JP4692801B2 - Coefficient data generating apparatus and generating method, information signal processing apparatus and processing method using the same, and coefficient information generating apparatus and generating method used therefor - Google Patents

Description

【００４５】
そして、動きクラス検出回路１２５では、上述したように算出された平均値ＡＶが１個または複数個のしきい値と比較されて動きクラスのクラス情報ＭＶが得られる。例えば、３個のしきい値ｔｈ１，ｔｈ２，ｔｈ３（ｔｈ１＜ｔｈ２＜ｔｈ３）が用意され、４つの動きクラスを検出する場合、ＡＶ≦ｔｈ１のときはＭＶ＝０、ｔｈ１＜ＡＶ≦ｔｈ２のときはＭＶ＝１、ｔｈ２＜ＡＶ≦ｔｈ３のときはＭＶ＝２、ｔｈ３＜ＡＶのときはＭＶ＝３とされる。
【００４６】
また、画像信号処理部１１０は、空間クラス検出回路１２４より出力される空間クラスのクラス情報としての再量子化コードｑｉと、動きクラス検出回路１２５より出力される動きクラスのクラス情報ＭＶに基づき、作成すべきＨＤ信号（１０５０ｉ信号）の画素（注目画素）が属するクラスを示すクラスコードＣＬを得るためのクラス合成回路１２６を有している。
【００４７】
このクラス合成回路１２６では、（３）式によって、クラスコードＣＬの演算が行われる。なお、（３）式において、Ｎａは空間クラスタップのデータ（ＳＤ画素データ）の個数、ＰはＡＤＲＣにおける再量子化ビット数を示している。
【００４８】
【数２】

【００４９】
また、画像信号処理部１１０は、係数メモリ１３４を有している。この係数メモリ１３４は、後述する推定予測演算回路１２７で使用される推定式の複数の係数データを、クラス毎に、格納するものである。この係数データは、ＳＤ信号（５２５ｉ信号）を、ＨＤ信号（１０５０ｉ信号）に変換するための情報である。係数メモリ１３４には上述したクラス合成回路１２６より出力されるクラスコードＣＬが読み出しアドレス情報として供給され、この係数メモリ１３４からはクラスコードＣＬに対応した推定式の複数の係数データＷｉが読み出され、推定予測演算回路１２７に供給されることとなる。
【００５０】
また、画像信号処理部１１０は、情報メモリバンク１３５を有している。この情報メモリバンク１３５には、各クラスにおける係数情報が予め蓄えられている。この係数情報は、上述した係数メモリ１３４に格納するための係数データを生成するための生成式の係数情報である。
【００５１】
係数情報について詳細に説明する。
後述する推定予測演算回路１２７では、予測タップのデータ（ＳＤ画素データ）ｘｉと、係数メモリ１３４より読み出される複数の係数データＷｉとから、（４）式の推定式によって、作成すべきＨＤ画素データｙが演算される。第１のタップ選択回路１２１で選択される予測タップ数が１０個であるとき、（４）式におけるｎは１０となる。
【００５２】
【数３】

【００５３】
本出願人による先の出願（特願平２０００−３４８７３０号）においては、この推定式の係数データＷｉ（ｉ＝１〜ｎ）を、例えば（５）式に示すように、パラメータｈ，ｖを含む生成式によって生成することを提案した。この生成式の係数データｗ₁₀〜ｗ_n9は、予め学習によって求められ、情報メモリバンクに格納されている。
【００５４】
【数４】

【００５５】
図３は、例えば上述した（５）式によって生成される複数の係数データＷｉの値、すなわち係数値（「●」で図示）を予測タップの中心の係数値を中央として予測タップの中心から離れる方向に各タップ位置に対応して順に並べた場合における、係数値の変化の様子を示している。この係数値の変化は、サイン関数のような変化となる。これは、予測タップの中心から、例えば水平方向、垂直方向、斜め方向など、どの方向に向かって見ても同じ傾向となる。
【００５６】
ここで、パラメータｈ，ｖのうち、パラメータｈを可変にし、パラメータｖをある値に固定した場合、係数値は、図４に示すように、パラメータｖのあらゆる値において、サイン関数のような変化となる。この場合、パラメータｈの値の違いは、サイン関数のゲインの違いとなって現れる。逆に、パラメータｈ，ｖのうち、パラメータｖを可変にし、パラメータｈをある値に固定した場合も、同様の結果が得られる。パラメータｈ，ｖの値を固定した場合に、係数値の係数値の空間的な広がりを全体で見ると、図５に示すようになる。
【００５７】
上述の係数値の変化の様子を考慮し、本実施の形態においては、（６）式を、推定式の複数の係数データＷｉを生成するための生成式とする。（６）式において、ｘ，ｙは、それぞれ水平方向、垂直方向の位置を示している。
【００５８】
【数５】

【００５９】
（６）式において、ｆ(x,y)は補正項である。係数データの近似精度を上げるために設けられている。なお、補正項を設けなくてもよく、補正項を設ける場合には、精度よく補正できる関数ならば、その形は限定されず、水平方向、垂直方向のいずれか、あるいは全ての方向を補正する関数を利用できる。補正項の関数の基本形としては、ｆ(x)＝sinｘⁿ／ｘⁿ、ｆ(x)＝ｅ^-x、ｆ(x)＝１／ｘ^-n、ｆ(x)＝δ(x)などが考えられる。
【００６０】
また、（６）式において、ｋ，ｍは、サイン関数における位相のずれである。この位置で、関数の中心となる最大値が現れる。この最大値にあたる位置をＨＤ信号の出力画素位置と定義すると、位相のずれと出力画素位置の関係は、図６に示すようになる。なお、図６は、水平方向のみに着目している。
【００６１】
中心の予測タップ位置に対して出力画素位置がｋ，ｍだけずれている場合、各予測タップの係数値は、図７に示すように、中心の予測タップ位置を（０，０）として、各タップ位置が中心から水平、垂直の方向にどれくらい離れているかを、（６）式に小数値として代入することで、求めることができる。なお、図７は、水平方向のみに着目している。
【００６２】
ただし、水平、垂直の各方向における隣接タップ位置までの距離は１となることから、０≦ｋ，ｍ≦１である。例えば、ｋがｋ′に変化すると、各予測タップの係数値は「●」から「○」に変化する。
【００６３】
また、（６）式において、ａ，ｂ，ｃは、パラメータｈ，ｖを含む関数から生成される係数データである。本実施の形態において、係数データａ，ｂ，ｃは、それぞれ（７）式によって演算される。情報メモリバンク１３５には、この演算式の係数データである係数種データｗ₁₀〜ｗ₃₅が、上述した係数情報として格納されている。この係数種データｗ₁₀〜ｗ₃₅の生成方法については、後述する。
【００６４】
【数６】

【００６５】
また、画像信号処理部１１０は、各クラスの係数種データｗ₁₀〜ｗ₃₅を用いて、（７）式によって、クラス毎に、係数データａ〜ｃを演算すると共に、各クラスの係数データａ〜ｃおよびシステムコントローラ１０１からのタップ選択信号ＳＴＰに基づいて、（６）式によって、クラス毎に、各予測タップに対応した推定式の係数データＷｉを生成する係数生成回路１３６を有している。この場合、各予測タップに対応した推定式の係数データＷｉは、（６）式のｘ，ｙに、各予測タップ位置の中心から水平、垂直の方向にどれくらい離れているかを代入することで、求められる。上述した係数生成回路１３６で生成される各クラスの係数データＷｉ（ｉ＝１〜ｎ）は、上述した係数メモリ１３４に格納される。
【００６６】
なお、上述したように、５２５ｉ信号を１０５０ｉ信号に変換する場合、奇数、偶数のそれぞれのフィールドにおいて、５２５ｉ信号の１画素に対応して１０５０ｉ信号の４画素を得る必要がある。この場合、奇数、偶数のそれぞれのフィールドにおける１０５０ｉ信号を構成する２×２の単位画素ブロック内の４画素は、それぞれ上述した中心予測タップに対して異なる位相ずれを持っている。
【００６７】
図８は、奇数フィールドにおける１０５０ｉ信号を構成する２×２の単位画素ブロック内の４画素ＨＤ₁〜ＨＤ₄における中心予測タップＳＤ₀からの位相ずれを示している。ここで、ＨＤ₁〜ＨＤ₄の位置は、それぞれ、ＳＤ₀の位置から水平方向にｋ₁〜ｋ₄、垂直方向にｍ₁〜ｍ₄だけずれている。
【００６８】
図９は、偶数フィールドにおける１０５０ｉ信号を構成する２×２の単位画素ブロック内の４画素ＨＤ₁′〜ＨＤ₄′における中心予測タップＳＤ₀′からの位相ずれを示している。ここで、ＨＤ₁′〜ＨＤ₄′の位置は、それぞれ、ＳＤ₀′の位置から水平方向にｋ₁′〜ｋ₄′、垂直方向にｍ₁′〜ｍ₄′だけずれている。
【００６９】
これら各出力画素（ＨＤ₁〜ＨＤ₄，ＨＤ₁′〜ＨＤ₄′）のずれ値の情報ｋ₁〜ｋ₄，ｍ₁〜ｍ₄，ｋ₁′〜ｋ₄′，ｍ₁′〜ｍ₄′も情報メモリバンク１３５に記憶されている。
【００７０】
係数生成回路１３６では、情報メモリバンク１３５に記憶されている各出力画素のずれ値の情報に基づいて、クラスおよび出力画素の組み合わせ毎に、各予測タップに対応した推定式の係数データＷｉが生成される。この場合、ある出力画素に対応した係数データＷｉを求める際には、（６）式のｋ，ｍが当該出力画素の水平、垂直方向へのずれ値とされる。
【００７１】
係数生成回路１３６における各クラスの係数データＷｉの生成は、例えば各垂直ブランキング期間で行われる。これにより、ユーザのリモコン送信機２００の操作によってパラメータｈ，ｖの値が変更されても、係数メモリ１３４に格納される各クラスの係数データＷｉを、そのパラメータｈ，ｖの値に対応したものに即座に変更でき、ユーザによる解像度の調整がスムーズに行われる。
【００７２】
また、画像信号処理部１１０は、係数生成回路１３６で生成される各クラスの係数データＷｉ（ｉ＝１〜ｎ）に対応した正規化係数Ｓを、（８）式によって、演算する正規化係数演算部１３７と、ここで生成された正規化係数Ｓを、クラス毎に格納する正規化係数メモリ１３８とを有している。正規化係数メモリ１３８には上述したクラス合成回路１２６より出力されるクラスコードＣＬが読み出しアドレス情報として供給され、この正規化係数メモリ１３８からはクラスコードＣＬに対応した正規化係数Ｓが読み出され、後述する正規化演算回路１２８に供給されることとなる。
【００７３】
【数７】

【００７４】
また、画像信号処理部１１０は、第１のタップ選択回路１２１で選択的に取り出される予測タップのデータ（ＳＤ画素データ）ｘｉと、係数メモリ１３４より読み出される係数データＷｉとから、（４）式の推定式によって、作成すべきＨＤ信号の画素（注目画素）のデータを演算する推定予測演算回路１２７を有している。
【００７５】
上述したように、ＳＤ信号（５２５ｉ信号）をＨＤ信号（１０５０ｉ信号）に変換する際には、ＳＤ信号の１画素に対してＨＤ信号の４画素（図８のＨＤ₁〜ＨＤ₄、図９のＨＤ₁′〜ＨＤ₄′参照）を得る必要があることから、この推定予測演算回路１２７では、ＨＤ信号を構成する２×２の単位画素ブロック毎に、画素データが生成される。すなわち、この推定予測演算回路１２７には、第１のタップ選択回路１２１より単位画素ブロック内の４画素（注目画素）に対応した予測タップのデータｘｉと、係数メモリ１３４よりその単位画素ブロックを構成する４画素に対応した係数データＷｉとが供給され、単位画素ブロックを構成する４画素のデータｙ₁〜ｙ₄は、それぞれ個別に上述した（４）式の推定式で演算される。
【００７６】
また、画像信号処理部１１０は、推定予測演算回路１２７より順次出力される４画素のデータｙ₁〜ｙ₄を、正規化係数メモリ１３８より読み出される、それぞれの演算に使用された係数データＷｉ（ｉ＝１〜ｎ）に対応した正規化係数Ｓで除算して正規化する正規化演算回路１２８を有している。上述せずも、係数生成回路１３６で係数種データより生成式によって推定式の係数データＷｉを求めるものであるが、生成される係数データは丸め誤差を含み、係数データＷｉ（ｉ＝１〜ｎ）の総和が１．０になることは保証されない。そのため、推定予測演算回路１２７で演算される各画素のデータｙ₁〜ｙ₄は、丸め誤差によってレベル変動したものとなる。上述したように、正規化演算回路１２８で正規化することで、その変動を除去できる。
【００７７】
また、画像信号処理部１１０は、正規化演算回路１２８で正規化されて順次供給される単位画素ブロック内の４画素のデータｙ₁′〜ｙ₄′を線順次化して１０５０ｉ信号のフォーマットで出力する後処理回路１２９を有している。
【００７８】
次に、画像信号処理部１１０の動作を説明する。
バッファメモリ１０９に記憶されているＳＤ信号（５２５ｉ信号）より、第２のタップ選択回路１２２で、作成すべきＨＤ信号（１０５０ｉ信号）を構成する単位画素ブロック内の４画素（注目画素）の周辺に位置する空間クラスタップのデータ（ＳＤ画素データ）が選択的に取り出される。この第２のタップ選択回路１２２で選択的に取り出される空間クラスタップのデータ（ＳＤ画素データ）は空間クラス検出回路１２４に供給される。この空間クラス検出回路１２４では、空間クラスタップのデータとしての各ＳＤ画素データに対してＡＤＲＣ処理が施されて空間クラス（主に空間内の波形表現のためのクラス分類）のクラス情報としての再量子化コードｑｉが得られる（（１）式参照）。
【００７９】
また、バッファメモリ１０９に記憶されているＳＤ信号（５２５ｉ信号）より、第３のタップ選択回路１２３で、作成すべきＨＤ信号（１０５０ｉ信号）を構成する単位画素ブロック内の４画素（注目画素）の周辺に位置する動きクラスタップのデータ（ＳＤ画素データ）が選択的に取り出される。この第３のタップ選択回路１２３で選択的に取り出される動きクラスタップのデータ（ＳＤ画素データ）は動きクラス検出回路１２５に供給される。この動きクラス検出回路１２５では、動きクラスタップのデータとしての各ＳＤ画素データより動きクラス（主に動きの程度を表すためのクラス分類）のクラス情報ＭＶが得られる。
【００８０】
この動き情報ＭＶと上述した再量子化コードｑｉはクラス合成回路１２６に供給される。このクラス合成回路１２６では、これら動き情報ＭＶと再量子化コードｑｉとから、作成すべきＨＤ信号（１０５０ｉ信号）を構成する単位画素ブロック毎にその単位画素ブロック内の４画素（注目画素）が属するクラスを示すクラスコードＣＬが得られる（（３）式参照）。そして、このクラスコードＣＬは、係数メモリ１３４および正規化係数メモリ１３８に読み出しアドレス情報として供給される。
【００８１】
係数メモリ１３４には、例えば各垂直ブランキング期間に、係数生成回路１３６で、ユーザによって調整されたパラメータｈ，ｖの値に対応して、クラスおよび出力画素（ＨＤ₁〜ＨＤ₄，ＨＤ₁′〜ＨＤ₄′）の組み合わせ毎に、推定式の係数データＷｉ（ｉ＝１〜ｎ）が係数生成回路１３６で生成されて格納される。この場合、係数生成回路１３６にシステムコントローラ１０１からのタップ選択信号ＳＴＰが供給され、従って係数生成回路１３６では、タップ選択信号ＳＴＰによって選択された予測タップパターン（図２（ａ）〜（ｉ）参照）に含まれる各予測タップに対応した推定式の係数データＷｉが生成される。また、正規化係数メモリ１３８には、上述したように係数生成回路１３６で生成された推定式の係数データＷｉ（ｉ＝１〜ｎ）に対応した正規化係数Ｓが正規化係数演算部１３７で生成されて格納される。
【００８２】
係数メモリ１３４に上述したようにクラスコードＣＬが読み出しアドレス情報として供給されることで、この係数メモリ１３４からクラスコードＣＬに対応した４出力画素（（奇数フィールドではＨＤ₁〜ＨＤ₄、偶数フィールドではＨＤ₁′〜ＨＤ₄′）分の推定式の係数データＷｉが読み出されて推定予測演算回路１２７に供給される。また、バッファメモリ１０９に記憶されているＳＤ信号（５２５ｉ信号）より、第１のタップ選択回路１２１で、作成すべきＨＤ信号（１０５０ｉ信号）を構成する単位画素ブロック内の４画素（注目画素）の周辺に位置する予測タップのデータ（ＳＤ画素データ）が選択的に取り出される。この場合、第１のタップ選択回路１２１にシステムコントローラ１０１からのタップ選択信号ＳＴＰが供給され、従って第１のタップ選択回路１２１では、タップ選択信号ＳＴＰによって選択された予測タップパターン（図２（ａ）〜（ｉ）参照）に含まれる各予測タップのデータ（ＳＤ画素データ）ｘｉが取り出される。
【００８３】
推定予測演算回路１２７では、予測タップのデータ（ＳＤ画素データ）ｘｉと、係数メモリ１３４より読み出される４出力画素分の係数データＷｉとから、作成すべきＨＤ信号を構成する単位画素ブロック内の４画素（注目画素）のデータｙ₁〜ｙ₄が同時的に演算される（（４）式参照）。そして、この推定予測演算回路１２７より順次出力されるＨＤ信号を構成する単位画素ブロック内の４画素のデータｙ₁〜ｙ₄は正規化演算回路１２８に供給される。
【００８４】
正規化係数メモリ１３８には上述したようにクラスコードＣＬが読み出しアドレス情報として供給され、この正規化係数メモリ１３８からはクラスコードＣＬに対応した正規化係数Ｓ、つまり推定予測演算回路１２７より出力されるＨＤ画素データｙ₁〜ｙ₄の演算に使用された係数データＷｉに対応した正規化係数Ｓが読み出されて正規化演算回路１２８に供給される。この正規化演算回路１２８では、推定予測演算回路１２７より出力されるＨＤ画素データｙ₁〜ｙ₄がそれぞれ対応する正規化係数Ｓで除算されて正規化される。これにより、生成式（（６）式参照）で推定式（（４）式参照）の係数データを求める際の丸め誤差によるデータｙ₁〜ｙ₄のレベル変動が除去される。
【００８５】
このように正規化演算回路１２８で正規化されて順次出力される単位画素ブロック内の４画素のデータｙ₁′〜ｙ₄′は後処理回路１２９に供給される。この後処理回路１２９では、正規化演算回路１２８より順次供給される単位画素ブロック内の４画素のデータｙ₁′〜ｙ₄′が線順次化され、１０５０ｉ信号のフォーマットで出力される。つまり、この後処理回路１２９からは、ＨＤ信号としての１０５０ｉ信号が出力される。
【００８６】
上述したように、図１に示すテレビ受信機１００では、係数生成回路１３６で、情報メモリバンク１３５よりロードされる、係数種データｗ₁₀〜ｗ₃₅および各出力画素（ＨＤ₁〜ＨＤ₄，ＨＤ₁′〜ＨＤ₄′）の中心予測タップに対するずれ値の情報を用いて、クラスおよび出力画素の組み合わせ毎に、パラメータｈ，ｖの値に対応した推定式の係数データＷｉ（ｉ＝１〜ｎ）が生成され、これが係数メモリ１３４に格納される。そして、この係数メモリ１３４より、クラスコードＣＬに対応して読み出される係数データＷｉ（ｉ＝１〜ｎ）を用いて推定予測演算回路１２７でＨＤ画素データｙが演算される。
【００８７】
したがって、ユーザは、パラメータｈ，ｖの値を調整することで、ＨＤ信号によって得られる画像の水平および垂直の画質を、任意に調整することができる。なおこの場合、調整されたパラメータｈ，ｖの値に対応した各クラスの係数データＷｉをその都度係数生成回路１３６で生成して使用するものであり、大量の係数データを格納しておくメモリを必要としない。
【００８８】
また、図１に示すテレビ受信機１００では、（６）式で示される１つの生成式で、各予測タップに対応した推定式の係数データＷｉを生成するものである。したがって、予測タップ数が増加しても、必要とする係数データの増加はなく、情報メモリバンク１３５の容量増加を抑制できる。
【００８９】
また、図１に示すテレビ受信機１００では、ユーザは、リモコン送信機２００の操作によって、複数の予測タップパターンから一の予測タップパターンを選択する等して、予測タップの数および位置を変更できる。これにより、画像信号処理部１１０からのＨＤ信号による画像の画質を任意に変更できる。
【００９０】
次に、図１の画像信号処理部１１０の情報メモリバンク１３５に蓄積される係数種データｗ₁₀〜ｗ₃₅の生成方法について説明する。
図１０は、この生成方法の概念を示している。ＨＤ信号から複数のＳＤ信号を生成する。例えば、ＨＤ信号からＳＤ信号を生成する際に使用するフィルタの水平帯域と垂直帯域を可変するパラメータｈ，ｖをそれぞれ９段階に可変して、合計８１種類のＳＤ信号を生成する。このようにして生成した各ＳＤ信号とＨＤ信号との間で学習を行って、パラメータｈ，ｖの段階的な値毎に、（４）式の推定式の係数データＷｉを生成する。そして、生成された係数データＷｉを使用して、係数種データｗ₁₀〜ｗ₃₅を生成する。
【００９１】
まず、推定式の係数データＷｉの求め方を説明する。
ここでは、（４）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。一般化した例として、Ｘを入力データ、Ｗを係数データ、Ｙを予測値として、（９）式の観測方程式を考える。この（９）式において、ｍは学習データの数を示し、ｎは予測タップの数を示している。
【００９２】
【数８】

【００９３】
（９）式の観測方程式により収集されたデータに最小二乗法を適用する。この（９）式の観測方程式をもとに、（１０）式の残差方程式を考える。
【００９４】
【数９】

【００９５】
（１０）式の残差方程式から、各Ｗiの最確値は、（１１）式のｅ²を最小にする条件が成り立つ場合と考えられる。すなわち、（１２）式の条件を考慮すればよいわけである。
【００９６】
【数１０】

【００９７】
つまり、（１２）式のｉに基づくｎ個の条件を考え、これを満たす、Ｗ₁，Ｗ₂，・・・，Ｗ_nを算出すればよい。そこで、（１０）式の残差方程式から、（１３）式が得られる。さらに、（１３）式と（９）式とから、（１４）式が得られる。
【００９８】
【数１１】

【００９９】
そして、（１０）式と（１４）式とから、（１５）式の正規方程式が得られる。
【０１００】
【数１２】

【０１０１】
（１５）式の正規方程式は、未知数の数ｎと同じ数の方程式を立てることが可能であるので、各Ｗiの最確値を求めることができる。この場合、掃き出し法（Gauss-Jordanの消去法）等を用いて連立方程式を解くことになる。なお、（１５）式の正規方程式は、パラメータｈ，ｖの段階的な値にそれぞれ対応して個別に生成され、それぞれに対応した係数データＷｉが求められる。
【０１０２】
次に、係数種データｗ₁₀〜ｗ₃₅の求め方を説明する。
（６）式に（７）式を代入して、係数種データｗ₁₀〜ｗ₃₅と位相のずれｋ，ｍで表される基本関数を生成する。そして、この基本関数のｋ，ｍに予め分かっている位相のずれを代入し、残りの係数種データｗ₁₀〜ｗ₃₅を非線形最小二乗近似により求める。
【０１０３】
なお、非線形最小二乗法が制約条件を持たない場合には値が発散してしまう可能性があるので、ａ〜ｃの係数値が取りうる範囲を限定し、制約条件付きの非線形最小二乗法であるＳＱＲ法(Successive quadratic programming method)により近似をする。
【０１０４】
図１１は、図１０に示す概念に基づいて係数種データｗ₁₀〜ｗ₃₅を生成する係数種データ生成装置１５０の構成を示している。
この係数種データ生成装置１５０は、教師信号としてのＨＤ信号（１０５０ｉ信号）が入力される入力端子１５１と、このＨＤ信号に対して水平および垂直の間引き処理を行って、生徒信号としてのＳＤ信号を得るＳＤ信号生成回路１５２とを有している。
【０１０５】
また、ＳＤ信号生成回路１５２には、パラメータｈ，ｖが制御信号として供給される。このパラメータｈ，ｖに対応して、ＨＤ信号からＳＤ信号を生成する際に使用するフィルタの水平帯域と垂直帯域とが可変される。
【０１０６】
このフィルタは、例えば水平帯域を制限する１次元ガウシアンフィルタと垂直帯域を生成する１次元ガウシアンフィルタとから構成される。この１次元ガウシアンフィルタは、（１６）式で示される。この場合、ｈまたはｖの段階的な値に対応して標準偏差σの値を段階的に変えることにより、ｈまたはｖの段階的な値に対応した周波数特性を持つ１次元ガウシアンフィルタを得ることができる。
【０１０７】
【数１３】

【０１０８】
また、係数種データ生成装置１５０は、ＳＤ信号生成回路１５２より出力されるＳＤ信号（５２５ｉ信号）より、ＨＤ信号（１０５０ｉ信号）に係る注目画素の周辺に位置する複数のＳＤ画素のデータを選択的に取り出して出力する第１〜第３のタップ選択回路１５３〜１５５を有している。これら第１〜第３のタップ選択回路１５３〜１５５は、上述した画像信号処理部１１０の第１〜第３のタップ選択回路１２１〜１２３と同様に構成される。
【０１０９】
また、係数種データ生成装置１５０は、第２のタップ選択回路１５４で選択的に取り出される空間クラスタップのデータ（ＳＤ画素データ）のレベル分布パターンを検出し、このレベル分布パターンに基づいて空間クラスを検出し、そのクラス情報を出力する空間クラス検出回路１５７を有している。この空間クラス検出回路１５７は、上述した画像信号処理部１１０の空間クラス検出回路１２４と同様に構成される。この空間クラス検出回路１５７からは、空間クラスタップのデータとしての各ＳＤ画素データ毎の再量子化コードｑｉが空間クラスを示すクラス情報として出力される。
【０１１０】
また、係数種データ生成装置１５０は、第３のタップ選択回路１５５で選択的に取り出される動きクラスタップのデータ（ＳＤ画素データ）より、主に動きの程度を表すための動きクラスを検出し、そのクラス情報ＭＶを出力する動きクラス検出回路１５８を有している。この動きクラス検出回路１５８は、上述した画像信号処理部１１０の動きクラス検出回路１２５と同様に構成される。この動きクラス検出回路１５８では、第３のタップ選択回路１５５で選択的に取り出される動きクラスタップのデータ（ＳＤ画素データ）からフレーム間差分が算出され、さらにその差分の絶対値の平均値に対してしきい値処理が行われて動きの指標である動きクラスが検出される。
【０１１１】
また、係数種データ生成装置１５０は、空間クラス検出回路１５７より出力される空間クラスのクラス情報としての再量子化コードｑｉと、動きクラス検出回路１５８より出力される動きクラスのクラス情報ＭＶに基づき、ＨＤ信号（１０５０ｉ信号）に係る注目画素が属するクラスを示すクラスコードＣＬを得るためのクラス合成回路１５９を有している。このクラス合成回路１５９も、上述した画像信号処理部１１０のクラス合成回路１２６と同様に構成される。
【０１１２】
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ＨＤ信号より得られる注目画素データとしての各ＨＤ画素データｙと、この各ＨＤ画素データｙにそれぞれ対応して第１のタップ選択回路１５３で選択的に取り出される予測タップのデータ（ＳＤ画素データ）ｘｉと、各ＨＤ画素データｙにそれぞれ対応してクラス合成回路１５９より出力されるクラスコードＣＬとから、クラス毎に、係数データＷｉ（ｉ＝１〜ｎ）を得るための正規方程式（（１５）式参照）を生成する正規方程式生成部１６０を有している。
【０１１３】
この場合、１個のＨＤ画素データｙとｎ個の予測タップのデータ（ＳＤ画素データ）ｘｉとの組み合わせで学習データが生成されるが、正規方程式生成部１６０では、出力画素（図８のＨＤ₁〜ＨＤ₄、図９のＨＤ₁′〜ＨＤ₄′参照）毎に、個別に正規方程式が生成される。例えば、ＨＤ₁に対応した正規方程式は、中心予測タップに対するずれ値がＨＤ₁と同じ関係にあるＨＤ画素のデータｙから構成される学習データから生成される。
【０１１４】
さらに、ＳＤ信号生成回路１５２へのパラメータｈ，ｖが順次変更されていき、正規方程式生成部１６０では、パラメータｈ，ｖの段階的な値にそれぞれ対応して、正規方程式が生成される。これにより、正規方程式生成部１６０では、パラメータｈ，ｖの段階的な値にそれぞれ対応して、クラスおよび出力画素の組み合わせ毎に、係数データＷｉを得るための正規方程式が生成される。
【０１１５】
また、係数種データ生成装置１５０は、正規方程式生成部１６０で生成された各正規方程式のデータが供給され、各正規方程式を解いて、パラメータｈ，ｖの段階的な値にそれぞれ対応して、クラスおよび出力画素の組み合わせ毎に、係数データＷｉを求める係数データ決定部１６１と、この求められた係数データＷｉを記憶する係数メモリ１６２とを有している。係数データ決定部１６１では、正規方程式が例えば掃き出し法などによって解かれて、係数データＷｉが求められる。
【０１１６】
また、係数種データ生成装置１５０は、係数種データｗ₁₀〜ｗ₃₅を求めるための基本関数を生成する基本関数生成部１６３を有している。この基本関数生成部１６３においては、上述の（６）式に上述の（７）式が代入されて、基本関数が生成される。
【０１１７】
また、係数種データ生成装置１５０は、基本関数生成部１６３で生成された基本関数を使用し、クラス毎にパラメータｈ，ｖの段階的な値にそれぞれ対応して求められた係数データＷｉを用いて、非線形最小二乗近似により各クラスの係数種データｗ₁₀〜ｗ₃₅を求める係数種データ生成部１６４と、この係数種データ生成部１６４で生成されたクラス毎の係数種データｗ₁₀〜ｗ₃₅を記憶する係数種メモリ１６５を有している。
【０１１８】
図１１に示す係数データ生成装置１５０の動作を説明する。
入力端子１５１には教師信号としてのＨＤ信号（１０５０ｉ信号）が供給され、そしてこのＨＤ信号に対してＳＤ信号生成回路１５２で水平および垂直の間引き処理が行われて生徒信号としてのＳＤ信号（５２５ｉ信号）が生成される。この場合、ＳＤ信号生成回路１５２には、パラメータｈ，ｖが制御信号として供給され、水平および垂直の帯域が段階的に変化した複数のＳＤ信号が順次生成されていく。
【０１１９】
このＳＤ信号（５２５ｉ信号）より、第２のタップ選択回路１５４で、ＨＤ信号（１０５０ｉ信号）に係る注目画素の周辺に位置する空間クラスタップのデータ（ＳＤ画素データ）が選択的に取り出される。この第２のタップ選択回路１５４で選択的に取り出される空間クラスタップのデータ（ＳＤ画素データ）は空間クラス検出回路１５７に供給される。この空間クラス検出回路１５７では、空間クラスタップのデータとしての各ＳＤ画素データに対してＡＤＲＣ処理が施されて空間クラス（主に空間内の波形表現のためのクラス分類）のクラス情報としての再量子化コードｑｉが得られる（（１）式参照）。
【０１２０】
また、ＳＤ信号生成回路１５２で生成されたＳＤ信号より、第３のタップ選択回路１５５で、ＨＤ信号に係る注目画素の周辺に位置する動きクラスタップのデータ（ＳＤ画素データ）が選択的に取り出される。この第３のタップ選択回路１５５で選択的に取り出される動きクラスタップのデータ（ＳＤ画素データ）は動きクラス検出回路１５８に供給される。この動きクラス検出回路１５８では、動きクラスタップのデータとしての各ＳＤ画素データより動きクラス（主に動きの程度を表すためのクラス分類）のクラス情報ＭＶが得られる。
【０１２１】
このクラス情報ＭＶと上述した再量子化コードｑｉはクラス合成回路１５９に供給される。このクラス合成回路１５９では、これらクラス情報ＭＶと再量子化コードｑｉとから、ＨＤ信号（１０５０ｉ信号）に係る注目画素が属するクラスを示すクラスコードＣＬが得られる（（３）式参照）。
【０１２２】
また、ＳＤ信号生成回路１５２で生成されるＳＤ信号より、第１のタップ選択回路１５３で、ＨＤ信号に係る注目画素の周辺に位置する予測タップのデータ（ＳＤ画素データ）が選択的に取り出される。そして、入力端子１５１に供給されるＨＤ信号より得られる注目画素データとしての各ＨＤ画素データｙと、この各ＨＤ画素データｙにそれぞれ対応して第１のタップ選択回路１５３で選択的に取り出される予測タップのデータ（ＳＤ画素データ）ｘｉと、各ＨＤ画素データｙにそれぞれ対応してクラス合成回路１５９より出力されるクラスコードＣＬとから、正規方程式生成部１６０では、パラメータｈ，ｖの段階的な値にそれぞれ対応して、クラスおよび出力画素の組み合わせ毎に、係数データＷｉを得るための正規方程式が個別に生成される。
【０１２３】
そして、係数データ決定部１６１で各正規方程式が解かれ、パラメータｈ，ｖの段階的な値にそれぞれ対応した、クラスおよび出力画素の組み合わせ毎の係数データＷｉが求められ、それらの係数データＷｉは係数メモリ１６２に記憶される。
【０１２４】
また、基本関数生成部１６３で、係数種データｗ₁₀〜ｗ₃₅を求めるための基本関数が生成される。この基本関数のｋ，ｍには、出力画素の中心予測タップからのずれ値が代入される。そして、係数種データ生成部１６４で、上述の基本関数を使用し、クラス毎にパラメータｈ，ｖの段階的な値にそれぞれ対応して求められれた係数データＷｉを用いて、非線形最小二乗近似により、クラス毎の係数種データｗ₁₀〜ｗ₃₅が求められ、それらの係数種データｗ₁₀〜ｗ₃₅は係数種メモリ１６５に記憶される。
【０１２５】
なおこの場合、（６）式における補正項ｆ(x,y)の関数形が順次変更され、最も二乗誤差の小さくなる補正項を加えた関数が最終的な基本関数として使用され、その最終的な基本関数を使用して生成された係数種データｗ₁₀〜ｗ₃₅が係数種メモリ１６５に記憶される。この場合、図１の画像信号処理部１１０における係数生成回路１３６でも、この最終的な基本関数が用いられることとなる。
【０１２６】
このように、図１１に示す係数種データ生成装置１５０においては、図１の画像信号処理部１１０の情報メモリバンク１３５に記憶される、各クラスの係数種データｗ₁₀〜ｗ₃₅を生成することができる。
【０１２７】
図１１に示す係数種データ生成装置１５０では、教師信号（１０５０ｉ信号）から生徒信号（５２５ｉ信号）を生成する例を用いて、学習（係数種データ生成）を行うものであった。しかし、教師信号と生徒信号を取得できる撮像装置を利用するなどして、独立して得られた教師信号、生徒信号を用いて学習を行ってもよい。
【０１２８】
なお、図１の画像信号処理部１１０における処理を、例えば図１２に示すような画像信号処理装置３００によって、ソフトウェアで実現することも可能である。
まず、図１２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。
【０１２９】
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（Ｒ）ディスク３０６をドライブするフロッピー（Ｒ）ディスクドライブ（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。
【０１３０】
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。
【０１３１】
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。
【０１３２】
また、画像信号処理装置３００は、ＳＤ信号を入力するための入力端子３１４と、ＨＤ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。
【０１３３】
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（Ｒ）ディスク３０６で提供するようにしてもよい。
【０１３４】
また、処理すべきＳＤ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ＨＤ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。
【０１３５】
図１３のフローチャートを参照して、図１２に示す画像信号処理装置３００における、ＳＤ信号よりＨＤ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、ＳＤ画素データをフレーム単位またはフィールド単位で入力する。このＳＤ画素データが入力端子３１４より入力される場合には、このＳＤ画素データをＲＡＭ３０３に一時的に格納する。また、このＳＤ画素データがハードディスクに記録されている場合には、ハードディスクドライブ３０７でこのＳＤ画素データを読み出し、ＲＡＭ３０３に一時的に格納する。そして、ステップＳＴ３で、入力ＳＤ画素データ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。
【０１３６】
このステップＳＴ５では、ユーザがリモコン送信機２００を操作して入力した画質指定値、つまりパラメータｈ，ｖの値を例えばＲＡＭ３０３より読み込む。そして、ステップＳＴ６で、例えばＲＯＭ３０２から各クラスの係数種データｗ₁₀〜ｗ₃₅と、出力画素（ＨＤ₁〜ＨＤ₄，ＨＤ₁′〜ＨＤ₄′）のずれ値の情報を読み込む。
【０１３７】
次に、ステップＳＴ７で、ユーザがリモコン送信機２００を操作して入力した予測タップパターンに対応した各予測タップの位置（予測タップ座標）を例えばＲＯＭ３０２内のテーブルより取得する。そして、ステップＳＴ８で、ステップＳＴ５で読み込んだパラメータｈ，ｖおよびステップＳＴ６で取得された係数種データｗ₁₀〜ｗ₃₅を（７）式に代入して（６）式のａ〜ｃの係数データを求め、その後に（６）式の基本関数のｋ，ｍにステップＳＴ６で取得された各出力画素のずれ値を代入し、クラスおよび出力画素の組み合わせ毎に、各予測タップに対応した係数データＷｉを生成する。
【０１３８】
次に、ステップＳＴ９で、ステップＳＴ２で入力されたＳＤ画素データより、生成すべき各ＨＤ画素データに対応して、クラスタップおよび予測タップの画素データを取得する。そして、ステップＳＴ１０で、入力されたＳＤ画素データの全領域においてＨＤ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、次のフレームまたはフィールドのＳＤ画素データの入力処理に移る。一方、処理が終了していないときは、ステップＳＴ１１に進む。
【０１３９】
このステップＳＴ１１では、ステップＳＴ９で取得されたクラスタップのＳＤ画素データからクラスコードＣＬを生成する。そして、ステップＳＴ１２で、そのクラスコードＣＬに対応した係数データと予測タップのＳＤ画素データを使用して、推定式により、ＨＤ画素データを生成し、その後にステップＳＴ９に戻って、上述したと同様の処理を繰り返す。
【０１４０】
このように、図１３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ＳＤ信号を構成するＳＤ画素データを処理して、ＨＤ信号を構成するＨＤ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ＨＤ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。
また、処理装置の図示は省略するが、図１１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１４のフローチャートを参照して、係数種データを生成するための処理手順を説明する。
まず、ステップＳＴ４１で、処理を開始し、ステップＳＴ４２で、学習に使われる、画質パターン（例えば、パラメータｈ，ｖで特定される）を選択する。そして、ステップＳＴ４３で、全ての画質パターンに対する係数データの算出処理が終了したか否かを判定する。終了していないときは、ステップＳＴ４４に進む。
【０１４２】
このステップＳＴ４４では、既知のＨＤ画素データをフレーム単位またはフィールド単位で入力する。そして、ステップＳＴ４５で、全てのＨＤ画素データについて処理が終了したか否かを判定する。終了していないときは、ステップＳＴ４６で、ステップＳＴ４４で入力されたＨＤ画素データより、ステップＳＴ４２で選択された画質パターンに基づいて、ＳＤ画素データを生成する。
【０１４３】
そして、ステップＳＴ４７で、ステップＳＴ４６で生成されたＳＤ画素データより、ステップＳＴ４４で入力された各ＨＤ画素データに対応して、クラスタップおよび予測タップの画素データを取得する。そして、ステップＳＴ４８で、生成されたＳＤ画素データの全領域において学習処理を終了しているか否かを判定する。学習処理を終了しているときは、ステップＳＴ４４に戻って、次のＨＤ画素データの入力を行って、上述したと同様の処理を繰り返し、一方、学習処理を終了していないときは、ステップＳＴ４９に進む。
【０１４４】
このステップＳＴ４９では、ステップＳＴ４７で取得されたクラスタップのＳＤ画素データからクラスコードＣＬを生成する。そして、ステップＳＴ５０で、係数データを得るための正規方程式（（１５）式参照）を生成する。ここでは、クラスおよび出力画素の組み合わせ毎に、係数データＷｉを得るための正規方程式が個別に生成される。その後に、ステップＳＴ４７に戻る。
【０１４５】
上述したステップＳＴ４５で、全てのＨＤ画素データについて処理が終了したときは、ステップＳＴ５１で、ステップＳＴ５０で生成された各正規方程式を掃き出し法などで解いて、クラスおよび出力画素の組み合わせ毎に、係数データＷｉを算出する。その後に、ステップＳＴ４２に戻って、次の画質パターンを選択して、上述したと同様の処理を繰り返し、次の画質パターンに対応した、クラスおよび出力画素の組み合わせ毎の係数データＷｉを求める。
【０１４６】
また、上述のステップＳＴ４３で、全ての画質パターンに対する係数データＷｉの算出処理が終了したときは、ステップＳＴ５２に進む。このステップＳＴ５２では、上述の（６）式に上述の（７）式が代入され、係数種データｗ₁₀〜ｗ₃₅を求めるための基本関数が生成される。
【０１４７】
そして、ステップＳＴ５３で、ステップＳＴ５２で生成された基本関数を使用し、パラメータｈ，ｖの段階的な値にそれぞれ対応して求められれたクラスおよび出力画素の組み合わせ毎の係数データＷｉを用いて、非線形最小二乗近似により各クラスの係数種データｗ₁₀〜ｗ₃₅を求め、ステップＳＴ５４で、その各クラスの係数種データｗ₁₀〜ｗ₃₅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。
【０１４８】
このように、図１４に示すフローチャートに沿って処理をすることで、図１１に示す係数種データ生成装置１５０と同様の手法によって、各クラスの係数種データｗ₁₀〜ｗ₃₅を得ることができる。
【０１４９】
なお、上述実施の形態においては、ユーザは、リモコン送信機２００の操作によって、予測タップの数および位置を変更できると共に、水平、垂直の解像度を調整できるものを示したが、どちらか一方のみを可能とするものも、同様に構成できる。
【０１５０】
また、上述実施の形態においては、情報メモリバンク１３５に記憶される係数情報は、生成式（（６）式参照）の係数データａ〜ｃを演算するための演算式（７）式参照）の係数データである係数種データｗ₁₀〜ｗ₃₅であるが、水平、垂直の解像度を変更しないものである場合には、上述した生成式（（６）式参照）の係数データａ〜ｃそのものを係数情報として記憶しておいてもよい。
【０１５１】
また、上述実施の形態においては、情報メモリバンク１３５には、各クラスの係数種データｗ₁₀〜ｗ₃₅の他に、出力画素（ＨＤ₁〜ＨＤ₄、ＨＤ₁′〜ＨＤ₄′）のずれ値の情報が記憶される。そのずれ値の情報は、ＳＤ信号（５２５ｉ信号）とＨＤ信号（１０５０ｉ信号）の関係で決まるものであるが、ＳＤ信号（５２５ｉ信号）と他のＨＤ信号との関係できまるずれ値の情報を記憶しておくことで、他のＨＤ信号の各出力画素に対応した係数データＷｉを同様に生成でき、他のＨＤ信号を生成することができる。この場合、ずれ値の情報を情報メモリバンク１３５に記憶しておくのではなく、生成すべきＨＤ信号の種類に応じたずれ値の情報を、その都度システムコントローラ１０１から係数生成回路１３６に供給するようにしてもよい。これにより、ディスプレイ部１１１に使用される表示素子の特性に合った解像度（画素数）のＨＤ信号を容易に得ることができる。
【０１５２】
また、上述実施の形態においては、ＨＤ信号を生成する際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するものであってもよい。
また、上述実地の形態においては、情報信号が画像信号である場合を示したが、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。
【０１５３】
【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使用される推定式の複数の係数データを、第２の情報信号に係る注目点の周辺に位置する第１の情報信号の複数の情報データの位置を指定するためのパラメータを含む生成式を使用して生成するものであり、予測タップ数が増加しても、係数データを格納しておくメモリの容量増加を抑えることができ、メモリ容量の節約を図ることができる。
【図面の簡単な説明】
【図１】実施の形態としてのテレビ受信機の構成を示すブロック図である。
【図２】予測タップパターン例を示す図である。
【図３】水平、垂直、斜め等の同方向における係数値の変化の様子を示す図である。
【図４】垂直方の解像度を示すパラメータｖを固定とし、水平方向の解像度を示すパラメータｈを可変にした場合の各予測タップ位置における係数値の変化の様子を示す図である。
【図５】パラメータｈ，ｖの値を固定にした場合の係数値の空間的な広がりを示す図である。
【図６】水平方向における位相のずれと出力画素位置の関係を示す図である。
【図７】位相のずれと各予測タップの係数値の関係を示す図である。
【図８】ＨＤ信号（１０５０ｉ信号）の単位画素ブロック内の４画素の中心予測タップからの位相ずれ（奇数フィールド）を示す図である。
【図９】ＨＤ信号（１０５０ｉ信号）の単位画素ブロック内の４画素の中心予測タップからの位相ずれ（偶数フィールド）を示す図である。
【図１０】係数種データの生成方法の一例を示す図である。
【図１１】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１２】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１３】画像信号の処理手順を示すフローチャートである。
【図１４】係数種データ生成処理を示すフローチャートである。
【図１５】５２５ｉ信号と１０５０ｉ信号の画素位置関係を示す図である。
【符号の説明】
１００・・・テレビ受信機、１０１・・・システムコントローラ、１０２・・・リモコン信号受信回路、１０５・・・受信アンテナ、１０６・・・チューナ、１１０・・・画像信号処理部、１１１・・・ディスプレイ部、１１２・・・ＯＳＤ回路、１２１・・・第１のタップ選択回路、１２２・・・第２のタップ選択回路、１２３・・・第３のタップ選択回路、１２４・・・空間クラス検出回路、１２５・・・動きクラス検出回路、１２６・・・クラス合成回路、１２７・・・推定予測演算回路、１２８・・・正規化演算回路、１２９・・・後処理回路、１３４・・・係数メモリ、１３５・・・情報メモリバンク、１３６・・・係数生成回路、１３７・・・正規化係数演算部、１３８・・・正規化係数メモリ、１５０・・・係数種データ生成装置、１５１・・・入力端子、１５２・・・ＳＤ信号生成回路、１５３・・・第１のタップ選択回路、１５４・・・第２のタップ選択回路、１５５・・・第３のタップ選択回路、１５７・・・空間クラス検出回路、１５８・・・動きクラス検出回路、１５９・・・クラス合成回路、１６０・・・正規方程式生成部、１６１・・・係数データ決定部、１６２・・・係数メモリ、１６３・・・基本関数生成部、１６４・・・係数種データ生成部、１６５・・・係数種メモリ、２００・・・リモコン送信機、３００・・・画像信号処理装置[0001]
BACKGROUND OF THE INVENTION
  The present invention is, for example, a coefficient data generation apparatus and generation method suitable for application when converting an NTSC video signal to a high-definition video signal, and an information signal processing apparatus and processing method using the coefficient data generation apparatus.AndCoefficient information generation device and generation method used thereforLawAbout. Specifically, the plurality of coefficient data of the estimation formula used when converting the first information signal into the second information signal is the first information signal located around the point of interest related to the second information signal. By generating using a generation formula including a parameter for specifying the position of a plurality of information data, it is possible to suppress an increase in the capacity of the memory for storing coefficient data even if the number of predicted taps increases The present invention relates to a coefficient data generation device and the like.
[0002]
[Prior art]
Conventionally, for example, a format conversion for converting an SD (Standard Definition) signal called a 525i signal into an HD (High Definition) signal called a 1050i signal has been proposed. The 525i signal means an interlaced image signal having 525 lines, and the 1050i signal means an interlaced image signal having 1050 lines.
[0003]
FIG. 15 shows the pixel position relationship between the 525i signal and the 1050i signal. Here, a large dot is a pixel of a 525i signal, and a small dot is a pixel of a 1050i signal. In addition, pixel positions in odd fields are indicated by solid lines, and pixel positions in even fields are indicated by broken lines. When converting a 525i signal to a 1050i signal, it is necessary to obtain four pixels of the 1050i signal corresponding to one pixel of the 525i signal in each of the odd and even fields.
[0004]
Conventionally, when the pixel data of the 1050i signal is obtained from the pixel data of the 525i signal in order to perform the format conversion as described above, the coefficient data of the estimation formula corresponding to the phase of each pixel of the 1050i signal with respect to the pixel of the 525i signal is obtained. It has been proposed that pixel data of a 1050i signal is obtained by an estimation formula using the coefficient data stored in a memory.
[0005]
[Problems to be solved by the invention]
As described above, in the case of obtaining the pixel data of the 1050i signal by the estimation formula, the image quality is improved as the number of pixel data of the 525i signal used for obtaining the pixel data of the 1050i signal, that is, the number of predicted taps is increased. However, if the number of predicted taps is increased, the necessary coefficient data also increases, and the capacity of the memory for storing the coefficient data increases, leading to an increase in the cost of the apparatus.
Therefore, an object of the present invention is to provide a coefficient data generation device or the like that can suppress an increase in the capacity of a memory for storing coefficient data even when the number of predicted taps increases, and can save the memory capacity.
[0006]
[Means for Solving the Problems]
A coefficient data generation device according to the present invention is a coefficient for generating coefficient data of an estimation formula used when converting a first information signal composed of a plurality of information data into a second information signal composed of a plurality of information data A storage for storing coefficient information of a generation equation including a first parameter indicating a position of a plurality of pieces of information data of a first information signal located around a point of interest related to a second information signal, the data generating device And a generation formula constructed based on the coefficient information stored in the storage means, and based on the value of the first parameter indicating the position of the plurality of information data of the first information signal, Coefficient data generating means for generating a plurality of coefficient data of an estimation formula corresponding to each of a plurality of information data of one information signal.
[0007]
Also, the coefficient data generation method according to the present invention generates coefficient data of an estimation formula used when converting a first information signal composed of a plurality of information data into a second information signal composed of a plurality of information data. A coefficient data generation method including a first parameter indicating positions of a plurality of pieces of information data of the first information signal located around the point of interest related to the second information signal, A step of configuring the generation formula based on coefficient information of the generation formula for generating, and using the generation formula, based on the value of the first parameter indicating the positions of the plurality of information data of the first information signal And generating a plurality of coefficient data of estimation equations respectively corresponding to the plurality of information data of the first information signal.
[0008]
  thisIn the invention, the generation formula for generating coefficient data of the estimation formula, including the first parameter indicating the position of a plurality of pieces of information data of one information signal located around the point of interest related to the second information signal Is stored in the storage means.
[0009]
A generation formula is constructed based on the coefficient information. And this generation formula is used, and the value of the first parameter indicating the position of the plurality of information data of the first information signal is given, so that it corresponds to the plurality of information data of the first information signal, respectively. A plurality of coefficient data of the estimation formula is generated.
[0010]
In this way, a plurality of coefficient data of the estimation formula is generated by one generation formula. Even if the number of prediction taps increases, the required coefficient data does not increase, and the coefficient data is stored. Increase in memory capacity can be suppressed. Here, the user can arbitrarily change the number of predicted taps and the tap position by further including first parameter setting means for setting the value of the first parameter.
[0011]
In addition, a second parameter setting unit that sets a value of a second parameter that determines the quality of the output obtained by the second information signal is provided, and the generation formula including the first parameter further includes the second parameter. In this case, this generation formula is used to generate a plurality of coefficient data of the estimation formula corresponding to the set value of the second parameter.
[0012]
Thereby, the user can arbitrarily change the quality of the output obtained by the second information signal by changing the value of the second parameter. In this case, even if there is a change in the value of the second parameter, since a plurality of coefficient data of the estimation expression is generated with one generation expression, there is no increase in the required coefficient data, and the coefficient data is stored. It is possible to suppress an increase in memory capacity.
[0013]
And a third parameter setting means for setting a value of a third parameter indicating the phase of the point of interest related to the second information signal with reference to the position of the information data of the first information signal. When the generation formula including one parameter further includes the third parameter, the generation formula is used to generate coefficient data of the estimation formula corresponding to the set value of the third parameter.
[0014]
Thereby, the user can arbitrarily change the phase of the point of interest related to the second information signal by changing the value of the third parameter. For example, when the information signal is an image signal, the point of interest is the output pixel position, and an output image signal having an arbitrary density can be obtained with respect to the input image signal. In this case, even if there is a change in the value of the third parameter, since a plurality of coefficient data of the estimation formula is generated with one generation formula, there is no increase in the required coefficient data, and the coefficient data is stored. It is possible to suppress an increase in memory capacity.
[0015]
An information signal processing apparatus according to the present invention is an information signal processing apparatus that converts a first information signal made up of a plurality of information data into a second information signal made up of a plurality of information data. First data selection means for selecting a plurality of first information data located around the point of interest relating to the second information signal, and a plurality of first information data selected by the first data selection means A plurality of first information data based on the value of the first parameter indicating the positions of the plurality of information data of the first information signal, using a generation formula including a first parameter for designating the position of the first information signal Coefficient data generating means for generating a plurality of coefficient data of estimation formulas respectively corresponding to the above, a plurality of first information data selected by the first data selecting means, and a plurality of coefficients generated by the coefficient data generating means From the data, Using serial estimation formula, in which and a computing means capable calculates the information data of the target point.
[0016]
  An information signal processing method according to the present invention is an information signal processing method for converting a first information signal composed of a plurality of information data into a second information signal composed of a plurality of information data, wherein the first information signal A first step of selecting a plurality of pieces of information data located around a point of interest related to the second information signal from the signal, and a parameter for designating positions of the plurality of pieces of information data selected in the first step And generating a plurality of coefficient data of estimation formulas corresponding to the plurality of information data based on the values of the parameters indicating the positions of the plurality of information data of the first information signal, respectively. The second information obtained by calculating the information data of the attention point from the plurality of information data selected in the second step, the plurality of information data selected in the first step, and the plurality of coefficient data generated in the second step using an estimation formula. Those comprising of the stepsis there.
[0017]
In the present invention, a plurality of pieces of first information data located around the point of interest related to the second information signal are selected from the first information signal. Further, a generation formula including a first parameter indicating the position of the plurality of first information data is used, and based on the value of the first parameter indicating the position of the plurality of information data of the first information signal, A plurality of coefficient data of the estimation formula respectively corresponding to the plurality of first information data is generated. Then, the information data of the attention point is calculated from the plurality of first information data and the plurality of coefficient data respectively corresponding to the plurality of first information data using the estimation formula.
[0018]
In this way, a plurality of coefficient data of the estimation formula is generated by one generation formula. Even if the number of prediction taps increases, the required coefficient data does not increase, and the coefficient data is stored. Increase in memory capacity can be suppressed. Here, the user can arbitrarily change the number of predicted taps and the tap position by further including first parameter setting means for setting the value of the first parameter.
[0019]
In addition, a second parameter setting unit that sets a value of a second parameter that determines the quality of the output obtained by the second information signal is provided, and the generation formula including the first parameter further includes the second parameter. In this case, this generation formula is used to generate a plurality of coefficient data of the estimation formula corresponding to the set value of the second parameter.
[0020]
Thereby, the user can arbitrarily change the quality of the output obtained by the second information signal by changing the value of the second parameter. In this case, even if there is a change in the value of the second parameter, since a plurality of coefficient data of the estimation expression is generated with one generation expression, there is no increase in the required coefficient data, and the coefficient data is stored. It is possible to suppress an increase in memory capacity.
[0021]
And a third parameter setting means for setting a value of a third parameter indicating the phase of the point of interest related to the second information signal with reference to the position of the information data of the first information signal. When the generation formula including one parameter further includes the third parameter, the generation formula is used to generate coefficient data of the estimation formula corresponding to the set value of the third parameter.
[0022]
Thereby, the user can arbitrarily change the phase of the point of interest related to the second information signal by changing the value of the third parameter. For example, when the information signal is an image signal, the point of interest is the output pixel position, and an output image signal having an arbitrary density can be obtained with respect to the input image signal. In this case, even if there is a change in the value of the third parameter, since a plurality of coefficient data of the estimation formula is generated with one generation formula, there is no increase in the required coefficient data, and the coefficient data is stored. It is possible to suppress an increase in memory capacity.
[0023]
The coefficient information generation device according to the present invention is used to obtain coefficient data of an estimation formula used when converting a first information signal composed of a plurality of information data into a second information signal composed of a plurality of information data. And generating a plurality of pieces of information data located around a point of interest related to a teacher signal corresponding to a second information signal from a student signal corresponding to a first information signal. Based on the plurality of information data selected by the data selection means and the information data of the attention point related to the teacher signal, the estimation equations respectively corresponding to the plurality of information data selected by the data selection means Based on coefficient data calculation means for calculating a plurality of coefficient data and a plurality of coefficient data of the estimation expression obtained by the coefficient data calculation means, the coefficient information of the generation expression is calculated. Is intended and a coefficient information calculating means for calculating.
[0024]
  Also, the coefficient information generation method according to the present invention is for obtaining coefficient data of an estimation formula used when converting a first information signal composed of a plurality of information data into a second information signal composed of a plurality of information data. A plurality of pieces of information data located around a point of interest related to a teacher signal corresponding to a second information signal from a student signal corresponding to the first information signal And a plurality of pieces of information data selected in the first step based on the plurality of pieces of information data selected in the first step and the information data of the attention point related to the teacher signal, respectively. A second step of calculating a plurality of coefficient data of the corresponding estimation formula, and calculating coefficient information of the generation formula based on the plurality of coefficient data of the estimation formula calculated in the second step In which a third step of obtaining Teis there.
[0025]
In the present invention, a plurality of information data located around the attention point related to the teacher signal is selected from the student signal. Based on the plurality of information data and the information data of the attention point related to the teacher signal, a plurality of coefficient data of the estimation formula corresponding to each of the plurality of information data is calculated. Then, based on the plurality of coefficient data of the estimation formula, coefficient information of the generation formula used to obtain the coefficient data of the estimation formula is calculated.
[0026]
The generation formula can be configured based on the coefficient information calculated in this way, and the coefficient of the estimation formula used when the first information signal is converted into the second information signal composed of a plurality of information data by the generation formula. Data can be obtained.
[0027]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, embodiments of the present invention will be described with reference to the drawings. FIG. 1 shows a configuration of a television receiver 100 as an embodiment. The television receiver 100 obtains an SD signal called a 525i signal from a broadcast signal, converts the 525i signal into an HD signal called a 1050i signal, and displays an image based on the 1050i signal.
[0028]
The television receiver 100 includes a microcomputer, and includes a system controller 101 for controlling the operation of the entire system, and a remote control signal receiving circuit 102 that receives a remote control signal. The remote control signal receiving circuit 102 is connected to the system controller 101, receives a remote control signal RM output from the remote control transmitter 200 according to a user operation, and supplies an operation signal corresponding to the signal RM to the system controller 101. Is configured to do.
[0029]
Further, the television receiver 100 is supplied with a receiving antenna 105 and a broadcast signal (RF modulated signal) captured by the receiving antenna 105, and performs a channel selection process, an intermediate frequency amplification process, a detection process, and the like. A tuner 106 for obtaining a signal (525i signal) and a buffer memory 109 for temporarily storing an SD signal output from the tuner 106 are provided.
[0030]
Further, the television receiver 100 converts the SD signal (525i signal) temporarily stored in the buffer memory 109 into an HD signal (1050i signal), and an output from the image signal processing unit 110. A display unit 111 for displaying an image by an HD signal, an OSD (On Screen Display) circuit 112 for generating a display signal SCH for displaying a character figure or the like on the screen of the display unit 111, and And a combiner 113 for combining the display signal SCH with the HD signal output from the image signal processing unit 110 and supplying it to the display unit 111. The display unit 111 is configured by a flat panel display such as a CRT (cathode-ray tube) display or an LCD (liquid crystal display).
[0031]
The operation of the television receiver 100 shown in FIG. 1 will be described.
The SD signal (525i signal) output from the tuner 106 is supplied to the buffer memory 109 and temporarily stored. The SD signal temporarily stored in the buffer memory 109 is supplied to the image signal processing unit 110 and converted into an HD signal (1050i signal). That is, the image signal processing unit 110 obtains pixel data (hereinafter referred to as “HD pixel data”) constituting an HD signal from pixel data (hereinafter referred to as “SD pixel data”) that constitutes an SD signal. The HD signal output from the image signal processing unit 110 is supplied to the display unit 111, and an image based on the HD signal is displayed on the screen of the display unit 111.
[0032]
The user can change the horizontal and vertical resolutions of the image displayed on the screen of the display unit 111 as described above by operating the remote control transmitter 200. As will be described later, the image signal processing unit 110 calculates the HD pixel data by an estimation formula. As the coefficient data of this estimation formula, horizontal and vertical resolutions adjusted by the user's operation of the remote control transmitter 200 are used. Those corresponding to the values of the parameters h and v to be determined are generated and used by a generation formula including these parameters h and v. Thereby, the horizontal and vertical resolutions of the image by the HD signal output from the image signal processing unit 110 correspond to the adjusted values of the parameters h and v.
[0033]
Further, the user can change the number and position of prediction taps by operating the remote control transmitter 200. A prediction tap is an SD pixel used for prediction. As described above, when the HD pixel data is calculated by the estimation formula, SD pixel data as the prediction tap is used. As the coefficient data of the estimation formula, only the number of prediction taps is required, and this coefficient data differs depending on the position of the prediction tap.
[0034]
By changing the number and position of the prediction taps, the image quality of the HD signal output from the image signal processing unit 110 is changed. In general, the image quality improves as the number of predicted taps increases. For example, the user can select an arbitrary predicted tap pattern by operating the remote control transmitter 200 from predicted tap patterns as shown in FIGS. Note that the user may arbitrarily create a predicted tap pattern.
[0035]
In the adjustment state of the parameters h and v described above, the values of the parameters h and v are displayed on the screen of the display unit 111. The user can adjust the values of the parameters h and v with reference to this display. Moreover, in the selection state of a prediction tap pattern, the display of the prediction tap pattern example (FIG. 2 (a)-(i)) is performed on the screen of the display part 111. FIG. The user can select an arbitrary predicted tap pattern with reference to this display.
[0036]
Next, details of the image signal processing unit 110 will be described. The image signal processing unit 110 selectively extracts data of a plurality of SD pixels located around the target pixel related to the HD signal (1050i signal) from the SD signal (525i signal) stored in the buffer memory 109. Output first to third tap selection circuits 121 to 123.
[0037]
The first tap selection circuit 121 selectively extracts data of SD pixels (referred to as “prediction taps”) used for prediction. A tap selection signal STP is supplied from the system controller 101 to the first tap selection circuit 121. In the tap selection circuit 121, SD pixel data of the number of taps and the tap position specified by the tap selection signal STP is selectively extracted.
[0038]
The second tap selection circuit 122 selectively extracts data of SD pixels (referred to as “space class taps”) used for class classification corresponding to the level distribution pattern of the SD pixel data. The third tap selection circuit 123 selectively extracts data of SD pixels (referred to as “motion class taps”) used for class classification corresponding to motion. When the space class is determined using SD pixel data belonging to a plurality of fields, motion information is also included in this space class.
[0039]
Further, the image signal processing unit 110 detects the level distribution pattern of the space class tap data (SD pixel data) selectively extracted by the second tap selection circuit 122, and determines the space class based on the level distribution pattern. It has a spatial class detection circuit 124 that detects and outputs the class information.
[0040]
In the space class detection circuit 124, for example, an operation is performed to compress each SD pixel data from 8-bit data to 2-bit data. The space class detection circuit 124 outputs compressed data corresponding to each SD pixel data as class information of the space class. In the present embodiment, data compression is performed by ADRC (Adaptive Dynamic Range Coding). As the information compression means, DPCM (predictive coding), VQ (vector quantization), or the like may be used in addition to ADRC.
[0041]
Originally, ADRC is an adaptive requantization method developed for high performance coding for VTR (Video Tape Recorder), but it can efficiently express local patterns of signal level with short word length. It is suitable for use in data compression. When ADRC is used, the maximum value of space class tap data (SD pixel data) is MAX, the minimum value is MIN, the dynamic range of space class tap data is DR (= MAX−MIN + 1), and the number of requantization bits Is P, the requantized code qi as compressed data is obtained by the calculation of the equation (1) for each SD pixel data ki as the space class tap data. However, in the expression (1), [] means a truncation process. When there are Na SD pixel data as the space class tap data, i = 1 to Na.
qi = [(ki-MIN + 0.5). 2^P/ DR] (1)
[0042]
The image signal processing unit 110 detects a motion class mainly representing the degree of motion from the motion class tap data (SD pixel data) selectively extracted by the third tap selection circuit 123, and A motion class detection circuit 125 that outputs class information is provided.
[0043]
In the motion class detection circuit 125, the inter-frame difference is calculated from the motion class tap data (SD pixel data) mi, ni selectively extracted by the third tap selection circuit 123, and the average of absolute values of the differences is calculated. Threshold processing is performed on the value to detect a motion class that is an index of motion. That is, in the motion class detection circuit 125, the average value AV of the absolute value of the difference is calculated by the equation (2). For example, when 12 pieces of SD pixel data m1 to m6 and n1 to n6 are extracted by the third tap selection circuit 123 as described above, Nb in the expression (2) is 6.
[0044]
[Expression 1]

[0045]
Then, in the motion class detection circuit 125, the average value AV calculated as described above is compared with one or a plurality of threshold values to obtain class information MV of the motion class. For example, three thresholds th1, th2, th3 (th1 <th2 <th3) are prepared, and when four motion classes are detected, when AV ≦ th1, MV = 0 and th1 <AV ≦ th2 Is MV = 2 when MV = 1, th2 <AV ≦ th3, and MV = 3 when th3 <AV.
[0046]
Also, the image signal processing unit 110 is based on the requantization code qi as the class information of the space class output from the space class detection circuit 124 and the class information MV of the motion class output from the motion class detection circuit 125. A class combining circuit 126 for obtaining a class code CL indicating a class to which a pixel (target pixel) of an HD signal (1050i signal) to be created belongs is provided.
[0047]
In the class synthesis circuit 126, the calculation of the class code CL is performed by the equation (3). In equation (3), Na represents the number of space class tap data (SD pixel data), and P represents the number of requantization bits in ADRC.
[0048]
[Expression 2]

[0049]
The image signal processing unit 110 has a coefficient memory 134. The coefficient memory 134 stores, for each class, a plurality of coefficient data of an estimation formula used in the estimation prediction calculation circuit 127 described later. This coefficient data is information for converting an SD signal (525i signal) into an HD signal (1050i signal). The class code CL output from the above class synthesis circuit 126 is supplied to the coefficient memory 134 as read address information, and a plurality of coefficient data Wi of the estimation formula corresponding to the class code CL is read from the coefficient memory 134. The estimated prediction calculation circuit 127 is supplied.
[0050]
Further, the image signal processing unit 110 has an information memory bank 135. In the information memory bank 135, coefficient information for each class is stored in advance. This coefficient information is coefficient information of a generation formula for generating coefficient data to be stored in the coefficient memory 134 described above.
[0051]
The coefficient information will be described in detail.
In the estimated prediction calculation circuit 127 described later, the HD pixel data to be created from the prediction tap data (SD pixel data) xi and a plurality of coefficient data Wi read from the coefficient memory 134 by the estimation expression (4). y is calculated. When the number of predicted taps selected by the first tap selection circuit 121 is 10, n in the equation (4) is 10.
[0052]
[Equation 3]

[0053]
In the previous application (Japanese Patent Application No. 2000-348730) by the present applicant, the coefficient data Wi (i = 1 to n) of this estimation formula is expressed by parameters h and v as shown in, for example, formula (5). It was proposed to generate by the generating formula including. Coefficient data w of this generation formula_Ten~ W_n9Is previously obtained by learning and stored in the information memory bank.
[0054]
[Expression 4]

[0055]
FIG. 3 shows the values of a plurality of coefficient data Wi generated by, for example, the above-described equation (5), that is, coefficient values (indicated by “●”) are separated from the center of the prediction tap with the coefficient value at the center of the prediction tap as the center. It shows how coefficient values change when arranged in order corresponding to each tap position in the direction. The change in the coefficient value is a change like a sine function. This is the same tendency when viewed from the center of the prediction tap in any direction such as the horizontal direction, the vertical direction, and the diagonal direction.
[0056]
Here, when the parameter h is made variable among the parameters h and v and the parameter v is fixed to a certain value, the coefficient value changes like a sine function at every value of the parameter v as shown in FIG. It becomes. In this case, a difference in the value of the parameter h appears as a difference in the gain of the sine function. Conversely, the same result can be obtained when the parameter v is made variable among the parameters h and v and the parameter h is fixed to a certain value. When the values of the parameters h and v are fixed, the spatial spread of the coefficient values as a whole is as shown in FIG.
[0057]
In consideration of the change of the coefficient values described above, in the present embodiment, Equation (6) is used as a generation equation for generating a plurality of coefficient data Wi of the estimation equation. In the equation (6), x and y indicate positions in the horizontal direction and the vertical direction, respectively.
[0058]
[Equation 5]

[0059]
In the equation (6), f (x, y) is a correction term. It is provided to increase the approximation accuracy of coefficient data. It is not necessary to provide a correction term. When a correction term is provided, the shape of the function is not limited as long as it is a function that can be corrected accurately, and either the horizontal direction or the vertical direction or all directions are corrected. Functions can be used. The basic form of the function of the correction term is f (x) = sinxⁿ/ Xⁿ, F (x) = e^-x, F (x) = 1 / x^-nF (x) = δ (x) can be considered.
[0060]
In Equation (6), k and m are phase shifts in the sine function. At this position, the maximum value that is the center of the function appears. If the position corresponding to the maximum value is defined as the output pixel position of the HD signal, the relationship between the phase shift and the output pixel position is as shown in FIG. Note that FIG. 6 focuses only on the horizontal direction.
[0061]
When the output pixel position is shifted by k and m with respect to the center predicted tap position, the coefficient value of each predicted tap is set to (0, 0) as the center predicted tap position as shown in FIG. How far the tap position is in the horizontal and vertical directions from the center can be obtained by substituting the equation (6) as a decimal value. Note that FIG. 7 focuses only on the horizontal direction.
[0062]
However, since the distance to the adjacent tap position in each of the horizontal and vertical directions is 1, 0 ≦ k and m ≦ 1. For example, when k changes to k ′, the coefficient value of each prediction tap changes from “●” to “◯”.
[0063]
In equation (6), a, b, and c are coefficient data generated from a function including parameters h and v. In the present embodiment, the coefficient data a, b, and c are calculated by the equation (7), respectively. In the information memory bank 135, coefficient seed data w which is coefficient data of this arithmetic expression is stored._Ten~ W₃₅Is stored as the coefficient information described above. This coefficient seed data w_Ten~ W₃₅The generation method of will be described later.
[0064]
[Formula 6]

[0065]
In addition, the image signal processing unit 110 performs coefficient seed data w for each class._Ten~ W₃₅Is used to calculate the coefficient data ac for each class according to the equation (7), and based on the coefficient data ac of each class and the tap selection signal STP from the system controller 101, the equation (6) Thus, each class has a coefficient generation circuit 136 that generates coefficient data Wi of the estimation formula corresponding to each prediction tap. In this case, the coefficient data Wi of the estimation formula corresponding to each prediction tap is obtained by substituting how far in the horizontal and vertical directions from the center of each prediction tap position into x and y in the equation (6), Desired. The coefficient data Wi (i = 1 to n) of each class generated by the coefficient generation circuit 136 described above is stored in the coefficient memory 134 described above.
[0066]
As described above, when the 525i signal is converted into the 1050i signal, it is necessary to obtain four pixels of the 1050i signal corresponding to one pixel of the 525i signal in each of the odd and even fields. In this case, the four pixels in the 2 × 2 unit pixel block constituting the 1050i signal in each of the odd and even fields have different phase shifts from the center prediction tap described above.
[0067]
FIG. 8 shows a 4-pixel HD in a 2 × 2 unit pixel block constituting a 1050i signal in an odd field.₁~ HD_FourCenter prediction tap SD₀The phase shift from is shown. Where HD₁~ HD_FourThe position of each is SD₀K horizontally from the position of₁~ K_Four, M in the vertical direction₁~ M_FourIt is only shifted.
[0068]
FIG. 9 shows four-pixel HD in a 2 × 2 unit pixel block constituting a 1050i signal in an even field.₁'~ HD_FourCenter prediction tap SD in ′₀The phase shift from ′ is shown. Where HD₁'~ HD_FourThe positions of ′ are respectively SD₀Horizontally from the position of ′₁′ 〜K_Four′, M in the vertical direction₁'~ M_FourIt is shifted by ′.
[0069]
Each of these output pixels (HD₁~ HD_Four, HD₁'~ HD_Four′) Deviation value information k₁~ K_Four, M₁~ M_Four, K₁′ 〜K_Four′, M₁'~ M_Four′ Is also stored in the information memory bank 135.
[0070]
The coefficient generation circuit 136 generates coefficient data Wi of an estimation formula corresponding to each prediction tap for each combination of class and output pixel based on the information on the deviation value of each output pixel stored in the information memory bank 135. Is done. In this case, when obtaining the coefficient data Wi corresponding to a certain output pixel, k and m in the equation (6) are set as deviation values in the horizontal and vertical directions of the output pixel.
[0071]
Generation of coefficient data Wi of each class in the coefficient generation circuit 136 is performed, for example, in each vertical blanking period. Thus, even if the values of the parameters h and v are changed by the user's operation of the remote control transmitter 200, the coefficient data Wi of each class stored in the coefficient memory 134 corresponds to the values of the parameters h and v. Therefore, the user can adjust the resolution smoothly.
[0072]
Further, the image signal processing unit 110 calculates the normalization coefficient S corresponding to the coefficient data Wi (i = 1 to n) of each class generated by the coefficient generation circuit 136 according to the equation (8). The calculation unit 137 includes a normalization coefficient memory 138 that stores the normalization coefficient S generated here for each class. The normalization coefficient memory 138 is supplied with the class code CL output from the above class synthesis circuit 126 as read address information, and the normalization coefficient S corresponding to the class code CL is read from the normalization coefficient memory 138. This is supplied to the normalization operation circuit 128 described later.
[0073]
[Expression 7]

[0074]
Further, the image signal processing unit 110 uses the prediction tap data (SD pixel data) xi selectively extracted by the first tap selection circuit 121 and the coefficient data Wi read from the coefficient memory 134, from the equation (4). The estimation prediction calculation circuit 127 that calculates the data of the pixel (target pixel) of the HD signal to be created is calculated using the estimation formula (1).
[0075]
As described above, when an SD signal (525i signal) is converted into an HD signal (1050i signal), four pixels of the HD signal (HD in FIG.₁~ HD_Four, HD in FIG.₁'~ HD_FourTherefore, the estimated prediction calculation circuit 127 generates pixel data for each 2 × 2 unit pixel block constituting the HD signal. That is, in the estimated prediction calculation circuit 127, the first tap selection circuit 121 configures the prediction pixel data xi corresponding to the four pixels (target pixel) in the unit pixel block and the unit memory block from the coefficient memory 134. The coefficient data Wi corresponding to the four pixels to be supplied is supplied, and the data y of the four pixels constituting the unit pixel block₁~ Y_FourAre individually calculated by the estimation equation (4) described above.
[0076]
The image signal processing unit 110 also outputs 4-pixel data y sequentially output from the estimated prediction calculation circuit 127.₁~ Y_FourIs normalized by dividing by the normalization coefficient S corresponding to the coefficient data Wi (i = 1 to n) used for each calculation read out from the normalization coefficient memory 138 ing. Although not described above, the coefficient generation circuit 136 obtains the coefficient data Wi of the estimation formula from the coefficient seed data by the generation formula. The generated coefficient data includes a rounding error, and the coefficient data Wi (i = 1 to n). Is not guaranteed to be 1.0. Therefore, the data y of each pixel calculated by the estimated prediction calculation circuit 127₁~ Y_FourBecomes a level fluctuation due to a rounding error. As described above, normalization by the normalization arithmetic circuit 128 can eliminate the fluctuation.
[0077]
The image signal processing unit 110 also normalizes the data y of four pixels in the unit pixel block that is normalized by the normalization calculation circuit 128 and sequentially supplied.₁′ 〜Y_Four′ Is line-sequentially processed and output in the format of a 1050i signal.
[0078]
Next, the operation of the image signal processing unit 110 will be described.
From the SD signal (525i signal) stored in the buffer memory 109, the second tap selection circuit 122 uses the second tap selection circuit 122 to surround the four pixels (target pixel) in the unit pixel block constituting the HD signal (1050i signal) to be created The data of the space class tap located at (SD pixel data) is selectively extracted. The space class tap data (SD pixel data) selectively extracted by the second tap selection circuit 122 is supplied to the space class detection circuit 124. In this space class detection circuit 124, each of the SD pixel data as space class tap data is subjected to ADRC processing and re-used as class information of the space class (mainly class classification for waveform expression in space). A quantization code qi is obtained (see equation (1)).
[0079]
In addition, from the SD signal (525i signal) stored in the buffer memory 109, the third tap selection circuit 123 uses the four pixels (target pixel) in the unit pixel block constituting the HD signal (1050i signal) to be created. The data (SD pixel data) of the motion class tap located in the vicinity of is selectively extracted. The motion class tap data (SD pixel data) selectively extracted by the third tap selection circuit 123 is supplied to the motion class detection circuit 125. In this motion class detection circuit 125, class information MV of a motion class (mainly class classification for representing the degree of motion) is obtained from each SD pixel data as motion class tap data.
[0080]
The motion information MV and the above-described requantization code qi are supplied to the class synthesis circuit 126. In this class synthesizing circuit 126, from these motion information MV and requantization code qi, for each unit pixel block constituting the HD signal (1050i signal) to be created, four pixels (target pixel) in the unit pixel block are obtained. A class code CL indicating the class to which it belongs is obtained (see equation (3)). The class code CL is supplied to the coefficient memory 134 and the normalized coefficient memory 138 as read address information.
[0081]
In the coefficient memory 134, for example, in each vertical blanking period, a class and an output pixel (HD) corresponding to the values of parameters h and v adjusted by the user by the coefficient generation circuit 136 are stored.₁~ HD_Four, HD₁'~ HD_FourFor each combination of ′), coefficient data Wi (i = 1 to n) of the estimation formula is generated by the coefficient generation circuit 136 and stored. In this case, the coefficient selection circuit 136 is supplied with the tap selection signal STP from the system controller 101. Therefore, the coefficient generation circuit 136 predicts the predicted tap pattern selected by the tap selection signal STP (see FIGS. 2A to 2I). The coefficient data Wi of the estimation formula corresponding to each prediction tap included in () is generated. In the normalized coefficient memory 138, the normalized coefficient S corresponding to the coefficient data Wi (i = 1 to n) of the estimation formula generated by the coefficient generation circuit 136 as described above is stored in the normalized coefficient calculation unit 137. Generated and stored.
[0082]
As described above, the class code CL is supplied to the coefficient memory 134 as read address information, so that four output pixels corresponding to the class code CL from the coefficient memory 134 ((HD in the odd field).₁~ HD_Four, Even field is HD₁'~ HD_FourThe coefficient data Wi of the estimation equation for ′) is read and supplied to the estimated prediction calculation circuit 127. Further, the four pixels (target pixel) in the unit pixel block constituting the HD signal (1050i signal) to be created by the first tap selection circuit 121 from the SD signal (525i signal) stored in the buffer memory 109. Prediction tap data (SD pixel data) located in the vicinity of the area is selectively extracted. In this case, the tap selection signal STP from the system controller 101 is supplied to the first tap selection circuit 121. Therefore, in the first tap selection circuit 121, the predicted tap pattern selected by the tap selection signal STP (FIG. ) To (i))), the prediction tap data (SD pixel data) xi included in the prediction tap is extracted.
[0083]
In the estimated prediction calculation circuit 127, the prediction tap data (SD pixel data) xi and the coefficient data Wi for four output pixels read from the coefficient memory 134, 4 in the unit pixel block constituting the HD signal to be created. Pixel (attention pixel) data y₁~ Y_FourAre calculated simultaneously (see equation (4)). Then, the data y of the four pixels in the unit pixel block constituting the HD signal sequentially output from the estimated prediction calculation circuit 127.₁~ Y_FourIs supplied to the normalization operation circuit 128.
[0084]
As described above, the class code CL is supplied to the normalization coefficient memory 138 as read address information, and is output from the normalization coefficient S corresponding to the class code CL, that is, the estimated prediction calculation circuit 127. HD pixel data y₁~ Y_FourThe normalization coefficient S corresponding to the coefficient data Wi used for the calculation is read out and supplied to the normalization calculation circuit 128. In the normalization operation circuit 128, the HD pixel data y output from the estimated prediction operation circuit 127 is displayed.₁~ Y_FourAre each divided by the corresponding normalization factor S and normalized. Thereby, the data y due to the rounding error when obtaining the coefficient data of the estimation formula (see formula (4)) by the generation formula (see formula (6)).₁~ Y_FourLevel fluctuations are eliminated.
[0085]
In this way, the data y of the four pixels in the unit pixel block which are normalized by the normalization operation circuit 128 and sequentially output.₁′ 〜Y_Four'Is supplied to the post-processing circuit 129. In the post-processing circuit 129, data y of four pixels in the unit pixel block sequentially supplied from the normalization arithmetic circuit 128 is obtained.₁′ 〜Y_Four'Is line-sequentialized and output in the format of a 1050i signal. That is, the post-processing circuit 129 outputs a 1050i signal as an HD signal.
[0086]
As described above, in the television receiver 100 shown in FIG. 1, the coefficient seed data w loaded from the information memory bank 135 by the coefficient generation circuit 136 is used._Ten~ W₃₅And each output pixel (HD₁~ HD_Four, HD₁'~ HD_FourThe coefficient data Wi (i = 1 to n) of the estimation formula corresponding to the values of the parameters h and v is generated for each combination of the class and the output pixel using the information on the deviation value with respect to the center prediction tap of ′). This is stored in the coefficient memory 134. Then, the HD pixel data y is calculated by the estimated prediction calculation circuit 127 using the coefficient data Wi (i = 1 to n) read from the coefficient memory 134 corresponding to the class code CL.
[0087]
Therefore, the user can arbitrarily adjust the horizontal and vertical image quality of the image obtained by the HD signal by adjusting the values of the parameters h and v. In this case, coefficient data Wi for each class corresponding to the adjusted values of the parameters h and v is generated and used by the coefficient generation circuit 136 each time, and a memory for storing a large amount of coefficient data is used. do not need.
[0088]
Further, in the television receiver 100 shown in FIG. 1, the coefficient data Wi of the estimation formula corresponding to each prediction tap is generated by one generation formula shown by the formula (6). Therefore, even if the number of predicted taps increases, the required coefficient data does not increase, and the increase in capacity of the information memory bank 135 can be suppressed.
[0089]
In the television receiver 100 shown in FIG. 1, the user can change the number and position of the prediction taps by selecting one prediction tap pattern from a plurality of prediction tap patterns by operating the remote control transmitter 200. . Thereby, the image quality of the HD signal from the image signal processing unit 110 can be arbitrarily changed.
[0090]
Next, coefficient seed data w stored in the information memory bank 135 of the image signal processing unit 110 in FIG._Ten~ W₃₅The generation method of will be described.
FIG. 10 shows the concept of this generation method. A plurality of SD signals are generated from the HD signal. For example, a total of 81 types of SD signals are generated by varying the parameters h and v for varying the horizontal band and vertical band of the filter used when generating the SD signal from the HD signal in 9 stages. Learning is performed between each SD signal and HD signal generated in this way, and coefficient data Wi of the estimation expression of Expression (4) is generated for each stepped value of the parameters h and v. Then, using the generated coefficient data Wi, coefficient seed data w_Ten~ W₃₅Is generated.
[0091]
First, how to obtain the coefficient data Wi of the estimation formula will be described.
Here, an example will be shown in which the coefficient data Wi (i = 1 to n) of the estimation formula (4) is obtained by the least square method. As a generalized example, consider the observation equation (9), where X is input data, W is coefficient data, and Y is a predicted value. In this equation (9), m represents the number of learning data, and n represents the number of prediction taps.
[0092]
[Equation 8]

[0093]
The least square method is applied to data collected by the observation equation (9). Consider the residual equation (10) based on the observation equation (9).
[0094]
[Equation 9]

[0095]
From the residual equation of equation (10), the most probable value of each Wi is e in equation (11).²It is considered that the condition for minimizing is satisfied. That is, the condition of equation (12) should be considered.
[0096]
[Expression 10]

[0097]
That is, consider n conditions based on i in the equation (12), and satisfy this condition.₁, W₂, ..., W_nMay be calculated. Therefore, Equation (13) is obtained from the residual equation of Equation (10). Furthermore, the equation (14) is obtained from the equations (13) and (9).
[0098]
[Expression 11]

[0099]
And the normal equation of (15) Formula is obtained from (10) Formula and (14) Formula.
[0100]
[Expression 12]

[0101]
Since the number of equations equal to the number n of unknowns can be established as the normal equation of equation (15), the most probable value of each Wi can be obtained. In this case, simultaneous equations are solved using a sweeping method (Gauss-Jordan elimination method) or the like. Note that the normal equation (15) is generated individually corresponding to the stepped values of the parameters h and v, and the coefficient data Wi corresponding to each is obtained.
[0102]
Next, coefficient seed data w_Ten~ W₃₅Explain how to find out.
Substituting equation (7) into equation (6), coefficient seed data w_Ten~ W₃₅And a basic function expressed by the phase shifts k and m. Then, a known phase shift is substituted into k and m of this basic function, and the remaining coefficient seed data w_Ten~ W₃₅Is obtained by nonlinear least square approximation.
[0103]
Note that if the nonlinear least squares method does not have a constraint condition, the value may diverge. Therefore, the range that the coefficient values of a to c can be taken is limited, and the nonlinear least square method with a constraint condition is used. Approximation is performed by a certain SQR method (Successive quadratic programming method).
[0104]
FIG. 11 shows coefficient seed data w based on the concept shown in FIG._Ten~ W₃₅The configuration of the coefficient seed data generation device 150 for generating
The coefficient seed data generation device 150 performs an horizontal thinning process and a vertical thinning process on an input terminal 151 to which an HD signal (1050i signal) as a teacher signal is input, and an SD signal as a student signal. SD signal generation circuit 152 for obtaining
[0105]
The SD signal generation circuit 152 is supplied with parameters h and v as control signals. Corresponding to the parameters h and v, the horizontal band and the vertical band of the filter used when generating the SD signal from the HD signal are varied.
[0106]
This filter includes, for example, a one-dimensional Gaussian filter that limits a horizontal band and a one-dimensional Gaussian filter that generates a vertical band. This one-dimensional Gaussian filter is expressed by equation (16). In this case, a one-dimensional Gaussian filter having a frequency characteristic corresponding to the step value of h or v is obtained by changing the value of the standard deviation σ stepwise corresponding to the step value of h or v. Can do.
[0107]
[Formula 13]

[0108]
Further, the coefficient seed data generation device 150 selects data of a plurality of SD pixels located around the target pixel related to the HD signal (1050i signal) from the SD signal (525i signal) output from the SD signal generation circuit 152. The first to third tap selection circuits 153 to 155 for taking out and outputting the data are provided. These first to third tap selection circuits 153 to 155 are configured similarly to the first to third tap selection circuits 121 to 123 of the image signal processing unit 110 described above.
[0109]
The coefficient seed data generation device 150 detects the level distribution pattern of the space class tap data (SD pixel data) selectively extracted by the second tap selection circuit 154, and the space class based on the level distribution pattern. And a space class detection circuit 157 for outputting the class information. The space class detection circuit 157 is configured in the same manner as the space class detection circuit 124 of the image signal processing unit 110 described above. From this space class detection circuit 157, a requantization code qi for each SD pixel data as space class tap data is output as class information indicating a space class.
[0110]
The coefficient seed data generation device 150 detects a motion class mainly representing the degree of motion from the motion class tap data (SD pixel data) selectively extracted by the third tap selection circuit 155, A motion class detection circuit 158 that outputs the class information MV is provided. The motion class detection circuit 158 is configured in the same manner as the motion class detection circuit 125 of the image signal processing unit 110 described above. In this motion class detection circuit 158, the inter-frame difference is calculated from the motion class tap data (SD pixel data) selectively extracted by the third tap selection circuit 155, and the difference between the absolute values of the absolute values of the difference is calculated. Then, threshold processing is performed to detect a motion class that is an index of motion.
[0111]
Also, the coefficient seed data generation device 150 is based on the requantization code qi as the class information of the space class output from the space class detection circuit 157 and the class information MV of the motion class output from the motion class detection circuit 158. And a class synthesis circuit 159 for obtaining a class code CL indicating a class to which the target pixel relating to the HD signal (1050i signal) belongs. The class synthesis circuit 159 is also configured similarly to the class synthesis circuit 126 of the image signal processing unit 110 described above.
[0112]
Also, the coefficient seed data generation device 150 selects each HD pixel data y as target pixel data obtained from the HD signal supplied to the input terminal 151, and a first tap selection corresponding to each HD pixel data y. Coefficient data for each class is obtained from the prediction tap data (SD pixel data) xi selectively extracted by the circuit 153 and the class code CL output from the class synthesis circuit 159 corresponding to each HD pixel data y. A normal equation generating unit 160 that generates a normal equation (see equation (15)) for obtaining Wi (i = 1 to n) is provided.
[0113]
In this case, learning data is generated by a combination of one HD pixel data y and n prediction tap data (SD pixel data) xi, but the normal equation generation unit 160 outputs pixels (HD in FIG. 8).₁~ HD_Four, HD in FIG.₁'~ HD_FourA normal equation is generated for each time (see '). For example, HD₁The normal equation corresponding to, the deviation value for the center prediction tap is HD₁Is generated from learning data composed of data y of HD pixels having the same relationship.
[0114]
Further, the parameters h and v to the SD signal generation circuit 152 are sequentially changed, and the normal equation generation unit 160 generates normal equations corresponding to the stepped values of the parameters h and v, respectively. As a result, the normal equation generation unit 160 generates a normal equation for obtaining coefficient data Wi for each combination of class and output pixel corresponding to the stepwise values of the parameters h and v.
[0115]
Also, the coefficient seed data generation device 150 is supplied with data of each normal equation generated by the normal equation generation unit 160, solves each normal equation, and corresponds to the stepwise values of the parameters h and v, respectively. For each combination of class and output pixel, a coefficient data determination unit 161 for obtaining coefficient data Wi and a coefficient memory 162 for storing the obtained coefficient data Wi are provided. In the coefficient data determination unit 161, the normal equation is solved by, for example, a sweeping method, and the coefficient data Wi is obtained.
[0116]
Further, the coefficient seed data generation device 150 generates coefficient seed data w._Ten~ W₃₅Has a basic function generation unit 163 for generating a basic function for obtaining. In the basic function generation unit 163, the basic function is generated by substituting the above expression (7) into the above expression (6).
[0117]
In addition, the coefficient seed data generation device 150 uses the basic function generated by the basic function generation unit 163, and uses the coefficient data Wi obtained corresponding to the stepwise values of the parameters h and v for each class. The coefficient seed data w of each class by nonlinear least square approximation_Ten~ W₃₅Coefficient seed data generation unit 164 for obtaining the coefficient seed data w for each class generated by the coefficient seed data generation unit 164_Ten~ W₃₅Is stored in the coefficient seed memory 165.
[0118]
The operation of the coefficient data generation device 150 shown in FIG. 11 will be described.
An HD signal (1050i signal) as a teacher signal is supplied to the input terminal 151, and the HD signal is subjected to horizontal and vertical thinning processing by the SD signal generation circuit 152, and an SD signal (525i) as a student signal. Signal) is generated. In this case, parameters h and v are supplied as control signals to the SD signal generation circuit 152, and a plurality of SD signals whose horizontal and vertical bands change stepwise are sequentially generated.
[0119]
From this SD signal (525i signal), the second tap selection circuit 154 selectively extracts data of the space class tap (SD pixel data) located around the target pixel related to the HD signal (1050i signal). The space class tap data (SD pixel data) selectively extracted by the second tap selection circuit 154 is supplied to the space class detection circuit 157. In this space class detection circuit 157, each SD pixel data as space class tap data is subjected to ADRC processing to be re-used as class information of a space class (mainly class classification for waveform expression in space). A quantization code qi is obtained (see equation (1)).
[0120]
Further, from the SD signal generated by the SD signal generation circuit 152, the third tap selection circuit 155 selectively extracts data of the motion class tap (SD pixel data) located around the target pixel related to the HD signal. It is. The motion class tap data (SD pixel data) selectively extracted by the third tap selection circuit 155 is supplied to the motion class detection circuit 158. In this motion class detection circuit 158, class information MV of a motion class (mainly class classification for representing the degree of motion) is obtained from each SD pixel data as motion class tap data.
[0121]
The class information MV and the above-described requantization code qi are supplied to the class synthesis circuit 159. The class synthesis circuit 159 obtains a class code CL indicating the class to which the pixel of interest related to the HD signal (1050i signal) belongs from the class information MV and the requantization code qi (see equation (3)).
[0122]
Further, from the SD signal generated by the SD signal generation circuit 152, the first tap selection circuit 153 selectively extracts data of prediction taps (SD pixel data) located around the target pixel related to the HD signal. . Then, each HD pixel data y as target pixel data obtained from the HD signal supplied to the input terminal 151 and the first tap selection circuit 153 are selectively extracted corresponding to each HD pixel data y. From the prediction tap data (SD pixel data) xi and the class code CL output from the class synthesizing circuit 159 corresponding to each HD pixel data y, the normal equation generation unit 160 performs stepwise adjustment of the parameters h and v. A normal equation for obtaining coefficient data Wi is individually generated for each combination of class and output pixel corresponding to each of the different values.
[0123]
Then, each normal equation is solved by the coefficient data determining unit 161, and coefficient data Wi for each combination of class and output pixel corresponding to the stepwise values of the parameters h and v is obtained. Stored in the coefficient memory 162.
[0124]
In addition, in the basic function generation unit 163, coefficient seed data w_Ten~ W₃₅A basic function for obtaining is generated. The deviation value from the center prediction tap of the output pixel is substituted for k and m of this basic function. Then, the coefficient seed data generation unit 164 uses the above-described basic function, and uses coefficient data Wi obtained corresponding to the stepwise values of the parameters h and v for each class, by nonlinear least square approximation. Coefficient seed data for each class_Ten~ W₃₅Are obtained, and their coefficient seed data w_Ten~ W₃₅Is stored in the coefficient seed memory 165.
[0125]
In this case, the function form of the correction term f (x, y) in equation (6) is sequentially changed, and the function to which the correction term with the smallest square error is added is used as the final basic function. Coefficient seed data w generated using simple basic functions_Ten~ W₃₅Is stored in the coefficient seed memory 165. In this case, the final basic function is also used in the coefficient generation circuit 136 in the image signal processing unit 110 of FIG.
[0126]
As described above, in the coefficient seed data generation device 150 shown in FIG. 11, the coefficient seed data w of each class stored in the information memory bank 135 of the image signal processing unit 110 in FIG._Ten~ W₃₅Can be generated.
[0127]
The coefficient seed data generation device 150 shown in FIG. 11 performs learning (coefficient seed data generation) using an example of generating a student signal (525i signal) from a teacher signal (1050i signal). However, learning may be performed using the teacher signal and the student signal obtained independently by using an imaging device capable of acquiring the teacher signal and the student signal.
[0128]
Note that the processing in the image signal processing unit 110 in FIG. 1 can be realized by software, for example, by an image signal processing apparatus 300 as shown in FIG.
First, the image signal processing apparatus 300 shown in FIG. 12 will be described. The image signal processing apparatus 300 includes a CPU 301 that controls the operation of the entire apparatus, a ROM (read only memory) 302 that stores an operation program of the CPU 301, coefficient seed data, and the like, and a RAM ( random access memory) 303. These CPU 301, ROM 302, and RAM 303 are each connected to a bus 304.
[0129]
The image signal processing apparatus 300 also includes a hard disk drive (HDD) 305 as an external storage device and a floppy (R) disk drive (FDD) 307 that drives a floppy (R) disk 306. These drives 305 and 307 are each connected to a bus 304.
[0130]
In addition, the image signal processing apparatus 300 includes a communication unit 308 that is connected to a communication network 400 such as the Internet by wire or wirelessly. The communication unit 308 is connected to the bus 304 via the interface 309.
[0131]
In addition, the image signal processing device 300 includes a user interface unit. The user interface unit includes a remote control signal receiving circuit 310 that receives a remote control signal RM from the remote control transmitter 200, and a display 311 that includes an LCD (liquid crystal display) or the like. The receiving circuit 310 is connected to the bus 304 via the interface 312, and similarly the display 311 is connected to the bus 304 via the interface 313.
[0132]
The image signal processing apparatus 300 also has an input terminal 314 for inputting an SD signal and an output terminal 315 for outputting an HD signal. The input terminal 314 is connected to the bus 304 via the interface 316, and similarly, the output terminal 315 is connected to the bus 304 via the interface 317.
[0133]
Here, instead of storing the processing program, coefficient seed data, and the like in the ROM 302 in advance as described above, for example, they are downloaded from the communication network 400 such as the Internet via the communication unit 308 and stored in the hard disk or RAM 303 for use. You can also These processing programs, coefficient seed data, and the like may be provided on the floppy (R) disk 306.
[0134]
Further, instead of inputting the SD signal to be processed from the input terminal 314, it may be recorded in advance on a hard disk or downloaded from the communication network 400 such as the Internet via the communication unit 308. Also, instead of outputting the processed HD signal to the output terminal 315 or in parallel therewith, it is supplied to the display 311 to display an image, further stored in a hard disk, or via the communication unit 308 such as the Internet. It may be sent to the communication network 400.
[0135]
A processing procedure for obtaining an HD signal from an SD signal in the image signal processing apparatus 300 shown in FIG. 12 will be described with reference to the flowchart of FIG.
First, in step ST1, processing is started, and in step ST2, SD pixel data is input in frame units or field units. When the SD pixel data is input from the input terminal 314, the SD pixel data is temporarily stored in the RAM 303. Further, when the SD pixel data is recorded on the hard disk, the SD pixel data is read by the hard disk drive 307 and temporarily stored in the RAM 303. In step ST3, it is determined whether or not processing of all frames or fields of input SD pixel data has been completed. When the process is finished, the process ends in step ST4. On the other hand, when the process is not finished, the process proceeds to step ST5.
[0136]
In step ST5, the image quality designation values input by the user by operating the remote control transmitter 200, that is, the values of the parameters h and v are read from the RAM 303, for example. In step ST6, the coefficient seed data w of each class is read from the ROM 302, for example._Ten~ W₃₅And output pixels (HD₁~ HD_Four, HD₁'~ HD_FourThe information of the deviation value of ′) is read.
[0137]
Next, in step ST7, the position (predicted tap coordinate) of each predicted tap corresponding to the predicted tap pattern input by the user operating the remote control transmitter 200 is acquired from, for example, a table in the ROM 302. In step ST8, the parameters h and v read in step ST5 and the coefficient seed data w acquired in step ST6 are displayed._Ten~ W₃₅Is substituted into the equation (7) to obtain the coefficient data of a to c in the equation (6), and then the deviation value of each output pixel obtained in step ST6 is added to k and m of the basic function in the equation (6). Substituting and generating coefficient data Wi corresponding to each prediction tap for each combination of class and output pixel.
[0138]
Next, in step ST9, pixel data of class taps and prediction taps are acquired from the SD pixel data input in step ST2 corresponding to each HD pixel data to be generated. In step ST10, it is determined whether or not the processing for obtaining HD pixel data has been completed in all areas of the input SD pixel data. If completed, the process returns to step ST2 and proceeds to the input process of SD pixel data of the next frame or field. On the other hand, when the process has not ended, the process proceeds to step ST11.
[0139]
In step ST11, the class code CL is generated from the SD pixel data of the class tap acquired in step ST9. In step ST12, the coefficient data corresponding to the class code CL and the SD pixel data of the prediction tap are used to generate HD pixel data according to the estimation formula, and then the process returns to step ST9 and is the same as described above. Repeat the process.
[0140]
In this way, by performing processing according to the flowchart shown in FIG. 13, it is possible to process the SD pixel data constituting the input SD signal and obtain HD pixel data constituting the HD signal. As described above, the HD signal obtained by such processing is output to the output terminal 315, supplied to the display 311 to display an image, and further supplied to the hard disk drive 305 to be stored on the hard disk. It is recorded.
Although illustration of the processing device is omitted, the processing in the coefficient seed data generation device 150 in FIG. 11 can be realized by software.
[0141]
A processing procedure for generating coefficient seed data will be described with reference to the flowchart of FIG.
First, in step ST41, processing is started, and in step ST42, an image quality pattern (for example, specified by parameters h and v) used for learning is selected. In step ST43, it is determined whether coefficient data calculation processing for all image quality patterns has been completed. If not completed, the process proceeds to step ST44.
[0142]
In this step ST44, known HD pixel data is input in frame units or field units. In step ST45, it is determined whether or not processing has been completed for all HD pixel data. If not completed, in step ST46, SD pixel data is generated from the HD pixel data input in step ST44 based on the image quality pattern selected in step ST42.
[0143]
In step ST47, the pixel data of the class tap and the prediction tap are acquired from the SD pixel data generated in step ST46, corresponding to each HD pixel data input in step ST44. In step ST48, it is determined whether or not the learning process has been completed in the entire region of the generated SD pixel data. When the learning process is completed, the process returns to step ST44, the next HD pixel data is input, and the same process as described above is repeated. On the other hand, when the learning process is not completed, the process proceeds to step ST49. Proceed to
[0144]
In this step ST49, the class code CL is generated from the SD pixel data of the class tap acquired in step ST47. In step ST50, a normal equation for obtaining coefficient data (see equation (15)) is generated. Here, a normal equation for obtaining coefficient data Wi is individually generated for each combination of class and output pixel. Thereafter, the process returns to step ST47.
[0145]
When the processing for all the HD pixel data is completed in step ST45 described above, in step ST51, each normal equation generated in step ST50 is solved by a sweeping method or the like, and a coefficient is calculated for each combination of class and output pixel. Data Wi is calculated. Thereafter, the process returns to step ST42, the next image quality pattern is selected, and the same processing as described above is repeated to obtain coefficient data Wi for each combination of class and output pixel corresponding to the next image quality pattern.
[0146]
If the calculation process of coefficient data Wi for all image quality patterns is completed in step ST43 described above, the process proceeds to step ST52. In this step ST52, the above equation (7) is substituted into the above equation (6), and the coefficient seed data w_Ten~ W₃₅A basic function for obtaining is generated.
[0147]
Then, in step ST53, using the basic function generated in step ST52, using the coefficient data Wi for each combination of class and output pixel obtained corresponding to the stepwise values of the parameters h and v, respectively, Coefficient seed data w of each class by nonlinear least square approximation_Ten~ W₃₅In step ST54, coefficient class data w of each class is obtained._Ten~ W₃₅Is stored in the memory, and then the process ends in step ST55.
[0148]
In this way, by performing processing according to the flowchart shown in FIG. 14, the coefficient seed data w of each class is obtained by the same method as the coefficient seed data generation apparatus 150 shown in FIG._Ten~ W₃₅Can be obtained.
[0149]
In the above-described embodiment, the user can change the number and position of the prediction taps and can adjust the horizontal and vertical resolutions by operating the remote control transmitter 200, but only one of them can be adjusted. What can be made can be similarly configured.
[0150]
In the above-described embodiment, the coefficient information stored in the information memory bank 135 is represented by the equation (7) for calculating the coefficient data a to c of the generation equation (see equation (6)). Coefficient seed data w which is coefficient data_Ten~ W₃₅However, in the case where the horizontal and vertical resolutions are not changed, the coefficient data a to c themselves of the above-described generation formula (see formula (6)) may be stored as coefficient information.
[0151]
In the above embodiment, the information memory bank 135 stores coefficient class data w for each class._Ten~ W₃₅In addition to the output pixel (HD₁~ HD_Four, HD₁'~ HD_FourInformation on the deviation value of ') is stored. The information on the deviation value is determined by the relationship between the SD signal (525i signal) and the HD signal (1050i signal), but information on the deviation value determined by the relationship between the SD signal (525i signal) and other HD signals is used. By storing, coefficient data Wi corresponding to each output pixel of other HD signals can be generated in the same manner, and other HD signals can be generated. In this case, information on the deviation value is not stored in the information memory bank 135, but information on the deviation value corresponding to the type of HD signal to be generated is supplied from the system controller 101 to the coefficient generation circuit 136 each time. You may do it. This makes it possible to easily obtain an HD signal having a resolution (number of pixels) that matches the characteristics of the display element used in the display unit 111.
[0152]
In the above-described embodiment, the linear equation is used as the estimation equation when generating the HD signal. However, the present invention is not limited to this, and for example, a higher-order equation is used as the estimation equation. It may be a thing.
Moreover, although the case where the information signal is an image signal has been shown in the above-described embodiment, the present invention is not limited to this. For example, the present invention can be similarly applied when the information signal is an audio signal.
[0153]
【The invention's effect】
According to the present invention, the plurality of coefficient data of the estimation formula used when converting the first information signal into the second information signal is the first position located around the attention point related to the second information signal. This is generated using a generation formula that includes a parameter for specifying the position of multiple pieces of information data in the information signal. Even if the number of predicted taps increases, the capacity of the memory that stores the coefficient data increases. The memory capacity can be saved.
[Brief description of the drawings]
FIG. 1 is a block diagram illustrating a configuration of a television receiver as an embodiment.
FIG. 2 is a diagram illustrating an example of a predicted tap pattern.
FIG. 3 is a diagram showing how coefficient values change in the same direction, such as horizontal, vertical, and diagonal.
FIG. 4 is a diagram illustrating how coefficient values change at each predicted tap position when a parameter v indicating vertical resolution is fixed and a parameter h indicating horizontal resolution is variable.
FIG. 5 is a diagram illustrating a spatial spread of coefficient values when the values of parameters h and v are fixed.
FIG. 6 is a diagram illustrating a relationship between a phase shift in the horizontal direction and an output pixel position.
FIG. 7 is a diagram illustrating a relationship between a phase shift and a coefficient value of each prediction tap.
FIG. 8 is a diagram illustrating a phase shift (odd field) from a center prediction tap of four pixels in a unit pixel block of an HD signal (1050i signal).
FIG. 9 is a diagram illustrating a phase shift (even field) from a center prediction tap of four pixels in a unit pixel block of an HD signal (1050i signal).
FIG. 10 is a diagram illustrating an example of a coefficient seed data generation method.
FIG. 11 is a block diagram illustrating a configuration example of a coefficient seed data generation apparatus.
FIG. 12 is a block diagram illustrating a configuration example of an image signal processing device to be realized by software.
FIG. 13 is a flowchart showing a processing procedure of an image signal.
FIG. 14 is a flowchart showing a coefficient seed data generation process.
FIG. 15 is a diagram illustrating a pixel position relationship between a 525i signal and a 1050i signal.
[Explanation of symbols]
DESCRIPTION OF SYMBOLS 100 ... Television receiver, 101 ... System controller, 102 ... Remote control signal receiving circuit, 105 ... Receiving antenna, 106 ... Tuner, 110 ... Image signal processing part, 111 ... Display unit 112 ... OSD circuit 121 ... first tap selection circuit 122 ... second tap selection circuit 123 ... third tap selection circuit 124 ... space class detection Circuit: 125... Motion class detection circuit, 126... Class synthesis circuit, 127... Prediction prediction calculation circuit, 128... Normalization calculation circuit, 129. Memory, 135 ... Information memory bank, 136 ... Coefficient generation circuit, 137 ... Normalized coefficient arithmetic unit, 138 ... Normalized coefficient memory, 150 ... Coefficient seed data generation 151 ... input terminal 152 ... SD signal generation circuit 153 ... first tap selection circuit 154 ... second tap selection circuit 155 ... third tap selection circuit 157... Spatial class detection circuit 158... Motion class detection circuit 159... Class synthesis circuit 160... Normal equation generation unit 161. Memory 163... Basic function generator, 164... Coefficient seed data generator, 165... Coefficient seed memory, 200... Remote control transmitter, 300.

Claims (17)

A coefficient data generation device that generates coefficient data of an estimation formula used when converting a first information signal composed of a plurality of information data into a second information signal composed of a plurality of information data,
A generation formula for generating coefficient data of the estimation formula, including a first parameter indicating the position of a plurality of pieces of information data of the first information signal located around the point of interest related to the second information signal Storage means for storing the coefficient information of
Based on the value of the first parameter indicating the position of a plurality of pieces of information data of the first information signal, using the generation formula configured based on the coefficient information stored in the storage means, the first parameter A coefficient data generation device comprising: coefficient data generation means for generating a plurality of coefficient data of the estimation formula corresponding respectively to a plurality of information data of one information signal. The coefficient data generation device according to claim 1, further comprising first parameter setting means for setting a value of the first parameter. Second parameter setting means for setting a value of a second parameter that determines the quality of the output obtained by the second information signal;
The generation formula including the first parameter further includes the second parameter,
The coefficient data generation device according to claim 1, wherein the coefficient data generation means generates a plurality of coefficient data of the estimation formula corresponding to the value of the second parameter. The storage means stores coefficient seed data, which is coefficient data of an arithmetic expression including the second parameter, for calculating coefficient data of the generation expression including the first parameter,
The coefficient data generating means is
Using the coefficient seed data stored in the storage means and the value of the second parameter set by the second parameter setting means, the generation formula including the first parameter by the arithmetic expression is used. A first calculation unit for calculating coefficient data;
A second arithmetic unit that uses the coefficient data of the generation formula including the first parameter calculated by the first calculation unit to calculate a plurality of coefficient data of the estimation formula by the generation formula. The coefficient data generation device according to claim 3. Third parameter setting means for setting a value of a third parameter indicating a phase of a point of interest related to the second information signal with reference to a position of information data of the first information signal;
The generation formula including the first parameter further includes the third parameter,
The coefficient data generation device according to claim 1, wherein the coefficient data generation means generates a plurality of coefficient data of the estimation formula corresponding to the value of the third parameter. A coefficient data generation method for generating coefficient data of an estimation formula used when converting a first information signal composed of a plurality of information data into a second information signal composed of a plurality of information data,
A generation formula for generating coefficient data of the estimation formula, including a first parameter indicating the position of a plurality of pieces of information data of the first information signal located around the point of interest related to the second information signal Configuring the generation formula based on the coefficient information of:
The estimation corresponding to each of the plurality of information data of the first information signal based on the value of the first parameter indicating the position of the plurality of information data of the first information signal using the generation formula Generating a plurality of coefficient data of the equation. A coefficient data generation method comprising: An information signal processing device for converting a first information signal composed of a plurality of information data into a second information signal composed of a plurality of information data,
First data selection means for selecting, from the first information signal, a plurality of first information data located around a point of interest related to the second information signal;
Using a generation formula including a first parameter for designating positions of the plurality of first information data selected by the first data selection means, the plurality of information data of the first information signal Coefficient data generating means for generating a plurality of coefficient data of estimation equations respectively corresponding to the plurality of first information data based on the value of the first parameter indicating the position;
From the plurality of first information data selected by the first data selection means and the plurality of coefficient data generated by the coefficient data generation means, information on the attention point is obtained using the estimation formula. An information signal processing apparatus comprising: an arithmetic means for calculating data. The information signal processing apparatus according to claim 7 , further comprising first parameter setting means for setting a value of the first parameter. Second parameter setting means for setting a value of a second parameter that determines the quality of the output obtained by the second information signal;
The generation formula including the first parameter further includes the second parameter,
Said coefficient data generation means, as claimed in claim 7, characterized in that for generating a plurality of coefficient data of said estimated equation corresponding to the value of the second parameter set in the second parameter setting means Information signal processing device. The coefficient data generating means is
A storage unit for storing coefficient seed data, which is coefficient data of an arithmetic expression including the second parameter, for calculating coefficient data of the generation expression including the first parameter;
Using the coefficient seed data stored in the storage unit and the value of the second parameter set by the second parameter setting means, the generation formula including the first parameter by the arithmetic expression A first calculation unit for calculating coefficient data;
Using a coefficient data of the generation formula including the first parameter calculated by the first calculation unit, and a second calculation unit that calculates a plurality of coefficient data of the estimation formula by the generation formula. 10. The information signal processing apparatus according to claim 9 , wherein Third parameter setting means for setting a value of a third parameter indicating a phase of a point of interest related to the second information signal with reference to a position of information data of the first information signal;
The generation formula including the first parameter further includes the third parameter,
8. The information signal processing apparatus according to claim 7 , wherein the coefficient data generating means generates a plurality of coefficient data of the estimation formula corresponding to the value of the third parameter. Second data selection means for selecting, from the first information signal, a plurality of second information data located around the point of interest related to the second information signal;
Class detection means for detecting a class to which the attention point belongs based on the plurality of second information data selected by the second data selection means;
The coefficient data generating means is
A coefficient data generation unit that generates a plurality of coefficient data of the estimation formula for each class detected by the class detection unit;
A storage unit that stores a plurality of coefficient data of the estimation formula for each class generated by the coefficient data generation unit;
The coefficient data reading part which reads and outputs the several coefficient data of the said estimation formula corresponding to the class detected by the said class detection means from the said storage part, The output of Claim 7 characterized by the above-mentioned. Information signal processing device. Adding means for calculating a sum of coefficient data of the estimation formula generated by the coefficient data generating means;
The information signal processing apparatus according to claim 7 , further comprising: a normalizing unit that normalizes the information data of the attention point obtained by the calculating unit by dividing by the sum. An information signal processing method for converting a first information signal composed of a plurality of information data into a second information signal composed of a plurality of information data,
A first step of selecting, from the first information signal, a plurality of information data located around a point of interest related to the second information signal;
Using a generation formula including a parameter for designating the position of the plurality of information data selected in the first step, the value of the parameter indicating the position of the plurality of information data of the first information signal is set. A second step of generating a plurality of coefficient data of an estimation formula corresponding to each of the plurality of information data,
Obtained by calculating the information data of the attention point from the plurality of information data selected in the first step and the plurality of coefficient data generated in the second step using the estimation formula An information signal processing method comprising: a third step. An apparatus for generating coefficient information of a generation formula used to obtain coefficient data of an estimation formula used when converting a first information signal composed of a plurality of information data into a second information signal composed of a plurality of information data Because
Data selection means for selecting, from the student signal corresponding to the first information signal, a plurality of information data located around the attention point related to the teacher signal corresponding to the second information signal;
A plurality of estimation equations respectively corresponding to the plurality of information data selected by the data selection unit based on the plurality of information data selected by the data selection unit and the information data of the attention point related to the teacher signal Coefficient data calculation means for obtaining the coefficient data of
Coefficient information generating means, comprising: coefficient information calculating means for determining coefficient information of the generating expression based on a plurality of coefficient data of the estimating expression determined by the coefficient data calculating means. Parameter setting means for setting a parameter value that determines the quality of the output obtained by the student signal;
The coefficient data calculation means obtains a plurality of coefficient data of the estimation equation for each value of the parameter set by the parameter setting means,
16. The coefficient information generation apparatus according to claim 15 , wherein the coefficient information calculation means calculates coefficient seed data that is coefficient data of an arithmetic expression including the parameter for determining coefficient data of the generation expression. A method of generating coefficient information of a generation formula used to obtain coefficient data of an estimation formula used when converting a first information signal composed of a plurality of information data into a second information signal composed of a plurality of information data Because
A first step of selecting, from a student signal corresponding to the first information signal, a plurality of information data located around a point of interest related to a teacher signal corresponding to the second information signal;
The estimation formulas corresponding to the plurality of information data selected in the first step based on the plurality of information data selected in the first step and the information data of the attention point related to the teacher signal, respectively. A second step of calculating a plurality of coefficient data of
A coefficient information generation method comprising: a third step of calculating and calculating coefficient information of the generation formula based on a plurality of coefficient data of the estimation formula determined in the second step.

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