JP2004007227A - Processor and processing method of information signal, device and method of creating coefficient class data used therefor, program for implementing each method, and computer-readable medium recording the program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To reduce a circuit scale by saving a memory capacity and to improve the quality of an output by an information signal after processing. <P>SOLUTION: An image signal processing unit 104 converts an NTSC (National Television System Committee) signal into Y, R-Y and B-Y component signals. A feature amount v concerning target pixels from a plurality of pixel data corresponding to target pixels of the component signals which are selectively drawn out of the NTSC signal. Coefficient class data w<SB>io</SB>-w<SB>i3</SB>are stored in a coefficient class memory 115. In a coefficient generating unit 114, coefficient data Wi corresponding to the feature amount v are created by a preparation formula containing the feature amount as a parameter, on the basis of the feature amount v and the data w<SB>io</SB>-w<SB>i3</SB>. In an operation unit 116, pixel data are determined from the data xi of a prediction tap corresponding to the target pixels of the component signal and the coefficient data Wi corresponding to the feature amount v, by using an estimating formula. <P>COPYRIGHT: (C)2004,JPO

Description

【００５４】
この推定式の係数データＷｉは、（２）式に示すように、特徴量ｖをパラメータとして含む生成式によって生成される。係数種メモリ１１５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（輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙの各信号分）が格納されている。上述した係数生成部１１４では、この係数種データｗ_ｉ０〜ｗ_ｉ３を用いて、（２）式の生成式に基づいて、係数データＷｉが生成される。
Ｗｉ＝ｗ_ｉ０＋ｗ_ｉ１ｖ＋ｗ_ｉ２ｖ^２＋ｗ_ｉ３ｖ^３・・・（２）
【００５５】
また、画像信号処理部１０４は、タップ選択部１１２で選択的に取り出される予測タップの画素データｘｉと、係数生成部１１４で生成される係数データＷｉ（輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙの各信号分）とから、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号を構成する輝度信号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部１１６と、この推定予測演算部１１６で演算される画素データｙからなるＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号を出力する出力端子１１７とを有している。
【００５６】
次に、図３に示す画像信号処理部１０４の動作を説明する。
入力端子１１１に入力されるＮＴＳＣ信号は特徴量抽出部１１３に供給される。この特徴量抽出部１１３は、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の画素データに関連した特徴量ｖを抽出する。この特徴量抽出部１１３で抽出される特徴量ｖは係数生成部１１４に供給される。
【００５７】
係数生成部１１４は、係数種メモリ１１５に記憶されている係数種データｗ_{ｉ０ 〜 ｗｉ３}（輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙの各信号分）を用いて、（２）式の生成式に基づいて、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号を構成する輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙのそれぞれを得るための、特徴量ｖに対応した係数データＷｉを生成する。
【００５８】
係数生成部１１４で生成された係数データＷｉは（輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙの各信号分）は推定予測演算部１１６に供給される。また、入力端子１１１に入力されるＮＴＳＣ信号はタップ選択部１１２に供給される。タップ選択部１１２は、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の周辺に位置する予測タップの画素データｘｉを選択的に取り出す。このタップ選択部１１２で取り出された予測タップの画素データｘｉは推定予測演算部１１６に供給される。
【００５９】
推定予測演算部１１６は、予測タップの画素データｘｉと、係数データＷｉ（輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙの各信号分）とを用いて、（１）式の推定式に基づいて、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号を構成する輝度信号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を演算して求める。そして、出力端子１１７には、推定予測演算部１１６で演算された画素データｙからなるＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号が出力される。
【００６０】
このように、図３に示す画像信号処理部１０４により、ＮＴＳＣ信号をＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号に変換することができる。
この画像信号処理部１０４においては、特徴量抽出部１１３で抽出された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に関連した特徴量ｖに対応した係数データＷｉを、係数生成部１１４で生成して用いるものである。したがって、予め係数メモリに複数の係数データを格納しておくものでなく、メモリ容量を節約でき、回路規模を低減できる。
【００６１】
また、画像信号処理部１０４においては、特徴量抽出部１１３で抽出された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に関連した特徴量ｖそのものに対応した係数データＷｉを係数生成部１１４で生成して用いるものである。したがって、特徴量ｖをクラス分けしてクラス別に係数データＷｉを生成して用いるものではなく、変換して得られるＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号による画像の画質の向上を図ることができる。
【００６２】
また、画像信号処理部１０４において、特徴量抽出部１１３を図５に示すように構成し、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号における注目位置が存在するラインに対するその上ラインおよび下ラインの色信号の変化量の比を特徴量ｖとして抽出することで、主に垂直方向に色の変化が見られるパターンについて、ドット妨害の軽減を図ることができる。
【００６３】
図９は、図１０に示すような予測タップ構造において、図５に示す特徴量抽出部１１３で抽出された特徴量ｖと、これを用いて生成される各タップの係数データＷｉとの関係の一例を示している。図９の横軸は特徴量ｖの対数値である。またこの図９では、注目位置に対応するタップｘ_５の係数データが１となるように正規化している。
【００６４】
この図から明らかなように、上ラインに対応するタップｘ_１，ｘ_２，ｘ_３の係数の絶対値は、特徴量ｖ（＝Ｌ_２／Ｌ_１）が大きくなっていくにつれて、つまり注目位置が存在するラインに対して上ラインが下ラインより色の相関が高くなっていくにつれて大きくなっていく。一方、下ラインに対応するタップｘ_７，ｘ_８，ｘ_９の係数の絶対値は、特徴量ｖが小さくなっていくにつれて、つまり注目位置が存在するラインに対して下ラインが上ラインより色の相関が高くなっていくにつれて大きくなっていく。
【００６５】
また、画像信号処理部１０４において、特徴量抽出部１１３を図７に示すように構成し、Ｙ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号における注目位置の近傍のＩ信号成分およびＱ信号成分のそれぞれの垂直方向における変化量の比を特徴量ｖとして抽出することで、主に垂直方向にＩ信号成分かＱ信号成分の片方だけが大きく変化しているパターンについて、ドット妨害の軽減を図ることができる。
【００６６】
図１１は、図４に示すような予測タップ構造において、図７に示す特徴量抽出部１１３で抽出された特徴量ｖと、これを用いて生成される各タップの係数データＷｉとの関係の一例を示している。図１１の横軸は特徴量ｖの対数値である。またこの図１１は、注目位置がＹ＋Ｉ信号に対応している場合の例である。
【００６７】
この図から明らかなように、例えばＹ＋Ｉ信号に対応しているタップｘ_８の係数は、特徴量ｖ（＝Ｔ_２／Ｔ_１）が小さくなっていくにつれて、つまりＩ信号成分の垂直方向における変化量がＱ信号成分の垂直方向の変化量より小さくなっていくにつれて大きくなっていく。一方、例えばＹ＋Ｑ信号、Ｙ−Ｑ信号に対応しているタップｘ_７，ｘ_９の係数の絶対値は、特徴量ｖ（＝Ｔ_２／Ｔ_１）が大きくなっていくにつれて、つまりＱ信号成分の垂直方向における変化量がＩ信号成分の垂直方向の変化量より小さくなっていくにつれて大きくなっていく。
【００６８】
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。
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。
ｔ_０＝１，ｔ_１＝ｖ，ｔ_２＝ｖ^２，ｔ_３＝ｖ^３・・・（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。
【００６９】
【数２】

【００７０】
最終的に、学習によって未定係数ｗ_ｉｊを求める。すなわち、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。
【００７１】
【数３】

【００７２】
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。
【００７３】
【数４】

【００７４】
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。
【００７５】
【数５】

【００７６】
【数６】

【００７７】
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ｗ_ｉ０〜ｗ_ｉ３が算出される。
【００７８】
図１２は、上述した係数種データの生成方法の一例に基づいて、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。
【００７９】
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１５２とを有している。
【００８０】
また、係数種データ生成装置１５０は、ＮＴＳＣエンコーダ１５２で得られたＮＴＳＣ信号より、教師信号ＳＴにおける注目位置の画素データに関連した特徴量ｖを抽出する特徴量抽出部１５３を有している。この特徴量抽出部１５３は、上述した画像信号処理部１０４の特徴量抽出部１１３と同様に構成される。
【００８１】
また、係数種データ生成装置１５０は、特徴量抽出部１５３で抽出された特徴量ｖをこの特徴量ｖを含む所定範囲の代表値に置き換え、この代表値をクラスコードＣＬとして決定するクラスコード決定部１５４を有している。
【００８２】
この場合、図１３に示すように、特徴量ｖの変化範囲内においてｌｏｇスケールで等間隔となるようにＭ個のクラス範囲が設定される。クラスコード決定部１５４は、特徴量抽出部１５３で抽出された特徴量ｖがどのクラス範囲に含まれるかを判定し、その含まれるクラス範囲の代表値、例えば中間値をクラスコードＣＬとして決定する。例えば、特徴量抽出部１５３で抽出された特徴量ｖがクラス範囲３に含まれるとき、このクラス範囲３の代表値Ｖ_３がクラスコードＣＬとなる。
【００８３】
このように、特徴量抽出部１５３で抽出された特徴量ｖをその特徴量ｖが含まれるクラス範囲の代表値に変更して用いることで、後述する正規方程式の生成に当たって必要とする学習データを容易に取得できるようになる。特徴量抽出部１５３で抽出された特徴量ｖをそのまま用いる場合には、特徴量ｖの各値に対応して予測タップ数以上の学習データを得ることが必要となり、非常に困難となる。
【００８４】
また、係数種データ生成装置１５０は、ＮＴＳＣエンコーダ１５２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タップ選択部１５５を有している。このタップ選択部１５５は、上述した画像信号処理部１０４のタップ選択部１１２と同様に構成される。
【００８５】
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１５６と、この遅延回路１５６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タップ選択部１５５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決定部１５４で決定されたクラスコードＣＬ（特徴量抽出部１５３で抽出された特徴量ｖの代表値）とから、係数種データｗ_ｉ０〜ｗ_ｉ３を得るための（９）式の正規方程式を生成する正規方程式生成部１５７を有している。
【００８６】
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。また、各学習データのそれぞれに対応してクラスコード決定部１５４からクラスコードＣＬが供給される。これにより、正規方程式生成部１５７では、複数のクラスコードＣＬ（図１３のｖ_１〜ｖ_Ｍ参照）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。
【００８７】
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１５７では、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。
【００８８】
また、係数種データ生成装置１５０は、正規方程式生成部１５７で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、係数種データｗ_ｉ０〜ｗ_ｉ３を求める係数種データ決定部１５８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１５９とを有している。係数種データ決定部１５８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ｗ_ｉ０〜ｗ_ｉ３が求められる。
【００８９】
図１２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ＮＴＳＣエンコーダ１５２に供給される。このＮＴＳＣエンコーダ１５２は、教師信号ＳＴ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信号を生成する。
【００９０】
ＮＴＳＣエンコーダ１５２で得られる生徒信号ＳＳは特徴量抽出部１５３に供給される。この特徴量抽出部１５３は、ＮＴＳＣ信号より教師信号ＳＴにおける注目位置の画素データに関連した特徴量ｖを抽出する。特徴量抽出部１５３で抽出された特徴量ｖはクラスコード決定部１５４に供給される。
【００９１】
クラスコード決定部１５４は、特徴量ｖをこの特徴量ｖを含む所定範囲の代表値に置き換え、この代表値をクラスコードＣＬとして決定する。このようにクラスコード決定部１５４で決定されたクラスコードＣＬは正規方程式生成部１５７に供給される。
【００９２】
また、ＮＴＳＣエンコーダ１５２で得られる生徒信号ＳＳはタップ選択部１５５に供給される。タップ選択部１５５は、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予測タップの画素データを選択的に取り出す。このようにタップ選択部１５５で取り出された予測タップの画素データｘｉは正規方程式生成部１５７に供給される。
【００９３】
正規方程式生成部１５７は、遅延回路１５６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タップ選択部１５５で選択的に取り出される予測タップの画素データｘｉと、この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決定部１５４で決定されるクラスコードＣＬとを用いて、係数種データｗ_ｉ０〜ｗ_ｉ３を得るための（９）式の正規方程式を生成する。
【００９４】
そして、正規方程式生成部１５７で生成された正規方程式のデータは係数種データ決定部１５８に供給される。係数種データ決定部１５８は、正規方程式を解いて係数種データｗ_ｉ０〜ｗ_ｉ３（輝度信号Ｙ、色差信号Ｒ−Ｙ，Ｂ−Ｙの各信号分）を求める。そして、このように求められた係数種データｗ_ｉ０〜ｗ_ｉ３は係数種メモリ１５９に格納される。
【００９５】
このように、図１２に示す係数種データ生成装置１５０においては、画像信号処理部１０４の係数種メモリ１１５に格納される、（１）式の推定式で用いられる係数データＷｉを求めるための（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を良好に生成することができる。
【００９６】
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。
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。
【００９７】
上述の、（１）式において、学習前は係数データＷ_１，Ｗ_２，‥‥，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（１０）式が設定される。ｎは予測タップの数を示している。
ｙ_ｋ＝Ｗ_１×ｘ_ｋ１＋Ｗ_２×ｘ_ｋ２＋‥‥＋Ｗ_ｎ×ｘ_ｋｎ・・・（１０）
（ｋ＝１，２，‥‥，ｍ）
【００９８】
ｍ＞ｎの場合、係数データＷ_１，Ｗ_２，‥‥，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ｅ^２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−｛ｗ_１×ｘ_ｋ１＋ｗ_２×ｘ_ｋ２＋‥‥＋ｗ_ｎ×ｘ_ｋｎ｝　・・・（１１）
（ｋ＝１，２，‥‥ｍ）
【００９９】
【数７】

【０１００】
（１２）式のｅ^２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、ｅ^２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。
【０１０１】
【数８】

【０１０２】
（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。
【０１０３】
【数９】

【０１０４】
【数１０】

【０１０５】
（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。
【０１０６】
次に、各クラスコード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ｗ_ｉ０〜ｗ_ｉ３の求め方を説明する。
あるクラスコードＣＬの係数データが、ｋ_ｖｉ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_ｖｉから係数種データｗ_ｉ０〜ｗ_ｉ３を求める。
【０１０７】
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。
【０１０８】
【数１１】

【０１０９】
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。
【０１１０】
【数１２】

【０１１１】
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。
【０１１２】
【数１３】

【０１１３】
図１４は、上述した係数種データの生成方法の他の例に基づいて、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１５０Ａの構成を示している。この図１４において、図１２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。
【０１１４】
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タップ選択部１５５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決定部１５４で決定されたクラスコードＣＬ（特徴量抽出部１５３で抽出された特徴量ｖの代表値）とから、クラスコード毎に、係数データＷｉ（ｉ＝１〜ｎ）を得るための（１６）式の正規方程式を生成する正規方程式生成部１６１を有している。
【０１１５】
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。また、各学習データのそれぞれに対応してクラスコード決定部１５４からクラスコードＣＬが供給される。正規方程式生成部１６１では、クラスコード毎に、多くの学習データが登録された正規方程式が生成される。これにより、クラスコード毎に、係数データＷｉ（ｉ＝１〜ｎ）を求めることが可能となる。
【０１１６】
また、係数種データ生成装置１５０Ａは、正規方程式生成部１６１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、クラスコード毎に、係数データＷｉを求める係数データ決定部１６２と、クラスコードＣＬと各クラスコードＣＬに対応した係数データＷｉとを使用して、係数種データｗ_ｉ０〜ｗ_ｉ３を得るための（２１）式の正規方程式を生成する正規方程式生成部１６３とを有している。
【０１１７】
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、正規方程式を解いて係数種データｗ_ｉ０〜ｗ_ｉ３を求める係数種データ決定部１６４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１５９とを有している。
図１４に示す係数種データ生成装置１５０Ａのその他は、図１２に示す係数種データ生成装置１５０と同様に構成される。
【０１１８】
次に、図１４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ＮＴＳＣエンコーダ１５２に供給される。このＮＴＳＣエンコーダ１５２は、教師信号ＳＴ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信号を生成する。
【０１１９】
ＮＴＳＣエンコーダ１５２で得られる生徒信号ＳＳは特徴量抽出部１５３に供給される。この特徴量抽出部１５３は、ＮＴＳＣ信号より教師信号ＳＴにおける注目位置の画素データに関連した特徴量ｖを抽出する。特徴量抽出部１５３で抽出された特徴量ｖはクラスコード決定部１５４に供給される。
【０１２０】
クラスコード決定部１５４は、特徴量ｖをこの特徴量ｖを含む所定範囲の代表値に置き換え、この代表値をクラスコードＣＬとして決定する。このようにクラスコード決定部１５４で決定されたクラスコードは正規方程式生成部１６１に供給される。
【０１２１】
また、ＮＴＳＣエンコーダ１５２で得られる生徒信号ＳＳはタップ選択部１５５に供給される。タップ選択部１５５は、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予測タップの画素データを選択的に取り出す。このようにタップ選択部１５５で取り出された予測タップの画素データｘｉは正規方程式生成部１６１に供給される。
【０１２２】
正規方程式生成部１６１は、遅延回路１５６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タップ選択部１５５で選択的に取り出される予測タップの画素データｘｉと、この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決定部１５４で決定されるクラスコードＣＬとを用いて、クラスコード毎に、係数データＷｉ（ｉ＝１〜ｎ）を得るための（１６）式の正規方程式を生成する。
【０１２３】
そして、係数データ決定部１６２でその正規方程式が解かれ、各クラスコードに対応した係数データＷｉが求められる。正規方程式生成部１６３では、クラスコードＣＬと各クラスコードに対応した係数データＷｉとを用いて、係数種データｗ_ｉ０〜ｗ_ｉ３を得るための（２１）式の正規方程式が生成される。
そして、係数種データ決定部１６４でその正規方程式が解かれ、係数種データｗ_ｉ０〜ｗ_ｉ３が求められ、その係数種データｗ_ｉ０〜ｗ_ｉ３は係数種メモリ１５９に格納される。
【０１２４】
このように、図１４に示す係数種データ生成装置１５０Ａにおいても、画像信号処理部１０４の係数種メモリ１１５に格納される、（１）式の推定式で用いられる係数データＷｉを求めるための（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を良好に生成することができる。
【０１２５】
なお、図１の画像信号処理部１０４における処理を、例えば図１５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。
【０１２６】
まず、図１５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。
【０１２７】
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。
【０１２８】
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。
【０１２９】
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。
【０１３０】
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。
【０１３１】
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。
【０１３２】
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３０５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。
【０１３３】
図１６のフローチャートを参照して、図１５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。
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３０５に予め記録されている場合には、このドライブ３０５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。
【０１３４】
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。
【０１３５】
このステップＳＴ５では、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（注目画素）を選択する。そして、ステップＳＴ６で、ステップＳＴ２で入力されたＮＴＳＣ信号より、この注目位置の周辺に位置する複数の画素データを取得する。
【０１３６】
次に、ステップＳＴ７で、ステップＳＴ６で取得された複数の画素データに基づいて、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に関連した特徴量ｖを演算で求める（図５、図７参照）。そして、ステップＳＴ８で、特徴量ｖに基づいて、ＲＯＭ３０２に格納されている係数種データｗ_ｉ０〜ｗ_ｉ３から、（２）式の生成式によって、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。
【０１３７】
次に、ステップＳＴ９で、ステップＳＴ８で生成された係数データＷｉと、ステップＳＴ６で取得された複数の画素データ（予測タップ）ｘｉとを使用して、（１）式の推定式により、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）における注目位置の画素データを生成する。
【０１３８】
次に、ステップＳＴ１０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、終了していないときは、ステップＳＴ５に戻って、次の注目画素の選択を行って、上述したと同様の処理を繰り返す。
【０１３９】
このように、図１６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。
【０１４０】
また、処理装置の図示は省略するが、図１２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。
【０１４１】
図１７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、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ＳＴ２３で、入力された教師信号より、生徒信号（ＮＴＳＣ信号）を生成する。
【０１４２】
次に、ステップＳＴ２４で、教師信号の全フレームまたは全フィールドに対して学習処理が終わったか否かを判定する。終わっていないときは、ステップＳＴ２５に進む。
ステップＳＴ２５では、教師信号（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（注目画素）を選択する。そして、ステップＳＴ２６で、ステップＳＴ２３で生成された生徒信号（ＮＴＳＣ信号）より、この注目位置の周辺に位置する複数の画素データを取得する。
【０１４３】
次に、ステップＳＴ２７で、ステップＳＴ２６で取得された複数の画素データに基づいて、教師信号における注目位置の画素データに関連した特徴量ｖを演算で求める（図５、図７参照）。さらに、ステップＳＴ２８で、ステップＳＴ２７で求めた特徴量ｖをこの特徴量ｖを含む所定範囲の代表値に置き換え、この代表値をクラスコードＣＬとして決定する（図１３参照）。
【０１４４】
次に、ステップＳＴ２９で、正規方程式（（９）式参照）を生成する。そして、ステップＳＴ３０で、ステップＳＴ２２で入力された教師信号の画素データの全領域において学習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ＳＴ２２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、終了していないときは、ステップＳＴ２５に戻って、次の注目画素の選択を行って、上述したと同様の処理を繰り返す。
【０１４５】
また、ステップＳＴ２４で、教師信号の全フレームまたは全フィールドに対して学習処理が終わったときは、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係数種データｗ_ｉ０〜ｗ_ｉ３を算出し、ステップＳＴ３２で、その係数種データｗ_ｉ０〜ｗ_ｉ３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。
【０１４６】
このように、図１７に示すフローチャートに沿って処理をすることで、図１２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。
また、処理装置の図示は省略するが、図１４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。
【０１４７】
図１８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、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ＳＴ４３で、入力された教師信号より、生徒信号（ＮＴＳＣ信号）を生成する。
【０１４８】
次に、ステップＳＴ４４で、教師信号の全フレームまたは全フィールドに対して学習処理が終わったか否かを判定する。終わっていないときは、ステップＳＴ４５に進む。
ステップＳＴ４５では、教師信号（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（注目画素）を選択する。そして、ステップＳＴ４６で、ステップＳＴ４３で生成された生徒信号（ＮＴＳＣ信号）より、この注目位置の周辺に位置する複数の画素データを取得する。
【０１４９】
次に、ステップＳＴ４７で、ステップＳＴ４６で取得された複数の画素データに基づいて、教師信号における注目位置の画素データに関連した特徴量ｖを演算で求める（図５、図７参照）。さらに、ステップＳＴ４８で、ステップＳＴ４７で求めた特徴量ｖをこの特徴量ｖを含む所定範囲の代表値に置き換え、この代表値をクラスコードＣＬとして決定する（図１３参照）。
【０１５０】
次に、ステップＳＴ４９で、クラス毎に、正規方程式（（１６）式参照）を生成する。そして、ステップＳＴ５０で、ステップＳＴ４２で入力された教師信号の画素データの全領域において学習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ＳＴ４２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、終了していないときは、ステップＳＴ４５に戻って、次の注目画素の選択を行って、上述したと同様の処理を繰り返す。
【０１５１】
また、ステップＳＴ４４で、教師信号の全フレームまたは全フィールドに対して学習処理が終わったときは、ステップＳＴ５１に進む。このステップＳＴ５１では、正規方程式を掃き出し法等で解くことによって、クラス毎に、係数データＷｉを算出する。
【０１５２】
次に、ステップＳＴ５２で、ステップＳＴ５１で算出された各クラスの係数データＷｉから、係数種データｗ_ｉ０〜ｗ_ｉ３を求めるための正規方程式（（２１）式参照）を生成する。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係数種データｗ_ｉ０〜ｗ_ｉ３を算出し、ステップＳＴ５４で、その係数種データｗ_ｉ０〜ｗ_ｉ３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。
【０１５３】
このように、図１８に示すフローチャートに沿って処理をすることで、図１４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。
【０１５４】
なお、図３の画像信号処理部１０４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。
【０１５５】
また、図３の画像信号処理部１０４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。
【０１５６】
また、図３の画像信号処理部１０４の特徴量抽出部１１３で抽出される特徴量は、図５あるいは図７に示すものに限定されるものではない。要は、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に関連した特徴量であればよい。また、図５の特徴量抽出部１１３では特徴量ｖ＝Ｌ_２／Ｌ_１としたものであるが、ｖ＝ｌｏｇ（Ｌ_２／Ｌ_１）の形で用いてもよい。同様に、図７の特徴量抽出部１１３では特徴量ｖ＝Ｔ_２／Ｔ_１としたものであるが、ｖ＝ｌｏｇ（Ｔ_２／Ｔ_１）の形で用いてもよい。
【０１５７】
また、図３の画像信号処理部１０４では、１種類の特徴量ｖのみを使用するものであったが、２種類以上の特徴量ｖを用いるものも同様に構成することができる。例えば、図５、図７の特徴量抽出部１１３で抽出された２つの特徴量ｖを使用した構成とすることもできる。
【０１５８】
その場合、それぞれの特徴量ｖをｖ_１，ｖ_２とするとき、係数データＷｉを生成するための生成式としては、（２２）式に示すような生成式を用いることができる。
【０１５９】
【数１４】

【０１６０】
また、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_ｉ９は、図１２に示す係数種データ生成装置１５０あるいは図１４に示す係数種データ生成装置１５０Ａと同様の構成の係数種データ生成装置により生成できる。その場合、特徴量ｖ_１よりＭ_１段階のクラスが得られ、特徴量ｖ_２よりＭ_２段階のクラスが得られ、合わせてＭ_１×Ｍ_２個にクラス分類される。つまり、クラスコード決定部１５４からは特徴量ｖ_１，ｖ_２それぞれの代表値がクラスコードＣＬ_１，ＣＬ_２として得られ、これらクラスコードＣＬ_１，ＣＬ_２を用いて正規方程式の生成が行われることとなる。
【０１６１】
また、図３の画像信号処理部１０４では、ＮＴＳＣ信号からＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号に変換するための推定式として線形一次方程式を使用したものを挙げたが、これに限定されるものではなく、例えば推定式として高次方程式を使用してもよい。
【０１６２】
また、図３の画像信号処理部１０４はＮＴＳＣ信号からＹ，Ｒ−Ｙ，Ｂ−Ｙコンポーネント信号に変換するものであったが、マトリックス回路１０５（図１参照）の機能をも含めることができる。その場合、ＮＴＳＣ信号からコンポーネント信号としての３原色信号Ｒ，Ｇ，Ｂに変換することができる。
【０１６３】
また、上述実施の形態においては、情報信号が画像信号であるものを示したが、この発明はこれに限定されない。例えば、情報信号が音声信号である場合にも、この発明を同様に適用することができる。
【０１６４】
【発明の効果】
この発明によれば、第１の情報信号を第２の情報信号に変換する際に、第２の情報信号における注目位置の画素データを推定式に基づいて生成するものであって、第２の情報信号における注目位置の情報データに関連した特徴量を抽出し、この特徴量に対応した推定式の係数データを係数種データを用いて生成するものであり、メモリ容量を節約して回路規模を低減できると共に、第２の情報信号（出力情報信号）による出力の質の向上を図ることができる。
【０１６５】
また、この発明によれば、第１の情報信号を第２の情報信号に変換する際に使用される推定式で用いられる係数データを生成するための、特徴量をパラメータとして含む生成式における係数データである係数種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】各フィールドの画素データの構成を示す図である。
【図３】画像信号処理部の構成を示すブロック図である。
【図４】予測タップの構造例を示す図である。
【図５】特徴量抽出部の構成例を示すブロック図である。
【図６】特徴量を抽出するための画素データ例を示す図である。
【図７】特徴量抽出部の構成例を示すブロック図である。
【図８】特徴量を抽出するための画素データ例を示す図である。
【図９】特徴量ｖと各タップの係数データＷｉとの関係例を示す図である。
【図１０】予測タップの構造例を示す図である。
【図１１】特徴量ｖと各タップの係数データＷｉとの関係例を示す図である。
【図１２】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１３】クラスコード決定処理を説明するための図である。
【図１４】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１５】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１６】画像信号処理を示すフローチャートである。
【図１７】係数種データ生成処理を示すフローチャートである。
【図１８】係数種データ生成処理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・・・受信アンテナ、１０２・・・チューナ、１０３・・・Ａ／Ｄ変換器、１０４・・・画像信号処理部、１０５・・・マトリックス回路、１０６・・・表示デバイス、１１１・・・入力端子、１１２・・・タップ選択部、１１３・・・特徴量抽出部、１１４・・・係数生成部、１１５・・・係数種メモリ、１１６・・・推定予測演算部、１１７・・・出力端子、１２１・・・バンドバスフィルタ、１２２，１２３，１３２・・・遅延回路、１２４，１２６，１３１・・・加算回路、１２５，１２７・・・絶対値化回路、１２８，１３３・・・除算回路、１５０，１５０Ａ・・・係数種データ生成装置、１５１・・・入力端子、１５２・・・ＮＴＳＣエンコーダ、１５３・・・特徴量抽出部、１５４・・・クラスコード決定部、１５５・・・タップ選択部、１５６・・・遅延回路、１５７，１６１，１６３・・・正規方程式生成部、１５８，１６４・・・係数種データ決定部、１５９・・・係数種メモリ、１６２・・・係数データ決定部[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention is suitable for, for example, converting a composite signal into a component signal, a processing device and a processing method of an information signal, a generating device and a generating method of coefficient seed data used for the same, and executing each method. And a computer-readable medium storing the program.
[0002]
More specifically, the present invention, when converting the first information signal into the second information signal, generates pixel data of a target position in the second information signal based on an estimation formula. By extracting the feature amount related to the information data of the target position in the information signal of, and generating the coefficient data of the estimation formula corresponding to this feature amount using the coefficient seed data, the memory capacity can be saved and the circuit scale can be reduced. The present invention relates to an information signal processing device and the like which reduce the output and improve the quality of output by the second information signal.
[0003]
[Prior art]
In the conventional composite-component conversion, a composite signal, for example, an NTSC (National Television System Comittee) signal is first separated into a Y signal (luminance signal) and a C signal (color signal) by a Y / C separation circuit. , C signals are converted to baseband Y, RY, and BY component signals by color demodulation.
[0004]
Therefore, there is a problem that the circuit scale of the configuration for performing the processing is increased. Further, there is a problem in that image quality degradation such as dot disturbance and cross color is likely to occur in an edge portion of an image, a moving image portion, and the like due to an error in Y / C separation.
[0005]
Therefore, the present applicant extracts a plurality of pixel data located around the target position of the Y, RY, BY component signal from, for example, an NTSC signal, and based on the level distribution pattern of the plurality of pixel data. Then, the class of the pixel data of the target position in the Y, RY, BY component signal is determined, and the pixel data of the target position in the Y, RY, BY component signal is determined in accordance with this class. There has been proposed an image information converting apparatus for generating the image information (see Japanese Patent Application Laid-Open No. 2000-138949).
[0006]
[Problems to be solved by the invention]
This image information converter converts NTSC signals directly into Y, RY, and BY component signals, for example, and does not use a Y / C separation circuit or a color demodulation circuit but can reduce the circuit scale. In addition, it is possible to reduce image disturbance such as dot interference and cross color caused by an error in Y / C separation.
[0007]
However, in this image information conversion apparatus, pixel data of a target position in a Y, RY, BY component signal is generated based on an estimation formula, and coefficient data of the estimation formula of each class is calculated in advance by a coefficient. It was to be stored in memory. In order to improve the image quality of the image based on the Y, RY, and BY component signals, it is necessary to increase the number of classes. However, when the number of classes is increased, the coefficient data of the estimation formula to be stored in the coefficient memory increases, and it is necessary to increase the memory capacity of the coefficient memory, thereby increasing the circuit scale.
[0008]
SUMMARY OF THE INVENTION It is an object of the present invention to provide an information signal processing device and the like which can save the memory capacity and reduce the circuit scale, and can improve the quality of output by an output information signal. Further, an object of the present invention is to provide a coefficient seed data generation device and the like that can satisfactorily obtain coefficient seed data that is coefficient data in a generation equation for generating coefficient data of an estimation equation corresponding to a feature amount. It is in.
[0009]
[Means for Solving the Problems]
An information signal processing device according to the present invention is an information signal processing device that converts a first information signal including a plurality of pieces of information data into a second information signal including a plurality of pieces of information data. First data selecting means for selecting a plurality of first information data located around the target position in the second information signal based on the first information selecting means, and a plurality of first information data selected by the first data selecting means. A feature amount extracting unit configured to extract a feature amount related to the information data of the target position in the second information signal using the information data; and a coefficient data generating unit configured to generate coefficient data used in an estimation expression including the feature amount as a parameter. A storage unit that stores coefficient seed data that is the coefficient data of the generation formula, and a coefficient type data that is estimated based on the generation formula using the coefficient seed data stored in the storage unit and the feature value extracted by the feature value extraction device. A coefficient data generating means for generating coefficient data used in the equation, and a second selecting means for selecting, based on the first information signal, a plurality of pieces of second information data located around the target position in the second information signal. Using the data selection means, the coefficient data generated by the coefficient data generation means, and the plurality of pieces of second information data selected by the second data selection means, the attention in the second information signal based on the estimation formula And calculating means for calculating pixel data of the position.
[0010]
Further, an information signal processing method according to the present invention is an information signal processing method for converting a first information signal including a plurality of pieces of information data into a second information signal including a plurality of pieces of information data. A first step of selecting a plurality of pieces of first information data located around a target position in a second information signal based on a signal, and a plurality of pieces of first information data selected in the first step A second step of extracting a feature amount related to the information data of the position of interest in the second information signal, using a formula (1), and a generation formula for generating coefficient data used in an estimation formula, including the feature value as a parameter. A third step of generating coefficient data used in an estimation expression based on a generation expression using coefficient seed data which is coefficient data of the first step and a feature amount extracted in the second step; and a first information signal. A fourth step of selecting a plurality of pieces of second information data located around the position of interest in the second information signal, based on the coefficient data generated in the third step, and a fourth step of selecting the second information data. A fifth step of calculating pixel data of a target position in the second information signal based on the estimation formula using the plurality of pieces of second information data.
[0011]
A program according to the present invention is for causing a computer to execute the above information signal processing method. A computer-readable medium according to the present invention stores the above-mentioned program.
[0012]
In the present invention, a plurality of first information data located around the target position in the second information signal is selected from the first information signal, and a second information data is selected based on the plurality of first information data. The feature amount related to the information data of the target position in the information signal is extracted. Information data of the target position in the second information signal is generated in accordance with the extracted feature amount.
[0013]
That is, coefficient seed data, which is the coefficient data of the generation formula for generating the coefficient data used in the estimation formula, including the feature amount as a parameter, is stored in the storage means. Using the coefficient seed data and the extracted feature amount, coefficient data of an estimation formula corresponding to the extracted feature amount is generated.
[0014]
In addition, a plurality of pieces of second information data located around the target position in the second information signal are selected from the first information signal. Then, using the coefficient data of the estimation formula and the plurality of pieces of second information data, the pixel data of the target position in the second information signal is obtained based on the estimation formula.
[0015]
As described above, the coefficient data of the estimation formula corresponding to the feature amount related to the information data of the target position in the second information signal is generated and used using the coefficient seed data. Therefore, a plurality of coefficient data are not stored in the coefficient memory in advance, and the memory capacity can be saved and the circuit scale can be reduced. In addition, coefficient data corresponding to the feature amount itself is generated and used. Instead of generating and using coefficient data for each class by classifying the feature amount, the quality of output by the second information signal is improved. it can.
[0016]
In the case where the information signal is a composite color video signal, the characteristic amount extraction means uses the ratio of the change amount of the color signal of the upper line and the lower line to the line where the target position in the second information signal exists as the characteristic amount. By extracting, it is possible to reduce dot disturbance for a pattern in which a color change is mainly observed in the vertical direction.
[0017]
In the case where the information signal is composed of a plurality of pixel data obtained by sampling the NTSC signal as a composite signal at four times the frequency of the chrominance carrier frequency in the phases of the I axis and the Q axis, By extracting the ratio of the amount of change in the vertical direction between the I signal component and the Q signal component in the vicinity of the target position in the information signal of No. 2 as a feature amount, one of the I signal component and the Q signal component is mainly It is possible to reduce dot disturbance for a pattern in which only a large change occurs.
[0018]
Further, the coefficient seed data generation device according to the present invention includes a coefficient used in an estimation formula used when converting a first information signal including a plurality of pieces of information data into a second information signal including a plurality of pieces of information data. An apparatus for generating coefficient seed data, which is coefficient data in a generation formula including a feature value as a parameter, for generating data, wherein a second information signal is generated based on a student signal corresponding to the first information signal. A first data selecting means for selecting a plurality of first information data located around the position of interest in the teacher signal corresponding to the above, and a plurality of the first information data selected by the first data selecting means. A feature value extracting means for extracting a feature value related to the information data at the position of interest in the second information signal, and a feature value extracted by the feature value extracting device in a predetermined range including the feature value. Class code determining means for replacing the representative value with the representative value and determining the representative value as a class code; and second data for selecting a plurality of second information data located around the target position in the teacher signal based on the student signal. Coefficient seed data is obtained by using the selecting means, the class code determined by the class code determining means, the plurality of second information data selected by the second data selecting means, and the information data of the target position in the teacher signal. Calculation means.
[0019]
Further, the coefficient seed data generating method according to the present invention is characterized in that a coefficient used in an estimation formula used when converting a first information signal including a plurality of pieces of information data into a second information signal including a plurality of pieces of information data. A method for generating coefficient seed data, which is coefficient data in a generation formula including a feature value as a parameter, for generating data, wherein a second information signal is generated based on a student signal corresponding to the first information signal. A first step of selecting a plurality of first information data located around the position of interest in the teacher signal corresponding to the first step, and a plurality of first information data selected in the first step. A second step of extracting a feature amount related to the information data of the position of interest in the information signal of No. 2 and placing the feature amount extracted in the second step in a representative value of a predetermined range including the feature amount. In other words, a third step of determining this representative value as a class code, a fourth step of selecting a plurality of pieces of second information data located around the position of interest in the teacher signal based on the student signal, A fifth step of obtaining coefficient seed data using the class code determined in the third step, the plurality of pieces of second information data selected in the fourth step, and the information data of the target position in the teacher signal. Things.
[0020]
A program according to the present invention causes a computer to execute the above-described coefficient seed data generation method. A computer-readable medium according to the present invention stores the above-mentioned program.
[0021]
In the present invention, a plurality of first information data located around a target position in the teacher signal corresponding to the second information signal is selected from the student signal corresponding to the first information signal, and the plurality of first information data are selected. Based on one information data, a feature amount related to the information data of the target position in the teacher signal is extracted. Then, the extracted feature value is replaced with a representative value in a predetermined range including the feature value, and the representative value is determined as a class code.
[0022]
In addition, a plurality of pieces of second information data located around the target position in the teacher signal are selected from the student signal. Then, coefficient seed data is determined using the determined class code, the selected plurality of second information data, and the information data of the target position in the teacher signal. Here, the coefficient seed data is coefficient data in a generation formula for generating coefficient data used in an estimation formula used when converting the first information signal into the second information signal. By using this coefficient seed data, it is possible to obtain coefficient data corresponding to an arbitrary feature amount by a generation formula.
[0023]
BEST MODE FOR CARRYING OUT 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 is supplied with a receiving antenna 101 and a broadcast signal (RF modulated signal) captured by the receiving antenna 101, performs channel selection processing, intermediate frequency amplification processing, detection processing, and the like, and performs composite color video processing. The system includes a tuner 102 that obtains an NTSC signal as a signal, and an analog-to-digital (A / D) converter 103 that converts an NTSC signal output from the tuner 102 from an analog signal to a digital signal.
[0024]
In the A / D converter 103, the NTSC signal is sampled on the I axis and the Q axis at a frequency four times as high as 4 fsc (where fsc is the color width carrier frequency), and is converted from an analog signal to a digital signal. FIG. 2 shows a configuration of pixel data of each field in that case.
[0025]
In FIG. 2, a circle indicates a Y-I signal having a phase of 0 degrees, a square indicates a YQ signal having a phase of 90 degrees, and a black circle indicates a Y + I signal having a phase of 180 degrees. ■ indicates a Y + Q signal having a phase of 270 degrees. As described above, the YI signal, the YQ signal, the Y + I signal, and the Y + Q signal are repeatedly arranged in the horizontal direction, and the YI signal and the Y + I signal are alternately arranged in a certain column in the vertical direction. In the column adjacent thereto, the YQ signal and the Y + Q signal are alternately arranged.
[0026]
Returning to FIG. 1, the television receiver 100 converts the NTSC signal converted into a digital signal by the A / D converter 103 into a component including a baseband luminance signal Y and color difference signals RY and BY. An image signal processing unit 104 that converts the Y, RY, and BY component signals into signals (Y, RY, and BY component signals); It has a matrix circuit 105 for converting into three primary color signals R, G, B, and a display device 106 for displaying an image based on the three primary color signals R, G, B output from the matrix circuit 105. The display device 106 includes, for example, a CRT (cathode-ray tube) display, an LCD (liquid crystal display), a PDP (plasma display panel), and the like.
[0027]
The operation of the television receiver 100 shown in FIG. 1 will be described.
The NTSC signal output from the tuner 102 is converted from an analog signal to a digital signal by an A / D converter 103, and then supplied to an image signal processing unit 104. The image signal processing unit 104 generates pixel data forming each of the luminance signal Y and the color difference signals RY and BY from the pixel data forming the NTSC signal.
[0028]
The Y, RY, and BY component signals output from the image signal processing unit 104 are supplied to a matrix circuit 105 and converted into three primary color signals R, G, and B of red, green, and blue. Then, the three primary color signals R, G, B output from the matrix circuit 105 are supplied to the display device 106. Thus, the image related to the channel selected by the tuner 102 is displayed on the display device 106 by the three primary color signals R, G, and B.
[0029]
Next, details of the image signal processing unit 104 will be described with reference to FIG.
The image signal processing unit 104 receives an NTSC signal converted into a digital signal by the A / D converter 103 (see FIG. 1), and inputs the NTSC signal. A tap selection unit 112 for selectively extracting and outputting a plurality of pixel data located around the target position in the RY and BY component signals.
[0030]
The tap selection unit 112 extracts a plurality of pixel data of a prediction tap used for prediction from the NTSC signal. FIG. 4 shows an example of the structure of the prediction tap. In this case, the prediction tap is x₁~ X₁₆, But x₁~ X_FifteenAre extracted from the NTSC signal. x₁₆Is a constant, and is used to adjust a DC (Direct @ Current) -like offset (offset) generated between the two signals at the time of composite-component signal conversion.
[0031]
Further, the image signal processing unit 104 extracts a feature amount related to pixel data of a target position in the Y, RY, BY component signal from the NTSC signal input to the input terminal 111. have.
[0032]
FIG. 5 illustrates an example of the feature amount extraction unit 113. The feature amount extraction unit 113 shown in FIG. 5 sets the ratio of the change amount of the color signal of the upper line and the color signal of the lower line to the line where the target position exists in the Y, RY, BY component signal as the feature amount v. It is to extract.
[0033]
In FIG. 5, the NTSC signal is supplied to a band-pass filter (BPF) 121 having a center frequency of fsc (fsc is a color width carrier frequency), and the band-pass filter 121 extracts a color signal C from the NTSC signal. The color signal C extracted by the band-pass filter 121 is supplied to a series circuit of delay circuits 122 and 123 having a delay amount of one horizontal period.
[0034]
Further, the output signal of the delay circuit 122 and the output signal of the delay circuit 123 are added by the adder circuit 124, and the added value is supplied to the absolute value conversion circuit 125 and the absolute value L₁Is obtained. Similarly, the output signal of the band-pass filter 121 and the output signal of the delay circuit 122 are added by the adding circuit 126, and the added value is supplied to the absolute value converting circuit 127 to output the absolute value L₂Is obtained.
[0035]
In this case, assuming that the pixel data output from the delay circuit 122 is the pixel data a0 at the same position in the vertical and horizontal directions as the target position in the Y, RY, and BY component signals, as shown in FIG. As described above, the pixel data output from the delay circuit 123 is pixel data au at the same position in the horizontal direction on one line in the vertical direction with respect to the pixel data a0, and the pixel data output from the bandpass filter 121 is The pixel data ad is located one line below the data a0 in the vertical direction and at the same position in the horizontal direction. The addition circuit 124 adds the pixel data a0 and au, and the addition circuit 126 adds the pixel data a0 and ad.
[0036]
In the NTSC signal, the phase of the chrominance carrier is inverted every line. Therefore, the absolute value L obtained from the absolute value conversion circuit 125₁Indicates the amount of change in the color signal C of the line above the line where the target position exists in the Y, RY, BY component signals. In this case, if the correlation between the color signal C of the line where the target position exists and the color signal C of the line above it is high, the absolute value L₁Becomes smaller.
[0037]
Similarly, the absolute value L obtained from the absolute value conversion circuit 127₂Indicates the amount of change in the color signal C of the line below the line where the target position exists in the Y, RY, BY component signals. In this case, if the correlation between the color signal C of the line where the target position exists and the color signal C of the line below the line is high, the absolute value L₂Becomes smaller.
[0038]
Absolute value L obtained by absolute value conversion circuits 125 and 127₁, L₂Is supplied to the division circuit 128, and the characteristic amount v = L₂/ L₁Is obtained. This feature amount v indicates the ratio of the change amount of the color signal of the upper line and the lower line to the line where the target position exists in the Y, RY, and BY component signals.
[0039]
As described above, the feature amount extraction unit 113 shown in FIG. 5 uses the pixel data a0, au, and ad to extract the pixel data of the target position in the Y, RY, and BY component signals as shown in FIG. To obtain a feature value v related to. Therefore, the processing in the feature amount extraction unit 113 shown in FIG. 5 selects pixel data a0, au, and ad located around the target position in the Y, RY, and BY component signals based on the NTSC signal. This is equivalent to extracting the feature amount v using the selected pixel data a0, au, and ad.
[0040]
FIG. 7 shows another example of the feature amount extraction unit 113. The feature value extraction unit 113 shown in FIG. 7 calculates the ratio of the change amount of each of the I signal component and the Q signal component in the Y, RY, and BY component signals near the target position in the vertical direction to the feature value v. Is extracted as 7, parts corresponding to those in FIG. 5 are denoted by the same reference numerals, and detailed description thereof will be omitted.
[0041]
In FIG. 7, the absolute value L obtained by the absolute value conversion circuits 125 and 127 is shown.₁, L₂Is supplied to the addition circuit 131 and added. The addition value T output from the addition circuit 131₂Is supplied to a delay circuit 132 having a delay time of one pixel period.
[0042]
Here, assuming that the pixel data output from the delay circuit 122 is the pixel data a0 at the same position in the vertical and horizontal directions as the target position in the Y, RY, and BY component signals, as shown in FIG. As described above, the pixel data output from the delay circuit 123 is pixel data au at the same position in the horizontal direction on one line in the vertical direction with respect to the pixel data a0, and the pixel data output from the bandpass filter 121 is The pixel data ad is located one line below the data a0 in the vertical direction and at the same position in the horizontal direction. The addition circuit 124 adds the pixel data a0 and au, and the addition circuit 126 adds the pixel data a0 and ad.
[0043]
In the NTSC signal, the phase of the chrominance carrier is inverted every line. Therefore, the absolute value L obtained from the absolute value conversion circuit 125₁Indicates the amount of change in the color signal C of the line above the line where the target position exists in the Y, RY, BY component signals. In this case, if the correlation between the color signal C of the line where the target position exists and the color signal C of the line above it is high, the absolute value L₁Becomes smaller.
[0044]
Similarly, the absolute value L obtained from the absolute value conversion circuit 127₂Indicates the amount of change in the color signal C of the line below the line where the target position exists in the Y, RY, BY component signals. In this case, if the correlation between the color signal C of the line where the target position exists and the color signal C of the line below the line is high, the absolute value L₂Becomes smaller.
[0045]
Therefore, the addition value T obtained from the addition circuit 131₂Indicates the amount of change in the I signal component or the Q signal component in the vertical direction including the target position in the Y, RY, and BY component signals. At this time, the added value T output from the delay circuit 132₁Is the added value T obtained from the adder 131 one pixel period ago.₂It is. That is, this added value T₁Is obtained based on pixel data a0 ', au', ad 'horizontally adjacent to the pixel data a0, au, ad, as shown in FIG. Therefore, the added value T obtained from the delay circuit 132₁Indicates the amount of change in the Q signal component or the I signal component in the vertical direction including the position one pixel before the target position in the Y, RY, and BY component signals.
[0046]
In the pixel data example shown in FIG. 8, since the target position corresponds to the position where the I signal component exists, the added value T₂Indicates the amount of change in the I signal component in the vertical direction, while the added value T₁Indicates the amount of change in the Q signal component in the vertical direction.
[0047]
Addition value T output from addition circuit 131₂And added value T output from delay circuit 132₁Is supplied to the division circuit 133, and the characteristic amount v = T₂/ T₁Is obtained. The feature amount v indicates the ratio of the change amount in the vertical direction of each of the I signal component and the Q signal component near the target position in the Y, RY, and BY component signals.
[0048]
As described above, the feature quantity extraction unit 113 shown in FIG. 7 uses the pixel data a0, au, ad, a0 ', au', and ad 'as shown in FIG. This is for obtaining a feature amount v related to pixel data at a position of interest in the Y component signal. Accordingly, the processing in the feature amount extraction unit 113 shown in FIG. 7 is based on the NTSC signal, and the pixel data a0, au, ad, a0 'located around the target position in the Y, RY, BY component signal. , Au ′, ad ′, and extracting the feature amount v using the selected pixel data a0, au, ad, a0 ′, au ′, ad ′.
[0049]
In the example of the pixel data shown in FIG. 8, the pixel data a0, au, and ad and the pixel data a0 ', au', and ad 'one pixel before the pixel data are used. Pixel data one pixel after pixel data a0, au, and ad may be used as ad '. In that case, the addition value T₁Are obtained using the pixel data a0, au, and ad, and the added value T₂Are obtained using the pixel data a0 ', au', and ad '.
[0050]
Returning to FIG. 3, the image signal processing unit 104 further includes a coefficient generation unit 114 that generates coefficient data Wi (i = 1 to n) corresponding to the feature amount v extracted by the feature amount extraction unit 113. ing. Here, n represents the number of prediction taps selected by the tap selection unit 112. The coefficient data Wi is used in an estimation formula used in the estimation prediction operation unit 116 described later, and is information for converting an NTSC signal into a Y, RY, and BY component signal.
[0051]
The coefficient generation unit 114 generates coefficient data Wi for obtaining each of the luminance signal Y and the color difference signals RY and BY constituting the Y, RY, and BY component signals. The coefficient data Wi (each signal of the luminance signal Y and the color difference signals RY and BY) generated by the coefficient generation unit 114 is supplied to the estimation prediction calculation unit 116.
[0052]
Further, the image signal processing unit 104 includes a coefficient seed memory 115. In the estimation prediction calculation unit 116, the luminance signal Y and the color difference signals R-Y, B- are calculated from the pixel data xi of the prediction tap and the coefficient data Wi generated in the coefficient generation unit 114 by the estimation expression of Expression (1). Pixel data y at the position of interest in each of Y is calculated.
[0053]
(Equation 1)

[0054]
The coefficient data Wi of the estimation expression is generated by a generation expression including the feature amount v as a parameter as shown in Expression (2). The coefficient seed memory 115 stores the coefficient seed data w which is the coefficient data in this generation formula._i0~ W_i3(For each of the luminance signal Y and the color difference signals RY and BY). In the coefficient generation unit 114 described above, the coefficient seed data w_i0~ W_i3Is used, coefficient data Wi is generated based on the generation expression of Expression (2).
Wi = w_i0+ W_i1v + w_i2v²+ W_i3v³... (2)
[0055]
Further, the image signal processing unit 104 includes pixel data xi of the prediction tap selectively extracted by the tap selection unit 112 and coefficient data Wi (luminance signal Y, color difference signals RY, B) generated by the coefficient generation unit 114. −Y signal components) to calculate the pixel data y of the target position in each of the luminance signal Y and the color difference signals RY, BY constituting the Y, RY, and BY component signals. It has a prediction operation unit 116 and an output terminal 117 that outputs a Y, RY, BY component signal composed of pixel data y calculated by the estimation prediction operation unit 116.
[0056]
Next, the operation of the image signal processing unit 104 shown in FIG. 3 will be described.
The NTSC signal input to the input terminal 111 is supplied to the feature amount extraction unit 113. The feature amount extraction unit 113 extracts a feature amount v associated with pixel data of a target position in the Y, RY, and BY component signals from the NTSC signal. The feature amount v extracted by the feature amount extraction unit 113 is supplied to the coefficient generation unit 114.
[0057]
The coefficient generation unit 114 stores the coefficient seed data w stored in the coefficient seed memory 115._{i0 ~ wi3}(The luminance signal Y and the color difference signals RY and BY), and the luminance signals constituting the Y, RY and BY component signals based on the generation formula of the expression (2). For obtaining each of the Y and color difference signals RY and BY, coefficient data Wi corresponding to the feature value v is generated.
[0058]
The coefficient data Wi (the luminance signal Y, the color difference signals RY, and BY signals) generated by the coefficient generation unit 114 are supplied to the estimation prediction calculation unit 116. The NTSC signal input to the input terminal 111 is supplied to the tap selection unit 112. The tap selection unit 112 selectively extracts, from the NTSC signal, pixel data xi of a prediction tap located around the target position in the Y, RY, and BY component signals. The pixel data xi of the prediction tap extracted by the tap selection unit 112 is supplied to the estimation prediction calculation unit 116.
[0059]
The estimation / prediction calculation unit 116 uses the pixel data xi of the prediction tap and the coefficient data Wi (for each signal of the luminance signal Y and the color difference signals RY and BY) to calculate the estimation equation (1). Based on this, the pixel data y of the target position in each of the luminance signal Y and the color difference signals RY and BY constituting the Y, RY and BY component signals is calculated. The output terminal 117 outputs the Y, RY, and BY component signals composed of the pixel data y calculated by the estimation / prediction calculation unit 116.
[0060]
As described above, the NTSC signal can be converted into the Y, RY, and BY component signals by the image signal processing unit 104 shown in FIG.
In the image signal processing unit 104, the coefficient data Wi corresponding to the characteristic amount v associated with the pixel data at the target position in the Y, RY, BY component components extracted by the characteristic amount extraction unit 113 It is generated and used by the generation unit 114. Therefore, a plurality of coefficient data are not stored in the coefficient memory in advance, and the memory capacity can be saved and the circuit scale can be reduced.
[0061]
Further, in the image signal processing unit 104, the coefficient data Wi corresponding to the feature amount v itself related to the pixel data of the target position in the Y, RY, and BY component signals extracted by the feature amount extraction unit 113 is calculated. It is generated and used by the coefficient generation unit 114. Therefore, instead of classifying the feature amount v to generate and use the coefficient data Wi for each class, it is possible to improve the image quality of the image using the Y, RY, and BY component signals obtained by conversion. it can.
[0062]
Further, in the image signal processing unit 104, the feature amount extraction unit 113 is configured as shown in FIG. 5, and the upper and lower lines of the line where the target position exists in the Y, RY, and BY component signals are located. By extracting the ratio of the change amount of the color signal as the feature amount v, it is possible to reduce the dot disturbance for a pattern in which the color change is mainly observed in the vertical direction.
[0063]
FIG. 9 shows the relationship between the feature value v extracted by the feature value extraction unit 113 shown in FIG. 5 and the coefficient data Wi of each tap generated using the feature value v in the prediction tap structure shown in FIG. An example is shown. The horizontal axis in FIG. 9 is the logarithmic value of the feature value v. In FIG. 9, the tap x corresponding to the target position₅Are normalized so that the coefficient data of becomes 1.
[0064]
As is clear from this figure, the tap x corresponding to the upper line₁, X₂, X₃Is the feature value v (= L₂/ L₁) Increases, that is, as the color correlation of the upper line with respect to the line where the target position exists is higher than that of the lower line. On the other hand, tap x corresponding to the lower line₇, X₈, X₉The absolute value of the coefficient increases as the feature amount v decreases, that is, as the color correlation between the lower line and the upper line with respect to the line where the target position exists increases.
[0065]
In the image signal processing unit 104, the feature amount extraction unit 113 is configured as shown in FIG. 7, and the I signal component and the Q signal component in the vicinity of the target position in the Y, RY, and BY component signals respectively. By extracting the ratio of the change amount in the vertical direction as the feature amount v, it is possible to reduce the dot disturbance mainly in the pattern in which only one of the I signal component and the Q signal component largely changes in the vertical direction. it can.
[0066]
FIG. 11 shows the relationship between the feature value v extracted by the feature value extraction unit 113 shown in FIG. 7 and the coefficient data Wi of each tap generated using the feature value v in the prediction tap structure shown in FIG. An example is shown. The horizontal axis in FIG. 11 is the logarithmic value of the feature value v. FIG. 11 shows an example in which the target position corresponds to the Y + I signal.
[0067]
As is clear from this figure, for example, the tap x corresponding to the Y + I signal₈Is a feature amount v (= T₂/ T₁) Becomes smaller, that is, as the change amount of the I signal component in the vertical direction becomes smaller than the change amount of the Q signal component in the vertical direction. On the other hand, for example, a tap x corresponding to the Y + Q signal and the Y-Q signal₇, X₉Is the feature value v (= T₂/ T₁) Increases, that is, as the amount of change in the vertical direction of the Q signal component becomes smaller than the amount of change in the vertical direction of the I signal component.
[0068]
Next, an example of a method of generating coefficient seed data will be described. In this example, the coefficient seed data w which is the coefficient data in the generation equation of the above-described equation (2)_i0~ W_i3Let us show an example of finding.
Here, for the following description, as shown in Expression (3), t_j(J = 0 to 3) is defined.
t₀= 1, t₁= V, t₂= V², T₃= V³... (3)
Using this equation (3), equation (2) can be rewritten as equation (4).
[0069]
(Equation 2)

[0070]
Finally, the undetermined coefficient w_ijAsk for. That is, the coefficient value that minimizes the square error is determined 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. This is a so-called least squares method. Let the number of learning be m and the residual in k-th (1 ≦ k ≦ m) learning data be e_k, And the total sum of the square errors is E, E is expressed by equation (5) using equations (1) and (2). Where x_ikIs the k-th pixel data at the i-th predicted tap position of the student signal, y_kRepresents the pixel data of the k-th teacher signal corresponding thereto.
[0071]
(Equation 3)

[0072]
In the solution by the method of least squares, w of equation (5) is used._ijW such that the partial differential by_ijAsk for. This is shown by equation (6).
[0073]
(Equation 4)

[0074]
Hereinafter, as shown in Expressions (7) and (8), X_ipjq, Y_ipIs defined, Expression (6) can be rewritten using a matrix as in Expression (9).
[0075]
(Equation 5)

[0076]
(Equation 6)

[0077]
This equation is generally called a normal equation. This normal equation is expressed by w using a sweeping method (Gauss-Jordan elimination method) or the like._ijAnd the coefficient seed data w_i0~ W_i3Is calculated.
[0078]
FIG. 12 shows coefficient seed data w based on an example of the above-described method of generating coefficient seed data._i0~ W_i32 shows a configuration of a coefficient seed data generation device 150 for generating a coefficient seed data.
[0079]
The coefficient seed data generation device 150 includes an input terminal 151 to which a teacher signal ST corresponding to the Y, RY, and BY component signals is input, and encodes the teacher signal ST to generate a student signal SS. And an NTSC encoder 152 that obtains an NTSC signal as
[0080]
In addition, the coefficient seed data generation device 150 includes a feature amount extraction unit 153 that extracts a feature amount v associated with pixel data of a target position in the teacher signal ST from the NTSC signal obtained by the NTSC encoder 152. The feature amount extraction unit 153 has the same configuration as the feature amount extraction unit 113 of the image signal processing unit 104 described above.
[0081]
Further, the coefficient seed data generation device 150 replaces the feature value v extracted by the feature value extraction unit 153 with a representative value in a predetermined range including the feature value v, and determines the representative value as a class code CL. It has a portion 154.
[0082]
In this case, as shown in FIG. 13, M class ranges are set so as to be equally spaced on the log scale within the change range of the feature amount v. The class code determination unit 154 determines in which class range the feature value v extracted by the feature value extraction unit 153 is included, and determines a representative value of the included class range, for example, an intermediate value as the class code CL. . For example, when the feature amount v extracted by the feature amount extraction unit 153 is included in the class range 3, the representative value V of the class range 3₃Becomes the class code CL.
[0083]
As described above, by changing the feature value v extracted by the feature value extraction unit 153 to a representative value of the class range including the feature value v and using it, learning data necessary for generating a normal equation described later can be obtained. You can easily get it. When the feature amount v extracted by the feature amount extraction unit 153 is used as it is, it is necessary to obtain learning data of the number of prediction taps or more corresponding to each value of the feature amount v, which is extremely difficult.
[0084]
Further, the coefficient seed data generation device 150 includes a tap selection unit 155 that 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 152. Have. The tap selection unit 155 has the same configuration as the tap selection unit 112 of the image signal processing unit 104 described above.
[0085]
Further, the coefficient seed data generating device 150 includes a delay circuit 156 for adjusting the time of the teacher signal ST supplied to the input terminal 151, and each target position obtained from the teacher signal ST time-adjusted by the delay circuit 156. , Pixel data xi of the prediction tap selectively extracted by the tap selection unit 155 corresponding to the pixel data y of each target position, and a class corresponding to the pixel data y of each target position. From the class code CL determined by the code determination unit 154 (representative value of the feature amount v extracted by the feature amount extraction unit 153), the coefficient seed data w_i0~ W_i3Has a normal equation generating unit 157 for generating the normal equation of the expression (9) for obtaining the equation (9).
[0086]
In this case, one piece of learning data is generated by a combination of one piece of pixel data y and corresponding pixel data xi of n prediction taps. Further, the class code CL is supplied from the class code determination unit 154 corresponding to each of the learning data. Accordingly, the normal equation generation unit 157 generates a plurality of class codes CL (v in FIG. 13).₁~ V_MIs generated, a normal equation in which a large amount of learning data relating to the reference data are registered, and coefficient seed data w_i0~ W_i3Can be obtained.
[0087]
Note that the coefficient seed data w_i0~ W_i3It 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 157 generates a normal equation corresponding to each of the luminance signal Y and the color difference signals RY and BY. 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 equation corresponding to each of the color difference signals RY and BY is generated by using the pixel data of the color difference signals RY and BY as the pixel data y of the target position.
[0088]
The coefficient seed data generation device 150 is supplied with the data of the normal equation generated by the normal equation generation unit 157, solves the normal equation, and obtains the coefficient seed data w_i0~ W_i3And the coefficient seed data w_i0~ W_i3And a coefficient seed memory 159 for storing In the coefficient seed data determining unit 158, the normal equation is solved by, for example, a sweeping method or the like, and the coefficient seed data w_i0~ W_i3Is required.
[0089]
The operation of the coefficient seed data generation device 150 shown in FIG. 12 will be described.
The input terminal 151 is supplied with Y, RY, and BY component signals as the teacher signal ST. The Y, RY, and BY component signals are supplied to the NTSC encoder 152. The NTSC encoder 152 performs an encoding process on the teacher signal ST to generate an NTSC signal as the student signal SS.
[0090]
The student signal SS obtained by the NTSC encoder 152 is supplied to the feature amount extraction unit 153. The feature amount extraction unit 153 extracts a feature amount v associated with pixel data of a target position in the teacher signal ST from the NTSC signal. The feature value v extracted by the feature value extraction unit 153 is supplied to the class code determination unit 154.
[0091]
The class code determination unit 154 replaces the feature value v with a representative value in a predetermined range including the feature value v, and determines the representative value as the class code CL. The class code CL determined by the class code determination unit 154 is supplied to the normal equation generation unit 157.
[0092]
Further, the student signal SS obtained by the NTSC encoder 152 is supplied to the tap selection unit 155. The tap selection unit 155 selectively extracts pixel data of a prediction tap located around the target position in the teacher signal ST from the student signal SS. The pixel data xi of the prediction tap extracted by the tap selection unit 155 as described above is supplied to the normal equation generation unit 157.
[0093]
The normal equation generation unit 157 selects pixel data y of each target position obtained from the teacher signal ST time-adjusted by the delay circuit 156, and selectively selects the pixel data y of each target position by the tap selection unit 155. , And the class code CL determined by the class code determination unit 154 corresponding to the pixel data y of each target position, respectively, using the coefficient seed data w_i0~ W_i3A normal equation of the equation (9) for obtaining the following equation is generated.
[0094]
Then, the data of the normal equation generated by the normal equation generation unit 157 is supplied to the coefficient seed data determination unit 158. The coefficient seed data determination unit 158 solves the normal equation to calculate the coefficient seed data w_i0~ W_i3(For each of the luminance signal Y and the color difference signals RY and BY). Then, the coefficient seed data w thus obtained_i0~ W_i3Are stored in the coefficient seed memory 159.
[0095]
As described above, in the coefficient seed data generation device 150 illustrated in FIG. 12, the coefficient data Wi used in the estimation expression of the expression (1) and stored in the coefficient seed memory 115 of the image signal processing unit 104 is obtained by ( 2) Coefficient seed data w which is coefficient data in the generation equation of the equation_i0~ W_i3Can be produced favorably.
[0096]
Next, another example of a method of generating coefficient seed data will be described. Also in this example, the coefficient seed data w which is the coefficient data in the generation equation of the above-described equation (2) is used._i0~ W_i3Let us show an example of finding.
First, a method of obtaining coefficient data of the estimation formula will be described. Here, an example is shown in which coefficient data Wi (i = 1 to n) of the estimation expression of Expression (1) is obtained by the least square method.
[0097]
In the above equation (1), before learning, the coefficient data W₁, W₂, ‥‥, W_nIs an undetermined coefficient. The learning is performed on a plurality of signal data for each class. When the number of learning data is m, the following equation (10) is set according to the equation (1). n indicates the number of prediction taps.
y_k= W₁× x_k1+ W₂× x_k2+ ‥‥ + W_n× x_kn... (10)
(K = 1, 2, ‥‥, m)
[0098]
If m> n, the coefficient data W₁, W₂, ‥‥, W_nIs not uniquely determined, so the element e of the error vector e_kIs defined by the following equation (11), and e in the equation (12)²Find coefficient data that minimizes The coefficient data is uniquely determined by the so-called least square method.
e_k= Y_k− ｛W₁× x_k1+ W₂× x_k2+ ‥‥ + w_n× x_kn｝ ・ ・ ・ (11)
(K = 1,2, ‥‥ m)
[0099]
(Equation 7)

[0100]
E in equation (12)²As a practical calculation method for obtaining coefficient data that minimizes the following equation, first, as shown in Expression (13), e²May be partially differentiated by coefficient data Wi (i = 1, 2,..., N), and coefficient data Wi may be obtained such that the partial differential value becomes 0 for each value of i.
[0101]
(Equation 8)

[0102]
A specific procedure for obtaining the coefficient data Wi from the equation (13) will be described. When Xji and Yi are defined as in Expressions (14) and (15), Expression (13) can be written in the form of a determinant of Expression (16).
[0103]
(Equation 9)

[0104]
(Equation 10)

[0105]
The expression (16) is generally called a normal equation. By solving this normal equation by a general solution method such as a sweeping-out method (Gauss-Jordan elimination method), coefficient data Wi (i = 1, 2,..., N) can be obtained.
[0106]
Next, coefficient seed data w using coefficient data Wi generated corresponding to each class code is used._i0~ W_i3Explain how to find.
The coefficient data of a certain class code CL is k_viLet's say Here, i is the number of the prediction tap. This k_viFrom the coefficient seed data w_i0~ W_i3Ask for.
[0107]
The coefficient data Wi (i = 1 to n) is the coefficient seed data w_i0~ W_i3And is expressed by the above equation (2). Here, considering that the least squares method is used for the coefficient data Wi, the residual is represented by the equation (17).
[0108]
[Equation 11]

[0109]
Where t_jIs shown in the above equation (3). When the least squares method is applied to equation (17), equation (18) is obtained.
[0110]
(Equation 12)

[0111]
Where X_jk, Y_jAre defined as Expressions (19) and (20), respectively, Expression (18) can be rewritten as Expression (21). This equation (21) is also a normal equation. By solving this equation by a general solution such as a sweeping-out method, the coefficient seed data w_i0~ W_i3Can be calculated.
[0112]
(Equation 13)

[0113]
FIG. 14 shows coefficient seed data w based on another example of the above-described method of generating coefficient seed data._i0~ W_i3Shows a configuration of a coefficient seed data generation device 150A for generating a. 14, parts corresponding to those in FIG. 12 are denoted by the same reference numerals, and detailed description thereof will be omitted.
[0114]
The coefficient seed data generation device 150A selects the pixel data y of each target position obtained from the teacher signal ST time-adjusted by the delay circuit 159, and selects the pixel data y of each target position by the tap selection unit 155. The class code CL determined by the class code determination unit 154 (corresponding to the pixel data xi of the prediction taps that are extracted dynamically and the pixel data y of each target position (the characteristic amount v of the characteristic amount v extracted by the characteristic amount extraction unit 153). From the (representative value) for each class code, a normal equation generation unit 161 for generating a normal equation of Expression (16) for obtaining coefficient data Wi (i = 1 to n).
[0115]
In this case, one piece of learning data is generated by a combination of one piece of pixel data y and corresponding pixel data xi of n prediction taps. Further, the class code CL is supplied from the class code determination unit 154 corresponding to each of the learning data. The normal equation generation unit 161 generates a normal equation in which a large amount of learning data is registered for each class code. This makes it possible to obtain coefficient data Wi (i = 1 to n) for each class code.
[0116]
Further, the coefficient seed data generation device 150A is supplied with the data of the normal equation generated by the normal equation generation unit 161, solves the normal equation, and obtains a coefficient data determination unit 162 for obtaining coefficient data Wi for each class code. , The coefficient seed data w using the class code CL and the coefficient data Wi corresponding to each class code CL._i0~ W_i3And a normal equation generating unit 163 for generating a normal equation of the equation (21) for obtaining the equation (21).
[0117]
The coefficient seed data generation device 150A is supplied with the data of the normal equation generated by the normal equation generation unit 163, and solves the normal equation to obtain the coefficient seed data w._i0~ W_i3And the coefficient seed data w obtained by_i0~ W_i3And a coefficient seed memory 159 for storing
The other components of the coefficient seed data generation device 150A shown in FIG. 14 are configured similarly to the coefficient seed data generation device 150 shown in FIG.
[0118]
Next, the operation of the coefficient seed data generation device 150A shown in FIG. 14 will be described.
The input terminal 151 is supplied with Y, RY, and BY component signals as the teacher signal ST. The Y, RY, and BY component signals are supplied to the NTSC encoder 152. The NTSC encoder 152 performs an encoding process on the teacher signal ST to generate an NTSC signal as the student signal SS.
[0119]
The student signal SS obtained by the NTSC encoder 152 is supplied to the feature amount extraction unit 153. The feature amount extraction unit 153 extracts a feature amount v associated with pixel data of a target position in the teacher signal ST from the NTSC signal. The feature value v extracted by the feature value extraction unit 153 is supplied to the class code determination unit 154.
[0120]
The class code determination unit 154 replaces the feature value v with a representative value in a predetermined range including the feature value v, and determines the representative value as the class code CL. The class code determined by the class code determination unit 154 is supplied to the normal equation generation unit 161.
[0121]
Further, the student signal SS obtained by the NTSC encoder 152 is supplied to the tap selection unit 155. The tap selection unit 155 selectively extracts pixel data of a prediction tap located around the target position in the teacher signal ST from the student signal SS. The pixel data xi of the prediction tap extracted by the tap selection unit 155 as described above is supplied to the normal equation generation unit 161.
[0122]
The normal equation generating unit 161 selectively outputs the pixel data y of each target position obtained from the teacher signal ST time-adjusted by the delay circuit 156 and the tap selecting unit 155 corresponding to the pixel data y of each target position. , And the class data CL determined by the class code determination unit 154 in correspondence with the pixel data y at each target position, and the coefficient data Wi ( A normal equation of the formula (16) for obtaining i = 1 to n) is generated.
[0123]
Then, the normal equation is solved by the coefficient data determination unit 162, and coefficient data Wi corresponding to each class code is obtained. The normal equation generation unit 163 uses the class code CL and coefficient data Wi corresponding to each class code to generate coefficient seed data w._i0~ W_i3The normal equation of the equation (21) for obtaining the following equation is generated.
Then, the normal equation is solved by the coefficient seed data determining unit 164, and the coefficient seed data w_i0~ W_i3Is obtained, and the coefficient seed data w_i0~ W_i3Are stored in the coefficient seed memory 159.
[0124]
As described above, also in the coefficient seed data generation device 150A illustrated in FIG. 14, the coefficient data Wi used in the estimation expression of the expression (1) and stored in the coefficient seed memory 115 of the image signal processing unit 104 is obtained by ( 2) Coefficient seed data w which is coefficient data in the generation equation of the equation_i0~ W_i3Can be produced favorably.
[0125]
Note that the processing in the image signal processing unit 104 in FIG. 1 can be realized by software using, for example, an image signal processing device 300 as shown in FIG.
[0126]
First, the image signal processing device 300 shown in FIG. 15 will be described. The image signal processing apparatus 300 includes a CPU 301 for controlling the operation of the entire apparatus, a ROM (read only memory) 302 in which an operation program of the CPU 301 and coefficient type data are stored, and a RAM (work area) for the CPU 301. (random @ access @ memory) 303. The CPU 301, the ROM 302, and the RAM 303 are connected to a bus 304, respectively.
[0127]
Further, the image signal processing device 300 has a hard disk drive (HDD) 305 as an external storage device, and a drive (FDD) 307 for driving a floppy (registered trademark) disk 306. These drives 305 and 307 are connected to the bus 304, respectively.
[0128]
In addition, the image signal processing device 300 includes a communication unit 308 that connects 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.
[0129]
Further, the image signal processing device 300 includes a user interface unit. The user interface unit has a remote control signal receiving circuit 310 for receiving a remote control signal RM from the remote control transmitter 200, and a display 311 such as an LCD (liquid crystal display). 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.
[0130]
Further, the image signal processing device 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 the output terminal 315 is similarly connected to the bus 304 via the interface 317.
[0131]
Here, instead of previously storing the processing program, coefficient seed data, and the like in the ROM 302 as described above, for example, the processing program and the coefficient seed data are downloaded from the communication network 400 such as the Internet via the communication unit 308 and stored in the hard disk or the 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.
[0132]
Instead of inputting the NTSC signal to be processed from the input terminal 314, the NTSC signal may be recorded in the hard disk drive 305 in advance or downloaded from the communication network 400 such as the Internet via the communication unit 308. Also, instead of outputting the processed Y, RY, and BY component signals to the output terminal 315, or in parallel with this, the image signal is supplied to the display 311 for image display, and further stored in the hard disk. The information may be transmitted to a communication network 400 such as the Internet via the communication unit 308.
[0133]
With reference to the flowchart of FIG. 16, a processing procedure for obtaining Y, RY, and BY component signals from the NTSC signal in the image signal processing device 300 shown in FIG. 15 will be described.
First, in step ST1, the process is started, and in step ST2, an NTSC signal for one frame or one field is input into the apparatus from, for example, 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. When the NTSC signal is recorded in the hard disk drive 305 in the apparatus in advance, the NTSC signal is read from the drive 305, and pixel data constituting the NTSC signal is temporarily stored in the RAM 303.
[0134]
Then, in step ST3, it is determined whether or not processing of all frames or all fields of the NTSC signal has been completed. If the process has been completed, the process ends in step ST4. On the other hand, if the processing has not been completed, the process proceeds to step ST5.
[0135]
In step ST5, a pixel at a target position (target pixel) in the Y, RY, and BY component signals is selected. Then, in step ST6, a plurality of pieces of pixel data located around the target position are acquired from the NTSC signal input in step ST2.
[0136]
Next, in step ST7, based on the plurality of pixel data obtained in step ST6, a feature amount v related to the pixel data of the target position in the Y, RY, and BY component signals is calculated (FIG. 5, see FIG. 7). Then, in step ST8, the coefficient seed data w stored in the ROM 302 based on the feature amount v._i0~ W_i3Then, the coefficient data Wi (for each signal of the luminance signal Y and the color difference signals RY and BY) is generated by the generation expression of the expression (2).
[0137]
Next, in step ST9, using the coefficient data Wi generated in step ST8 and the plurality of pixel data (prediction taps) xi obtained in step ST6, Y, The pixel data of the target position in the RY, BY component signals (luminance signal Y, color difference signals RY, BY) is generated.
[0138]
Next, in step ST10, the Y, RY, BY component signals (the luminance signal Y, the color difference signal R- (Y, BY) pixel data is determined. If the processing has been completed, the process returns to step ST2, and shifts to the input processing of the NTSC signal for the next one frame or one field. On the other hand, if the processing has not been completed, the process returns to step ST5, the next target pixel is selected, and the same processing as described above is repeated.
[0139]
As described above, by performing the processing according to the flowchart shown in FIG. 16, the pixel data of the input NTSC signal is processed, and the pixel data of the Y, RY, and BY component signals can be obtained. . As described above, the Y, RY, and BY component signals obtained by the above-described processing are output to the output terminal 315 or supplied to the display 311 to display an image based on the output. The data is supplied to the hard disk drive 305 and recorded on the hard disk.
[0140]
Although illustration of the processing device is omitted, the processing in the coefficient seed data generation device 150 in FIG. 12 can be realized by software.
[0141]
The processing procedure for generating coefficient seed data will be described with reference to the flowchart in FIG.
First, in step ST21, processing is started, and in step ST22, a teacher signal (Y, RY, BY component signal) is input for one frame or one field. Then, in step ST23, a student signal (NTSC signal) is generated from the input teacher signal.
[0142]
Next, in step ST24, it is determined whether the learning process has been completed for all frames or all fields of the teacher signal. If not, the process proceeds to step ST25.
In step ST25, the pixel at the position of interest (pixel of interest) in the teacher signal (Y, RY, BY component signal) is selected. Then, in step ST26, a plurality of pixel data located around the target position are acquired from the student signal (NTSC signal) generated in step ST23.
[0143]
Next, in step ST27, based on the plurality of pixel data obtained in step ST26, a feature amount v related to the pixel data of the target position in the teacher signal is obtained by calculation (see FIGS. 5 and 7). Further, in step ST28, the characteristic amount v obtained in step ST27 is replaced with a representative value in a predetermined range including the characteristic amount v, and the representative value is determined as a class code CL (see FIG. 13).
[0144]
Next, in step ST29, a normal equation (see equation (9)) is generated. Then, in step ST30, it is determined whether or not the learning process has been completed in all regions of the pixel data of the teacher signal input in step ST22. If completed, the process returns to step ST22 to input the next one frame or one field of teacher signal, and repeat the same processing as described above. On the other hand, if the processing has not been completed, the process returns to step ST25, the next target pixel is selected, and the same processing as described above is repeated.
[0145]
If the learning process has been completed for all frames or all fields of the teacher signal in step ST24, the process proceeds to step ST31. In this step ST31, the coefficient seed data w_i0~ W_i3Is calculated, and in step ST32, the coefficient seed data w_i0~ W_i3Is stored in the memory, and then the process ends in step ST33.
[0146]
In this way, by performing the processing according to the flowchart shown in FIG. 17, the coefficient seed data can be obtained by the same method as that of the coefficient seed data generation device 150 shown in FIG.
Further, although the illustration of the processing device is omitted, the processing in the coefficient seed data generation device 150A of FIG. 14 can also be realized by software.
[0147]
A processing procedure for generating coefficient seed data will be described with reference to the flowchart in FIG.
First, in step ST41, the process is started, and in step ST42, a teacher signal (Y, RY, BY component signal) for one frame or one field is input. Then, in step ST43, a student signal (NTSC signal) is generated from the input teacher signal.
[0148]
Next, in step ST44, it is determined whether the learning process has been completed for all frames or all fields of the teacher signal. If not, the process proceeds to step ST45.
In step ST45, the pixel at the position of interest (pixel of interest) in the teacher signal (Y, RY, BY component signal) is selected. Then, in step ST46, a plurality of pixel data located around the target position are acquired from the student signal (NTSC signal) generated in step ST43.
[0149]
Next, in step ST47, based on the plurality of pixel data obtained in step ST46, a feature amount v related to the pixel data of the target position in the teacher signal is obtained by calculation (see FIGS. 5 and 7). Further, in step ST48, the characteristic amount v obtained in step ST47 is replaced with a representative value in a predetermined range including the characteristic amount v, and the representative value is determined as a class code CL (see FIG. 13).
[0150]
Next, in step ST49, a normal equation (see equation (16)) is generated for each class. Then, in step ST50, it is determined whether or not the learning process has been completed in all regions of the pixel data of the teacher signal input in step ST42. If it has been completed, the process returns to step ST42 to input the teacher signal for the next one frame or one field, and repeats the same processing as described above. On the other hand, if the processing has not been completed, the process returns to step ST45, the next target pixel is selected, and the same processing as described above is repeated.
[0151]
If the learning process has been completed for all frames or all fields of the teacher signal in step ST44, the process proceeds to step ST51. In step ST51, coefficient data Wi is calculated for each class by solving a normal equation by a sweeping-out method or the like.
[0152]
Next, in step ST52, the coefficient seed data w is calculated from the coefficient data Wi of each class calculated in step ST51._i0~ W_i3Is generated (see the equation (21)) for finding. In step ST53, the coefficient seed data w is obtained by solving the normal equation generated in step ST52 by a sweeping method or the like._i0~ W_i3Is calculated, and in step ST54, the coefficient seed data w_i0~ W_i3Is stored in the memory, and then the process ends in step ST55.
[0153]
In this way, by performing the processing according to the flowchart shown in FIG. 18, the coefficient seed data can be obtained by the same method as the coefficient seed data generation device 150A shown in FIG.
[0154]
Note that the image signal processing unit 104 in FIG. 3 uses the generation expression of the expression (2) to generate the coefficient data Wi (i = 1 to n), but uses a polynomial having a different order or another function. It can also be realized by an expressed expression.
[0155]
The image signal processing unit 104 shown in FIG. 3 converts the NTSC signal, which is a composite signal, into a luminance signal Y and color difference signals RY, BY, which 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. Other component signals include three primary color signals of red, green, and blue.
[0156]
Further, the feature amount extracted by the feature amount extraction unit 113 of the image signal processing unit 104 in FIG. 3 is not limited to those shown in FIG. 5 or FIG. In short, it is sufficient that the feature amount is related to the pixel data of the target position in the Y, RY, and BY component signals. The feature amount extraction unit 113 shown in FIG.₂/ L₁Where v = log (L₂/ L₁) May be used. Similarly, the feature amount extraction unit 113 in FIG.₂/ T₁Where v = log (T₂/ T₁) May be used.
[0157]
Although the image signal processing unit 104 of FIG. 3 uses only one type of feature v, an image signal processing unit 104 using two or more types of feature v can be similarly configured. For example, a configuration using two feature values v extracted by the feature value extraction unit 113 in FIGS.
[0158]
In that case, each feature v₁, V₂In this case, a generation expression as shown in Expression (22) can be used as a generation expression for generating the coefficient data Wi.
[0159]
[Equation 14]

[0160]
Also, coefficient seed data w which is coefficient data in this generation formula_i0~ W_i9Can be generated by the coefficient seed data generation device 150 shown in FIG. 12 or the coefficient seed data generation device having the same configuration as the coefficient seed data generation device 150A shown in FIG. In that case, the feature amount v₁More M₁The class of the stage is obtained, and the feature amount v₂More M₂The class of the stage is obtained.₁× M₂Classified into individual. That is, the feature amount v₁, V₂Each representative value is a class code CL₁, CL₂And these class codes CL₁, CL₂Is used to generate a normal equation.
[0161]
Further, in the image signal processing unit 104 in FIG. 3, an example in which a linear linear equation is used as an estimation equation for converting the NTSC signal into the Y, RY, and BY component signals has been described, but the present invention is not limited to this. Instead, for example, a higher-order equation may be used as the estimation equation.
[0162]
Although the image signal processing unit 104 in FIG. 3 converts the NTSC signal into the Y, RY, and BY component signals, the function of the matrix circuit 105 (see FIG. 1) can be included. . In that case, it is possible to convert the NTSC signal into three primary color signals R, G, and B as component signals.
[0163]
Further, in the above embodiment, the information signal is an image signal, but the present invention is not limited to this. For example, the present invention can be similarly applied to a case where the information signal is a voice signal.
[0164]
【The invention's effect】
According to the present invention, when the first information signal is converted into the second information signal, the pixel data of the target position in the second information signal is generated based on the estimation formula. The feature amount related to the information data of the target position in the information signal is extracted, and the coefficient data of the estimation formula corresponding to the feature amount is generated using the coefficient seed data. In addition to the reduction, it is possible to improve the quality of output by the second information signal (output information signal).
[0165]
Further, according to the present invention, a coefficient in a generation expression including a feature amount as a parameter for generating coefficient data used in an estimation expression used when converting the first information signal into the second information signal. Coefficient type data, which is data, can be obtained favorably.
[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 a configuration of pixel data of each field.
FIG. 3 is a block diagram illustrating a configuration of an image signal processing unit.
FIG. 4 is a diagram illustrating a configuration example of a prediction tap.
FIG. 5 is a block diagram illustrating a configuration example of a feature amount extraction unit.
FIG. 6 is a diagram illustrating an example of pixel data for extracting a feature amount.
FIG. 7 is a block diagram illustrating a configuration example of a feature amount extraction unit.
FIG. 8 is a diagram illustrating an example of pixel data for extracting a feature amount.
FIG. 9 is a diagram illustrating an example of a relationship between a feature value v and coefficient data Wi of each tap.
FIG. 10 is a diagram illustrating a configuration example of a prediction tap.
FIG. 11 is a diagram illustrating an example of a relationship between a feature value v and coefficient data Wi of each tap.
FIG. 12 is a block diagram illustrating a configuration example of a coefficient seed data generation device.
FIG. 13 is a diagram illustrating a class code determination process.
FIG. 14 is a block diagram illustrating a configuration example of a coefficient seed data generation device.
FIG. 15 is a block diagram illustrating a configuration example of an image signal processing device realized by software.
FIG. 16 is a flowchart illustrating image signal processing.
FIG. 17 is a flowchart illustrating a coefficient seed data generation process.
FIG. 18 is a flowchart illustrating a coefficient seed data generation process.
[Explanation of symbols]
100 television receiver, 101 receiving antenna, 102 tuner, 103 A / D converter, 104 image signal processing unit, 105 matrix circuit, 106 Display device 111 Input terminal 112 Tap selection unit 113 Feature extraction unit 114 Coefficient generation unit 115 Coefficient seed memory 116 Estimation prediction Arithmetic operation unit, 117 output terminal, 121 bandpass filter, 122, 123, 132 delay circuit, 124, 126, 131 addition circuit, 125, 127 absolute value conversion circuit .., 128, 133... Division circuit, 150, 150A... Coefficient seed data generation device, 151... Input terminal, 152... NTSC encoder, 153. Lass code determination unit, 155: tap selection unit, 156: delay circuit, 157, 161, 163: normal equation generation unit, 158, 164: coefficient type data determination unit, 159: coefficient type Memory, 162... Coefficient data determination unit

Claims (13)

An information signal processing device for converting a first information signal including a plurality of pieces of information data into a second information signal including a plurality of pieces of information data,
First data selecting means for selecting, based on the first information signal, a plurality of pieces of first information data located around a target position in the second information signal;
A feature amount extracting unit that extracts a feature amount associated with the information data of the target position in the second information signal using the plurality of first information data selected by the first data selecting unit;
A storage unit that stores coefficient seed data that is coefficient data of a generation formula for generating coefficient data used in an estimation formula, including a feature amount as a parameter;
A coefficient data generation unit that generates coefficient data used in the estimation expression based on the generation expression using the coefficient seed data stored in the storage unit and the feature amount extracted by the feature amount extraction unit; ,
A second data selection unit that selects a plurality of pieces of second information data located around a target position in the second information signal based on the first information signal;
Using the coefficient data generated by the coefficient data generating means and the plurality of pieces of second information data selected by the second data selecting means, the position of interest in the second information signal based on the estimation formula And an operation means for calculating the pixel data. The first information signal is a composite color video signal,
The feature amount extracting means includes:
2. The information signal processing apparatus according to claim 1, wherein a ratio of a change amount of a color signal of an upper line and a lower color line of the second information signal with respect to a line in which a target position exists is extracted as the feature amount. . The first information signal is composed of a plurality of pixel data obtained by sampling the NTSC signal at the I-axis and Q-axis phases at a frequency four times the color width carrier frequency,
The first data selection means includes:
A first position that is the same position in the vertical and horizontal directions as the position of interest in the second information signal, and a second position that is the same position in the horizontal direction on one line in the vertical direction with respect to the first position. Selecting pixel data at a third position that is the same position in the horizontal direction one line vertically below the position and the first position,
The feature amount extracting means includes:
The ratio between the sum of absolute values of the color signal components of the pixel data at the first and second positions and the sum of the color signal components of the pixel data at the first and third positions is extracted as the feature amount. 3. The information signal processing device according to claim 2, wherein: The first information signal is composed of a plurality of pixel data obtained by sampling the NTSC signal at the I-axis and Q-axis phases at a frequency four times the color carrier frequency,
The feature amount extracting means includes:
2. The information signal processing according to claim 1, wherein a ratio of a vertical change amount of each of the I signal component and the Q signal component in the vicinity of the target position in the second information signal is extracted as the feature amount. apparatus. The first data selection means includes:
A first position that is the same position in the vertical and horizontal directions as the target position in the second information signal, and a second position that is the same position in the horizontal direction on one line in the vertical direction with respect to the first position. A third position, which is the same position in the horizontal direction one line vertically below the position and the first position, and fourth to fourth positions horizontally adjacent to the first to third positions. Select the pixel data at the position of 6,
The feature amount extracting means includes:
The sum of the absolute value of the sum of the color signal components of the pixel data at the first and second positions and the absolute value of the color signal component of the pixel data at the first and third positions; The ratio between the sum of absolute values of the color signal components of the pixel data at the positions and the sum of the absolute values of the color signal components of the pixel data at the fourth and sixth positions is extracted as a feature amount. 5. The information signal processing device according to 4. The first information signal is composed of a plurality of pixel data obtained by sampling the NTSC signal at the I-axis and Q-axis phases at a frequency four times the color carrier frequency,
The feature amount extracting means includes:
The ratio of the change amount of the color signal of the upper line and the lower line to the line where the target position exists in the second information signal, and the change in the vertical direction of each of the I signal component and the Q signal component near the target position. 2. The information signal processing apparatus according to claim 1, wherein a ratio of the amounts is extracted as the feature amount. An information signal processing method for converting a first information signal including a plurality of pieces of information data into a second information signal including a plurality of pieces of information data,
A first step of selecting, based on the first information signal, a plurality of pieces of first information data located around a target position in the second information signal;
A second step of using the plurality of first information data selected in the first step to extract a feature amount related to the information data of the target position in the second information signal;
Using the coefficient seed data, which is the coefficient data of the generation formula for generating the coefficient data used in the estimation formula, including the feature value as a parameter, and the feature value extracted in the second step, the generation formula A third step of generating coefficient data used in the estimation equation based on the third step;
A fourth step of selecting, based on the first information signal, a plurality of pieces of second information data located around a target position in the second information signal;
Using the coefficient data generated in the third step and the plurality of pieces of second information data selected in the fourth step, the pixel at the position of interest in the second information signal based on the estimation formula A fifth step of calculating data to obtain an information signal processing method. In order to convert a first information signal including a plurality of pieces of information data into a second information signal including a plurality of pieces of information data,
A first step of selecting, based on the first information signal, a plurality of pieces of first information data located around a target position in the second information signal;
A second step of using the plurality of first information data selected in the first step to extract a feature amount related to the information data of the target position in the second information signal;
Using the coefficient seed data, which is the coefficient data of the generation formula for generating the coefficient data used in the estimation formula, including the feature value as a parameter, and the feature value extracted in the second step, the generation formula A third step of generating coefficient data used in the estimation equation based on the third step;
A fourth step of selecting, based on the first information signal, a plurality of pieces of second information data located around a target position in the second information signal;
Using the coefficient data generated in the third step and the plurality of pieces of second information data selected in the fourth step, the pixel at the position of interest in the second information signal based on the estimation formula A computer-readable medium storing a program for causing a computer to execute an information signal processing method having the fifth step of calculating data. In order to convert a first information signal including a plurality of pieces of information data into a second information signal including a plurality of pieces of information data,
A first step of selecting, based on the first information signal, a plurality of pieces of first information data located around a target position in the second information signal;
A second step of using the plurality of first information data selected in the first step to extract a feature amount related to the information data of the target position in the second information signal;
Using the coefficient seed data, which is the coefficient data of the generation formula for generating the coefficient data used in the estimation formula, including the feature value as a parameter, and the feature value extracted in the second step, the generation formula A third step of generating coefficient data used in the estimation equation based on the third step;
A fourth step of selecting, based on the first information signal, a plurality of pieces of second information data located around a target position in the second information signal;
Using the coefficient data generated in the third step and the plurality of pieces of second information data selected in the fourth step, the pixel at the position of interest in the second information signal based on the estimation formula A program for causing a computer to execute an information signal processing method having a fifth step of calculating and obtaining data. A feature value is included as a parameter for generating coefficient data used in an estimation formula used when converting a first information signal including a plurality of pieces of information data into a second information signal including a plurality of pieces of information data. An apparatus for generating coefficient seed data that is coefficient data in a generation formula,
First data selecting means for selecting a plurality of first information data located around a target position in a teacher signal corresponding to the second information signal based on a student signal corresponding to the first information signal; When,
A feature amount extracting unit that extracts a feature amount associated with the information data of the target position in the second information signal using the plurality of first information data selected by the first data selecting unit;
Class code determining means for replacing the feature value extracted by the feature value extracting means with a representative value in a predetermined range including the feature value, and determining the representative value as a class code;
A second data selection unit that selects a plurality of pieces of second information data located around a target position in the teacher signal based on the student signal;
The coefficient seed data is obtained by using the class code determined by the class code determining means, the plurality of second information data selected by the second data selecting means, and the information data of the target position in the teacher signal. A coefficient seed data generation device, comprising: an operation unit. A feature value is included as a parameter for generating coefficient data used in an estimation formula used when converting a first information signal including a plurality of pieces of information data into a second information signal including a plurality of pieces of information data. A method for generating coefficient seed data that is coefficient data in a generation formula,
A first step of selecting, based on a student signal corresponding to the first information signal, a plurality of first information data located around a target position in a teacher signal corresponding to the second information signal;
A second step of using the plurality of first information data selected in the first step to extract a feature amount related to the information data of the target position in the second information signal;
A third step of replacing the feature value extracted in the second step with a representative value in a predetermined range including the feature value, and determining the representative value as a class code;
A fourth step of selecting, based on the student signal, a plurality of pieces of second information data located around the position of interest in the teacher signal;
Using the class code determined in the third step, the plurality of pieces of second information data selected in the fourth step, and the information data of the position of interest in the teacher signal, a fifth step of obtaining the coefficient seed data. And a method for generating coefficient seed data. A feature value is included as a parameter for generating coefficient data used in an estimation formula used when converting a first information signal including a plurality of pieces of information data into a second information signal including a plurality of pieces of information data. In order to generate coefficient seed data, which is the coefficient data in the generation formula,
A first step of selecting, based on a student signal corresponding to the first information signal, a plurality of first information data located around a target position in a teacher signal corresponding to the second information signal;
A second step of using the plurality of first information data selected in the first step to extract a feature amount related to the information data of the target position in the second information signal;
A third step of replacing the feature value extracted in the second step with a representative value in a predetermined range including the feature value, and determining the representative value as a class code;
A fourth step of selecting, based on the student signal, a plurality of pieces of second information data located around the position of interest in the teacher signal;
Using the class code determined in the third step, the plurality of pieces of second information data selected in the fourth step, and the information data of the position of interest in the teacher signal, a fifth step of obtaining the coefficient seed data. And a computer-readable medium storing a program for causing a computer to execute the coefficient seed data generation method having the steps of: A feature value is included as a parameter for generating coefficient data used in an estimation formula used when converting a first information signal including a plurality of pieces of information data into a second information signal including a plurality of pieces of information data. In order to generate coefficient seed data, which is the coefficient data in the generation formula,
A first step of selecting, based on a student signal corresponding to the first information signal, a plurality of first information data located around a target position in a teacher signal corresponding to the second information signal;
A second step of using the plurality of first information data selected in the first step to extract a feature amount related to the information data of the target position in the second information signal;
A third step of replacing the feature value extracted in the second step with a representative value in a predetermined range including the feature value, and determining the representative value as a class code;
A fourth step of selecting, based on the student signal, a plurality of pieces of second information data located around the position of interest in the teacher signal;
Using the class code determined in the third step, the plurality of pieces of second information data selected in the fourth step, and the information data of the position of interest in the teacher signal, a fifth step of obtaining the coefficient seed data. For causing a computer to execute a coefficient seed data generation method having the steps of:

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