the normal equation generation unit 163 generates a normal equation for obtaining coefficient data Wi (i = 1 to n) for each class corresponding to each value of the parameter p.
[0112]
Note that it is necessary to obtain the coefficient data Wi corresponding to each of the luminance signal Y and the color difference signals RY and BY constituting the teacher signal ST. Accordingly, the normal equation generation unit 163 generates a normal equation corresponding to each of the luminance signal Y and the color difference signals RY and BY for each class. In this case, the normal equation corresponding to the luminance signal Y is generated by using the pixel data of the luminance signal Y as the pixel data y of the target position. Similarly, the normal equations corresponding to the color difference signals RY and BY are generated by using the pixel data of the color difference signals RY and BY as the pixel data y of the target position.
[0113]
Further, the coefficient seed data generation device 150A is supplied with the data of the normal equation generated by the normal equation generation unit 163, solves the normal equation, and corresponds to each value of the parameter p, and the coefficient data Wi of each class. For each class using the coefficient data determination unit 164 for determining the coefficient p and the coefficient data Wi corresponding to each of the student signals. _i0 ~ W _i3 A normal equation generation unit 165 that generates a normal equation (see equation (21)) for obtaining Also in this case, a normal equation is generated corresponding to each of the luminance signal Y and the color difference signals RY and BY.
[0114]
Further, the coefficient seed data generation device 150A is supplied with the data of the normal equation generated for each class by the normal equation generation unit 165, solves the normal equation for each class, and generates coefficient seed data w for each class. _i0 ~ W _i3 The coefficient seed data determination unit 166 for determining the coefficient seed data w _i0 ~ W _i3 And a coefficient seed memory 162 for storing.
[0115]
The rest of the coefficient seed data generation device 150A shown in FIG. 6 is configured in the same manner as the coefficient seed data generation device 150 shown in FIG.
[0116]
Next, the operation of the coefficient seed data generation device 150A shown in FIG. 6 will be described.
The input terminal 151 is supplied with Y, RY, BY component signals as teacher signals ST. The Y, RY, BY component signals are supplied to the color tone conversion circuit 152. A value of the parameter p is input to the color tone conversion circuit 152. The tone conversion circuit 152 performs processing on the Y, RY, and BY component signals so that the tone (hue, saturation, etc.) changes in accordance with the value of the parameter p.
[0117]
The Y, RY, BY component signals processed by the color tone conversion circuit 152 in this way are supplied to the NTSC encoder 153. The NTSC encoder 153 encodes the Y, RY, and BY component signals to generate an NTSC signal as the student signal SS.
[0118]
Also, from the student signal SS output from the NTSC encoder 153, the second tap selection circuit 155 selectively extracts pixel data of class taps located around the target position in the teacher signal ST. The pixel data of this class tap is supplied to the feature amount extraction unit 156. The feature amount extraction unit 156 generates class information such as an ADRC code based on the pixel data of the class tap.
[0119]
Then, the class information (such as ADRC code) generated by the feature amount extraction unit 156 is supplied to the class code generation unit 158. In this class code generation unit 158, based on the class information, a class code CL indicating the class (class classification result) to which the pixel data y at the target position in the teacher signal ST belongs is generated.
[0120]
Further, from the student signal SS output from the NTSC encoder 153, the first tap selection circuit 154 selectively extracts pixel data of prediction taps located around the target position in the teacher signal ST.
[0121]
Then, the pixel data y of each target position obtained from the teacher signal ST time-adjusted by the delay circuit 159 and the first tap selection circuit 154 selectively corresponding to the pixel data y of each target position. The normal equation generation unit 163 uses the predicted tap pixel data xi, the value of the parameter p, and the class code CL generated by the class code generation unit 158 corresponding to the pixel data y of each target position. For each student signal SS corresponding to each value of the parameter p, a normal equation (see equation (16)) for generating coefficient data Wi (i = 1 to n) is generated for each class.
[0122]
Then, the coefficient data determination unit 164 solves the normal equation, and the coefficient data Wi of each class corresponding to each value of the parameter p is obtained. The normal equation generation unit 165 uses the coefficient data Wi of each class corresponding to each value of the parameter p, and uses the coefficient seed data w for each class. _i0 ~ W _i3 A normal equation (see equation (21)) is obtained.
[0123]
Then, the coefficient seed data determining unit 166 solves the normal equation, and the coefficient seed data w of each class. _i0 ~ W _i3 (The luminance signal Y and the color difference signals RY and BY) are obtained and the coefficient seed data w _i0 ~ W _i3 Is stored in the coefficient seed memory 162.
[0124]
As described above, also in the coefficient seed data generation device 150A shown in FIG. 6, the coefficient seed data w of each class stored in the information memory bank 128 of the image signal processing unit 110 in FIG. _i0 ~ W _i3 Can be generated.
[0125]
In the coefficient seed data generation devices 150 and 150A shown in FIGS. 4 and 6, the color tone conversion circuit 152 is applied to the Y, RY, and BY component signals as the teacher signal ST supplied to the input terminal 151. Then, processing is performed so that the color tone changes in accordance with the value of the parameter p, and NTSC signals as a plurality of student signals SS respectively corresponding to the plurality of values of the parameter p are obtained from the NTSC encoder 153. The coefficient seed data w is obtained by learning between the signal ST and the plurality of student signals SS. _i0 ~ W _i3 Showed what you get.
[0126]
However, conversely, the coefficient seed data w is similarly obtained by learning between a plurality of teacher signals ST and one student signal SS. _i0 ~ W _i3 Can be requested. FIG. 7 shows the configuration of the coefficient seed data generation device 150B in that case. 7, parts corresponding to those in FIG. 4 are denoted by the same reference numerals, and detailed description thereof is omitted.
[0127]
In this coefficient seed data generation device 150B, the Y, RY, BY component signals supplied to the input terminal 151 are directly supplied to the NTSC encoder 153. The NTSC encoder 153 encodes the Y, RY, and BY component signals to generate an NTSC signal as the student signal SS.
[0128]
In the coefficient seed data generation device 150B, the Y, RY, and BY component signals supplied to the input terminal 151 are supplied to the color tone conversion circuit 157. The tone conversion circuit 157 is also supplied with the value of the parameter p. The tone conversion circuit 157 performs processing on the Y, RY, and BY component signals so that the tone (hue, saturation, etc.) changes in accordance with the value of the parameter p. From the color tone conversion circuit 157, Y, RY, and BY component signals are obtained as a plurality of teacher signals ST respectively corresponding to a plurality of values of the parameter p.
[0129]
Then, in the normal equation generation unit 160, the pixel data y of each target position obtained from the teacher signal ST output from the tone conversion circuit 157, and the first tap selection corresponding to the pixel data y of each target position, respectively. The pixel data xi of the prediction tap selectively extracted by the circuit 154, the value of the parameter p, and the class code CL generated by the class code generation unit 158 corresponding to the pixel data y of each target position are used. Coefficient seed data w for each class _i0 ~ W _i3 A normal equation (see equation (9)) is obtained.
[0130]
Other configurations are the same as those of the coefficient seed data generating apparatus 150 shown in FIG. The time adjustment between the student signal SS and the teacher signal ST is performed by, for example, a delay circuit (not shown) provided in the color tone conversion circuit 157.
[0131]
As described above, the normal equation generation unit 160 of the coefficient seed data generation device 150B shown in FIG. 7 generates a normal equation by learning between a plurality of teacher signals ST and one student signal SS, and the coefficients shown in FIG. Similar to the seed data generation device 150, the coefficient seed data w _i0 ~ W _i3 Can be generated.
[0132]
Further, the coefficient seed data generation devices 150, 150A, and 150B shown in FIGS. 4, 6, and 7 include a color tone conversion circuit 152 or a color tone conversion circuit 157, whereby a plurality of student signals SS or a plurality of teacher signals are provided. ST was generated. However, a plurality of student signals SS or a plurality of teacher signals ST corresponding to the value of the parameter p may be input from the outside. FIG. 8 shows the configuration of the coefficient seed data generation device 150C in that case. 7, parts corresponding to those in FIG. 4 are denoted by the same reference numerals, and detailed description thereof is omitted.
[0133]
In the coefficient seed data generation device 150C, Y, RY, BY component signals are input to the input terminal 151T and the input terminal 151S. The Y, RY, and BY component signals input to the input terminal 151T or the input terminal 151S are assumed to change in color tone according to the value of the parameter p. The Y, RY, and BY component signals input to one of the input terminals 151T and 151S are obtained, for example, by being imaged by the same video camera. In order to obtain a color tone corresponding to the value of the parameter p, for example, color tone conversion processing is performed by a color tone conversion circuit built in the video camera, or imaging is performed by changing the imaging environment.
[0134]
The Y, RY, and BY component signals input to the input terminal 151T become the teacher signal ST. The Y, RY, BY component signals input to the input terminal 151S are supplied to the NTSC encoder 153. The NTSC encoder 153 encodes the Y, RY, and BY component signals to generate an NTSC signal as the student signal SS.
Other configurations are the same as those of the coefficient seed data generating apparatus 150 shown in FIG.
[0135]
In the normal equation generation unit 160 of the coefficient seed data generation apparatus 150C shown in FIG. 8, learning between one teacher signal ST and a plurality of student signals SS, or a plurality of teacher signals ST and one student signal SS, A normal equation is generated by learning between the coefficient seed data w and the coefficient seed data w as in the coefficient seed data generation device 150 in FIG. 4 or the coefficient seed data generation device 150B in FIG. _i0 ~ W _i3 Can be generated.
[0136]
The coefficient seed data generation device 150B in FIG. 7 and the coefficient seed data generation device 150C in FIG. 8 are each based on the coefficient seed data generation device 150 shown in FIG. 4, but the coefficient seed data shown in FIG. An apparatus based on the generation device 150A can be configured in the same manner.
[0137]
In the image signal processing unit 110 of the television receiver 100 shown in FIG. 1, the coefficient seed data w of each class is stored in the information memory bank 128. _i0 ~ W _i3 And the coefficient generation circuit 129 stores the coefficient seed data w for each class. _i0 ~ W _i3 Is used to generate coefficient data Wi of each class corresponding to the value of the parameter p and store it in the coefficient memory 126.
[0138]
However, the coefficient data Wi corresponding to each combination of the class and the parameter p is stored in the information memory bank 128, and the coefficient data Wi of each class corresponding to the value of the parameter p is read from the information memory bank 128, This may be stored in the coefficient memory 126. In this case, the coefficient data Wi corresponding to each combination of the class and the value of the parameter p stored in the information memory bank 128 can be obtained from the coefficient data determination unit 164 in the coefficient seed data generation device 150A shown in FIG. it can.
[0139]
Note that the processing in the image signal processing unit 110 in FIG. 1 can be realized by software, for example, by an image signal processing apparatus 300 as shown in FIG. First, the image signal processing apparatus 300 shown in FIG. 9 will be described. The image signal processing apparatus 300 includes a CPU 301 that controls the operation of the entire apparatus, a ROM (read only memory) 302 that stores an operation program of the CPU 301, coefficient seed data, and the like, and a RAM ( random access memory) 303. These CPU 301, ROM 302, and RAM 303 are each connected to a bus 304.
[0140]
The image signal processing apparatus 300 also includes a hard disk drive (HDD) 305 as an external storage device and a drive (FDD) 307 that drives a floppy (registered trademark) disk 306. These drives 305 and 307 are each connected to a bus 304.
[0141]
In addition, the image signal processing apparatus 300 includes a communication unit 308 that is connected to a communication network 400 such as the Internet by wire or wirelessly. The communication unit 308 is connected to the bus 304 via the interface 309.
[0142]
In addition, the image signal processing device 300 includes a user interface unit. The user interface unit includes a remote control signal receiving circuit 310 that receives a remote control signal RM from the remote control transmitter 200, and a display 311 that includes an LCD (liquid crystal display) or the like. The receiving circuit 310 is connected to the bus 304 via the interface 312, and similarly the display 311 is connected to the bus 304 via the interface 313.
[0143]
The image signal processing apparatus 300 has an input terminal 314 for inputting an NTSC signal and an output terminal 315 for outputting a Y, RY, BY component signal. The input terminal 314 is connected to the bus 304 via the interface 316, and similarly, the output terminal 315 is connected to the bus 304 via the interface 317.
[0144]
Here, instead of storing the processing program, coefficient seed data, and the like in the ROM 302 in advance as described above, for example, they are downloaded from the communication network 400 such as the Internet via the communication unit 308 and stored in the hard disk or RAM 303 for use. You can also Further, these processing programs, coefficient seed data, and the like may be provided on a floppy (registered trademark) disk 306.
[0145]
Further, instead of inputting the NTSC signal to be processed from the input terminal 314, it may be recorded in the hard disk drive 307 in advance or downloaded from the communication network 400 such as the Internet via the communication unit 308. Further, instead of outputting the processed Y, RY, and BY component signals to the output terminal 315 or in parallel therewith, they are supplied to the display 311 to display an image, and further stored in a hard disk. You may make it send out to the communication networks 400, such as the internet, via the communication part 308. FIG.
[0146]
A processing procedure for obtaining Y, RY, BY component signals from NTSC signals in the image signal processing apparatus 300 shown in FIG. 9 will be described with reference to the flowchart of FIG.
[0147]
First, in step ST1, processing is started. In step ST2, for example, an NTSC signal for one frame or one field is input into the apparatus from the input terminal 314. Thus, the pixel data constituting the NTSC signal input from the input terminal 314 is temporarily stored in the RAM 303. If the NTSC signal is recorded in advance in the hard disk drive 307 in the apparatus, the NTSC signal is read from the drive 307 and the pixel data constituting the NTSC signal is temporarily stored in the RAM 303.
[0148]
In step ST3, it is determined whether or not processing of all frames or fields of the NTSC signal has been completed. When the process is finished, the process ends in step ST4. On the other hand, when the process is not finished, the process proceeds to step ST5.
[0149]
In this step ST5, the value of the parameter p, which is a color tone designation value input by the user's operation of the remote control transmitter 200, is obtained from the RAM 303, for example. In step ST6, using the value of the parameter p and the coefficient seed data of each class, the coefficient data Wi of the estimation formula (see formula (1)) of each class is generated by a generation formula (for example, formula (2)). To do.
[0150]
Next, in step ST7, the pixel data of the class tap and the prediction tap are acquired from the NTSC signal input in step ST2 corresponding to the target position in the Y, RY, BY component signal. In step ST8, Y, RY, BY component signals (luminance signal Y, chrominance signal RY) are obtained in all regions of the NTSC signal pixel data for one frame or one field input in step ST2. , BY) is determined whether or not the processing for obtaining the pixel data is completed. If completed, the process returns to step ST2, and the process proceeds to the next one frame or one field worth of NTSC signal input processing. On the other hand, when the process has not ended, the process proceeds to step ST9.
[0151]
In step ST9, a class code CL is generated from the pixel data of the class tap acquired in step ST7. In step ST10, using the coefficient data Wi corresponding to the class code CL and the pixel data of the prediction tap, the pixel data of the target position in the Y, RY, BY component signal is generated by the estimation formula. Then, the process returns to step ST7, and the same processing as described above is repeated.
[0152]
In this way, by performing processing according to the flowchart shown in FIG. 10, it is possible to process pixel data of the input NTSC signal and obtain pixel data of Y, RY, BY component signals. . As described above, the Y, RY, BY component signals obtained by such processing are output to the output terminal 315, supplied to the display 311 to display an image, and further. It is supplied to the hard disk drive 305 and recorded on the hard disk.
[0153]
If the ROM 302 stores coefficient data of each combination of class and parameter p values instead of the coefficient seed data of each class, each class corresponding to the value of the parameter p from the ROM 302 is stored in step ST6. The coefficient data Wi of the estimation formula (see formula (1)) is read out.
Further, although illustration of the processing device is omitted, the processing in the coefficient seed data generation device 150 in FIG. 4 can also be realized by software.
[0154]
A processing procedure for generating coefficient seed data will be described with reference to the flowchart of FIG.
First, in step ST21, processing is started, and in step ST22, a color tone pattern (specified by the value of parameter p) used for learning is selected. In step ST23, it is determined whether or not learning has been completed for all tone patterns. If learning has not been completed for all tone patterns, the process proceeds to step ST24.
[0155]
In step ST24, teacher signals (Y, RY, BY component signals) are input for one frame or one field. In step ST25, it is determined whether or not the processing of all the frames or fields of the teacher signal has been completed. When the process is completed, the process returns to step ST22, the next color tone pattern is selected, and the same process as described above is repeated. On the other hand, when not completed, the process proceeds to step ST26.
[0156]
In step ST26, a student signal (NTSC signal) corresponding to the tone pattern selected in step ST22 is generated from the teacher signal input in step ST24. In step ST27, the pixel data of the class tap and the prediction tap are acquired from the student signal generated in step ST26 corresponding to the attention position in the teacher signal input in step ST24. In step ST28, it is determined whether or not the learning process has been completed in all regions of the teacher signal input in step ST24. When the learning process is completed, the process returns to step ST24, the teacher signal for the next one frame or one field is input, and the same process as described above is repeated. On the other hand, when the process has not ended, the process proceeds to step ST29.
[0157]
In step ST29, the class code CL is generated from the pixel data of the class tap acquired in step ST27. In step ST30, a normal equation (see equation (9)) is generated. Thereafter, the process returns to step ST27.
[0158]
In step ST23, when learning is completed for all the color patterns, the process proceeds to step ST31. In this step ST31, the coefficient seed data of each class is calculated by solving the normal equation by a sweeping method or the like. In step ST32, the coefficient seed data is stored in the memory, and then the process is terminated in step ST33. .
[0159]
In this way, by performing the processing according to the flowchart shown in FIG. 11, coefficient seed data can be obtained by the same method as the coefficient seed data generating apparatus 150 shown in FIG.
[0160]
Although illustration of the processing device is omitted, the processing in the coefficient seed data generation device 150A in FIG. 6 can also be realized by software.
[0161]
A processing procedure for generating coefficient seed data will be described with reference to the flowchart of FIG.
First, in step ST41, processing is started, and in step ST42, a color tone pattern (specified by the value of parameter p) used for learning is selected. In step ST43, it is determined whether or not learning has been completed for all tone patterns. If learning has not been completed for all the tone patterns, the process proceeds to step ST44.
[0162]
In this step ST44, a teacher signal (Y, RY, BY component signal) is input for one frame or one field. In step ST45, it is determined whether or not the processing of all the frames or fields of the teacher signal has been completed. If not finished, in step ST46, a student signal (NTSC signal) corresponding to the tone pattern selected in step ST42 is generated from the teacher signal input in step ST44.
[0163]
In step ST47, the pixel data of the class tap and the prediction tap are acquired from the student signal generated in step ST46 corresponding to the attention position in the teacher signal input in step ST44. In step ST48, it is determined whether or not the learning process has been completed in all regions of the teacher signal input in step ST44. When the learning process is finished, the process returns to step ST44, the teacher signal for the next one frame or one field is input, and the same process as described above is repeated. On the other hand, when the learning process is not ended, the process proceeds to step ST49.
[0164]
In step ST49, a class code CL is generated from the pixel data of the class tap acquired in step ST47. In step ST50, a normal equation for obtaining coefficient data (see equation (16)) is generated. Thereafter, the process returns to step ST47.
[0165]
When the processing is completed in step ST45 described above, in step ST51, the normal equation generated in step ST50 is solved by a sweeping method or the like to calculate coefficient data for each class. Thereafter, the process returns to step ST42, the next tone pattern is selected, the same processing as described above is repeated, and the coefficient data of each class corresponding to the next tone pattern is obtained.
[0166]
Further, when the coefficient data calculation processing for all the color patterns is completed in step ST43 described above, the process proceeds to step ST52. At this point, coefficient data for each combination of class and parameter p values has been generated. In step ST52, a normal equation (see equation (21)) for obtaining coefficient seed data is generated from coefficient data for all tone patterns.
[0167]
Then, in step ST53, the coefficient equation data of each class is calculated by solving the normal equation generated in step ST52 by a sweeping method or the like, and in step ST54, the coefficient species data is stored in the memory, and then the step In ST55, the process ends.
[0168]
In this way, by performing processing according to the flowchart shown in FIG. 12, coefficient seed data can be obtained by the same method as the coefficient seed data generating apparatus 150A shown in FIG.
[0169]
In the image signal processing unit 110 in FIG. 1, the generation formula (2) is used to generate the coefficient data Wi (i = 1 to n), but a polynomial having a different order or another function is used. It can also be realized with an expression.
[0170]
In addition, the image signal processing unit 110 in FIG. 1 shows the one that converts the NTSC signal that is a composite signal into the luminance signal Y and the color difference signals RY and BY that are component signals. However, the combination of the composite signal and the component signal is not limited to this. For example, another composite signal includes a PAL (phase alternating by line) signal. As other component signals, there are three primary color signals of red, green and blue.
[0171]
【The invention's effect】
According to the present invention, when the composite signal is directly converted into the component signal, the pixel data of the target position in the component signal is generated corresponding to the value of the parameter that determines the color tone of the image obtained from the component signal. Therefore, it is possible to satisfactorily adjust the color tone of the image obtained by the component signal.
[0172]
Further, according to the present invention, when the composite signal is directly converted into the component signal, the coefficient seed data or the coefficient data used for adjusting the color tone of the image obtained by the component signal according to the parameter value is excellent. Can get to.
[Brief description of the drawings]
FIG. 1 is a block diagram illustrating a configuration of a television receiver as an embodiment.
FIG. 2 is a diagram illustrating an example of a prediction tap and a class tap.
FIG. 3 is a diagram for explaining an example of a generation method of coefficient seed data.
FIG. 4 is a block diagram illustrating a configuration example of a coefficient seed data generation apparatus.
FIG. 5 is a diagram for explaining another example of the coefficient seed data generation method.
FIG. 6 is a block diagram illustrating a configuration example of a coefficient seed data generation apparatus.
FIG. 7 is a block diagram illustrating a configuration example of a coefficient seed data generation apparatus.
FIG. 8 is a block diagram illustrating a configuration example of a coefficient seed data generation apparatus.
FIG. 9 is a block diagram illustrating a configuration example of an image signal processing device to be realized by software.
FIG. 10 is a flowchart showing image signal processing.
FIG. 11 is a flowchart illustrating a coefficient seed data generation process (part 1).
FIG. 12 is a flowchart showing a coefficient seed data generation process (No. 2).
[Explanation of symbols]
DESCRIPTION OF SYMBOLS 100 ... Television receiver, 101 ... System controller, 102 ... Remote control signal receiving circuit, 105 ... Receiving antenna, 106 ... Tuner, 108 ... Buffer memory, 110 ... Image signal Processing unit 111 ... Display unit 112 ... OSD circuit 113 ... Synthesizer 121 ... First tap selection circuit 122 ... Second tap selection circuit 123 ... Feature amount extraction unit, 125 ... class code generation unit, 126 ... coefficient memory, 127 ... estimated prediction calculation circuit, 128 ... information memory bank, 129 ... coefficient generation circuit, 150, 150A, 150B, 150C ... Coefficient seed data generator, 151, 151T, 151S ... Input terminal, 152, 157 ... Color tone conversion circuit, 153 ... NTSC 155... First tap selection circuit, 155... Second tap selection circuit, 156... Feature amount extraction unit, 158... Class code generation unit, 159. , 163, 165 ... normal equation generation unit, 161, 166 ... coefficient seed data determination unit, 162 ... coefficient seed memory, 164 ... coefficient data determination unit

Claims (12)

An image signal processing device that converts a composite signal composed of a plurality of pixel data into a component signal composed of a plurality of pixel data,
First data selection means for selecting a plurality of first pixel data located around a target position in the component signal based on the composite signal;
Class detection means for detecting a class to which pixel data of a target position in the component signal belongs based on a plurality of first pixel data selected by the first data selection means;
Parameter input means for inputting a parameter value for determining the color tone of the image obtained by the component signal;
First storage means for storing coefficient seed data, which is coefficient data in a generation formula including the parameter, for generating coefficient data used in an estimation formula for calculating pixel data of the target position;
Using the coefficient seed data stored in the first storage means and the parameter value input by the parameter input means, the coefficient data of each class corresponding to the parameter value based on the generation formula Coefficient data generating means for generating
Second storage means for storing the coefficient data of each class generated by the coefficient data generation means;
Coefficient data reading means for reading out and outputting coefficient data of the estimation formula corresponding to the class detected by the class detecting means from the second storage means;
Second data selection means for selecting a plurality of second pixel data located around the target position in the component signal based on the composite signal;
Using the coefficient data output by the coefficient data reading means and the plurality of second pixel data selected by the second data selection means, the pixel data of the target position in the component signal based on the estimation formula An image signal processing apparatus comprising: an arithmetic means obtained by calculating An image signal processing method for converting a composite signal composed of a plurality of pixel data into a component signal composed of a plurality of pixel data,
A first step of selecting a plurality of first pixel data located around a target position in the component signal based on the composite signal;
A second step of detecting a class to which the pixel data of the target position in the component signal belongs based on the plurality of first pixel data selected in the first step;
A third step of obtaining a value of a parameter for determining a color tone of an image obtained by the component signal;
A fourth step of storing, for each class, coefficient seed data, which is coefficient data in a generation formula including the parameter, for generating coefficient data used in an estimation formula for calculating pixel data of the target position;
Using the coefficient seed data stored for each class and the parameter value acquired in the third step, the coefficient data of each class corresponding to the parameter value is generated based on the generation formula. And storing a fifth step;
A sixth step of reading out and outputting coefficient data of each class stored in the fifth step, corresponding to the class detected in the second step;
A seventh step of selecting a plurality of second pixel data located around a target position in the component signal based on the composite signal;
Using the coefficient data output in the sixth step and the plurality of second pixel data selected in the seventh step, pixel data of the target position in the component signal is calculated based on the estimation formula An image signal processing method comprising: an eighth step . In order to convert a composite signal composed of a plurality of pixel data into a component signal composed of a plurality of pixel data,
A first step of selecting a plurality of first pixel data located around a target position in the component signal based on the composite signal;
A second step of detecting a class to which the pixel data of the target position in the component signal belongs based on the plurality of first pixel data selected in the first step;
A third step of obtaining a value of a parameter for determining a color tone of an image obtained by the component signal;
A fourth step of storing, for each class, coefficient seed data, which is coefficient data in a generation formula including the parameter, for generating coefficient data used in an estimation formula for calculating pixel data of the target position;
Using the coefficient seed data stored for each class and the parameter value acquired in the third step, the coefficient data of each class corresponding to the parameter value is generated based on the generation formula. And storing a fifth step;
A sixth step of reading out and outputting coefficient data of each class stored in the fifth step, corresponding to the class detected in the second step;
A seventh step of selecting a plurality of second pixel data located around a target position in the component signal based on the composite signal;
Using the coefficient data output in the sixth step and the plurality of second pixel data selected in the seventh step, pixel data of the target position in the component signal is calculated based on the estimation formula A computer-readable medium having recorded thereon a program for causing a computer to execute the image signal processing method. In order to convert a composite signal composed of a plurality of pixel data into a component signal composed of a plurality of pixel data,
A first step of selecting a plurality of first pixel data located around a target position in the component signal based on the composite signal;
A second step of detecting a class to which the pixel data of the target position in the component signal belongs based on the plurality of first pixel data selected in the first step;
A third step of obtaining a value of a parameter for determining a color tone of an image obtained by the component signal;
A fourth step of storing, for each class, coefficient seed data, which is coefficient data in a generation formula including the parameter, for generating coefficient data used in an estimation formula for calculating pixel data of the target position;
Using the coefficient seed data stored for each class and the parameter value acquired in the third step, the coefficient data of each class corresponding to the parameter value is generated based on the generation formula. And storing a fifth step;
A sixth step of reading out and outputting coefficient data of each class stored in the fifth step, corresponding to the class detected in the second step;
A seventh step of selecting a plurality of second pixel data located around a target position in the component signal based on the composite signal;
Using the coefficient data output in the sixth step and the plurality of second pixel data selected in the seventh step, pixel data of the target position in the component signal is calculated based on the estimation formula And a program for causing a computer to execute an image signal processing method. Generating coefficient seed data that is coefficient data in a generation formula for generating coefficient data used in an estimation formula used when converting a composite signal composed of a plurality of pixel data into a component signal composed of a plurality of pixel data A device,
Parameter input means for inputting a value of a parameter for determining a color tone of an image obtained by a student signal corresponding to the composite signal or a teacher signal corresponding to the component signal corresponding to the parameter in the generation formula;
First data selection means for selecting a plurality of first pixel data located around a target position in the teacher signal based on the student signal;
Class detection means for detecting a class to which the pixel data of the target position in the teacher signal belongs based on a plurality of first pixel data selected by the first data selection means;
Second data selection means for selecting a plurality of second pixel data located around the target position in the teacher signal based on the student signal;
The detected class by the class detection means, said second plurality of second pixel data selected by the data selection means, the value of parameter inputted by the pixel data and the parameter input means of the target position in the teacher signal A coefficient seed data generating device, comprising: an arithmetic means for obtaining the coefficient seed data for each class using Generating coefficient seed data which is coefficient data in a generation formula for generating coefficient data used in an estimation formula used when converting a composite signal composed of a plurality of pixel data into a component signal composed of a plurality of pixel data A method,
A first step of obtaining a value of a parameter that determines a color tone of an image obtained by a student signal corresponding to the composite signal or a teacher signal corresponding to the component signal corresponding to the parameter in the generation formula;
A second step of selecting a plurality of first pixel data located around a target position in the teacher signal based on the student signal;
A third step of detecting a class to which the pixel data of the target position in the teacher signal belongs based on the plurality of first pixel data selected in the second step;
A fourth step of selecting a plurality of second pixel data located around a target position in the teacher signal based on the student signal;
The third detection class in step a, said fourth plurality of second pixel data selected in step, the value of the parameter obtained in the pixel data and the first step of the target position in the teacher signal And a fifth step of obtaining the coefficient seed data for each class. Generating coefficient seed data which is coefficient data in a generation formula for generating coefficient data used in an estimation formula used when converting a composite signal composed of a plurality of pixel data into a component signal composed of a plurality of pixel data for,
A first step of obtaining a value of a parameter that determines a color tone of an image obtained by a student signal corresponding to the composite signal or a teacher signal corresponding to the component signal corresponding to the parameter in the generation formula;
A second step of selecting a plurality of first pixel data located around a target position in the teacher signal based on the student signal;
A third step of detecting a class to which the pixel data of the target position in the teacher signal belongs based on the plurality of first pixel data selected in the second step;
A fourth step of selecting a plurality of second pixel data located around a target position in the teacher signal based on the student signal;
The third detection class in step a, said fourth plurality of second pixel data selected in step, the value of the parameter obtained in the pixel data and the first step of the target position in the teacher signal A computer-readable medium having recorded thereon a program for causing a computer to execute a coefficient seed data generation method including the fifth step of obtaining the coefficient seed data for each class. Generating coefficient seed data which is coefficient data in a generation formula for generating coefficient data used in an estimation formula used when converting a composite signal composed of a plurality of pixel data into a component signal composed of a plurality of pixel data for,
A first step of obtaining a value of a parameter that determines a color tone of an image obtained by a student signal corresponding to the composite signal or a teacher signal corresponding to the component signal corresponding to the parameter in the generation formula;
A second step of selecting a plurality of first pixel data located around a target position in the teacher signal based on the student signal;
A third step of detecting a class to which the pixel data of the target position in the teacher signal belongs based on the plurality of first pixel data selected in the second step;
A fourth step of selecting a plurality of second pixel data located around a target position in the teacher signal based on the student signal;
The third detection class in step a, said fourth plurality of second pixel data selected in step, the value of the parameter obtained in the pixel data and the first step of the target position in the teacher signal A program for causing a computer to execute a coefficient seed data generation method including: a fifth step of obtaining the coefficient seed data for each class. An apparatus for generating coefficient data of an estimation formula used when converting a composite signal composed of a plurality of pixel data into a component signal composed of a plurality of pixel data,
A parameter for determining the color tone of an image obtained by a student signal corresponding to the composite signal or a teacher signal corresponding to the component signal, corresponding to a parameter included in the generation expression for generating coefficient data used in the estimation expression A parameter input means for inputting a value;
First data selection means for selecting a plurality of first pixel data located around a target position in the teacher signal based on the student signal;
Class detection means for detecting a class to which the pixel data of the target position in the teacher signal belongs based on a plurality of first pixel data selected by the first data selection means;
Second data selection means for selecting a plurality of second pixel data located around the target position in the teacher signal based on the student signal;
The detected class by the class detection means, said second plurality of second pixel data selected by the data selection means, the value of parameter inputted by the pixel data and the parameter input means of the target position in the teacher signal using, for each of the classes, determine the coefficient seed data is coefficient data in the production equation, by the generation equation including the value of the coefficient seed data and the parameter, the coefficient data used in the estimation equation A coefficient data generating device comprising: a calculating means for obtaining. A method of generating coefficient data of an estimation formula used when converting a composite signal composed of a plurality of pixel data into a component signal composed of a plurality of pixel data,
A parameter for determining the color tone of an image obtained by a student signal corresponding to the composite signal or a teacher signal corresponding to the component signal, corresponding to a parameter included in the generation expression for generating coefficient data used in the estimation expression A first step of obtaining a value;
A second step of selecting a plurality of first pixel data located around a target position in the teacher signal based on the student signal;
A third step of detecting a class to which the pixel data of the target position in the teacher signal belongs based on the plurality of first pixel data selected in the second step;
A fourth step of selecting a plurality of second pixel data located around a target position in the teacher signal based on the student signal;
The third detection class in step a, said fourth plurality is selected in Step second pixel data, the value of parameter inputted by the pixel data and the parameter input means of the target position in the teacher signal using, for each of the classes, determine the coefficient seed data is coefficient data in the production equation, by the generation equation including the value of the coefficient seed data and the parameter, determine the coefficient data to be used in the estimated equation A coefficient data generation method comprising: a fifth step. In order to generate coefficient data of an estimation formula used when converting a composite signal composed of a plurality of pixel data into a component signal composed of a plurality of pixel data,
A parameter for determining the color tone of an image obtained by a student signal corresponding to the composite signal or a teacher signal corresponding to the component signal, corresponding to a parameter included in the generation expression for generating coefficient data used in the estimation expression A first step of obtaining a value;
A second step of selecting a plurality of first pixel data located around a target position in the teacher signal based on the student signal;
A third step of detecting a class to which the pixel data of the target position in the teacher signal belongs based on the plurality of first pixel data selected in the second step;
A fourth step of selecting a plurality of second pixel data located around a target position in the teacher signal based on the student signal;
The third detection class in step a, said fourth plurality is selected in Step second pixel data, the value of parameter inputted by the pixel data and the parameter input means of the target position in the teacher signal using, for each of the classes, determine the coefficient seed data is coefficient data in the production equation, by the generation equation including the value of the coefficient seed data and the parameter, determine the coefficient data to be used in the estimated equation A computer-readable medium recording a program for causing a computer to execute a coefficient data generation method having the fifth step. In order to generate coefficient data of an estimation formula used when converting a composite signal composed of a plurality of pixel data into a component signal composed of a plurality of pixel data,
A parameter for determining the color tone of an image obtained by a student signal corresponding to the composite signal or a teacher signal corresponding to the component signal, corresponding to a parameter included in the generation expression for generating coefficient data used in the estimation expression A first step of obtaining a value;
A second step of selecting a plurality of first pixel data located around a target position in the teacher signal based on the student signal;
A third step of detecting a class to which the pixel data of the target position in the teacher signal belongs based on the plurality of first pixel data selected in the second step;
A fourth step of selecting a plurality of second pixel data located around a target position in the teacher signal based on the student signal;
The third detection class in step a, said fourth plurality is selected in Step second pixel data, the value of parameter inputted by the pixel data and the parameter input means of the target position in the teacher signal using, for each of the classes, determine the coefficient seed data is coefficient data in the production equation, by the generation equation including the value of the coefficient seed data and the parameter, determine the coefficient data to be used in the estimated equation A program for causing a computer to execute a coefficient data generation method having the fifth step.

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