JP3627279B2 - Quantization apparatus and quantization method - Google Patents

Description

但し、〔ΔＳ_１ 〕、〔ΔＳ_２ 〕、〔ΔＳ_３ 〕、〔ΔＳ_４ 〕は、次式で定義される。
【００１０】

【００１１】
Ｓ／Ｎ評価値〔Ｓ／Ｎ〕は、従来の量子化装置で評価されるのと同様の量子化誤差である。空間変動評価値〔ΔＳ〕は、空間内の量子化復号値の信号変化量（すなわち、空間内の量子化復号値の傾き）と入力信号の信号変化量（すなわち、空間内の入力信号値の傾き）との比較を行うものである。量子化復号値の信号変化量を算出する場合、既に新規範Ｑ２により決定済みの、過去の画素の量子化値を使用して比較するという処理上の制約がある。図１４においては、量子化対象画素Ｌｘ（ｋ）に関し、処理済み画素は近傍４画素Ｌａ（ｋ）、Ｌｂ（ｋ）、Ｌｃ（ｋ）、Ｌｄ（ｋ）であり、これらを使用して〔ΔＳ_１ 〕、〔ΔＳ_２ 〕、〔ΔＳ_３ 〕、〔ΔＳ_４ 〕がそれぞれ求められる。
【００１２】
時間変動評価値〔ΔＴ〕は、量子化対象画素Ｌｘ（ｋ）と同一位置にある前フレームの画素Ｌｘ（ｋ−１）とに関し、入力信号のフレーム間の変化量と量子化復号値のフレーム間の信号変化量とを比較するものである。上述のように式（４）のＭＩＮ〔 〕は、〔 〕内の評価値を最小とする量子化値候補が最終的な量子化値として選択されることを意味する。その結果、従来の量子化装置で問題となる画質劣化が低減される。
【００１３】
その様子を図１３に示す。すなわち、従来の量子化装置では、入力信号が量子化境界レベル近傍で僅かに変動している場合、量子化復号値においては量子化ステップ幅に相当する信号変動に拡大されていたが、上述の式（４）に基づく新規範量子化装置においては、この信号変動は抑圧され安定した量子化復号化値が得られる。こうして新規範量子化装置によって、意図した画質改善が達成できる。
【００１４】
【発明が解決しようとする課題】
上述の新規範量子化装置と従来の量子化装置とを比較すると、画質劣化はかなり低減される。しかしながら、新規範量子化の構造に起因する独特な画質劣化が発生する。そのひとつに『時間へばりつき』パターンが挙げられる。この画質劣化は、式（４）における時間変動評価値の寄与率が高すぎる場合に発生する。
【００１５】
すなわち、物体輪郭部のような空間内信号変化が大きい対象が動く場合には、評価値の中で時間変動評価値が大きくなり、入力信号値の時間変化に追従した量子化値が選択される。一方、空間内の信号変化が小さい平坦部分が動いたときでは、時間変動評価値も小さくなる。この平坦部分が動く時には、時間変動評価値の全体の評価値に対する寄与率が適切でないと、動き部分であっても、過去と同じ量子化値が選択され、時間的に変化しない、『時間へばりつき』パターンが発生することになる。より具体的には、画面内の比較的大きな面積の物体が動いた時に、量子化復号値の画像では、その輪郭部は動くが、物体内の平坦部分が動かない現象が生じ、見る者が違和感を持つことになる。
【００１６】
従って、この発明の目的は、新規範による量子化に独特な画質劣化である、上述の『時間へばりつき』現象を防止できる量子化装置および量子化方法を提供することにある。
【００１７】
【課題を解決するための手段】
請求項１に記載の発明は、所定の量子化ビット数の入力信号値が供給され、量子化ビット数より少ないビット数の量子化値を出力する量子化手段と、入力信号値から画素毎の動き量を検出し、判定フラグを出力する動き量検出手段と、判定フラグが静止である場合、過去の静止連続数を計数し、計数した静止連続数を動き量の履歴として出力する動き履歴検出手段と、動き量の履歴に応じて重みを決定する重み決定手段と、入力信号値と、量子化手段から出力される量子化値と、重み決定手段から供給される重みとに基づいて、所定の量子化ビット数で生成可能な複数の量子化候補が算出され、算出された複数の量子化候補に関して、量子化値候補を復号し、復号した量子化値候補と入力信号値との差であるＳ／Ｎ評価値と、入力信号値の空間変動と量子化値候補の復号値の空間変動の差である空間変動評価値および入力信号値の時間変動と量子化値候補の復号値の時間変動の差である時間変動評価値とを重み付け加算した評価値をそれぞれ求め、評価値を最小とする量子化候補を量子化値として選択的に出力する判定手段とを有し、判定手段では、入力信号から得られる画素毎の動き量の履歴の静止連続数の増加に対して、Ｓ／Ｎ評価値に対する重みは一定とし、空間変動評価値に対する重みは減衰し、時間変動評価値に対する重みは大きくするように変化させるようにしたことを特徴とする量子化装置である。
【００１８】
さらに、請求項１０に記載の発明は、所定の量子化ビット数の入力信号値が供給され、量子化ビット数より少ないビット数の量子化値を出力する量子化手段と、入力信号値からブロック毎あるいは画面毎の動き量を検出し、判定フラグを出力する動き量検出手段と、判定フラグが静止である場合、過去の静止連続数を計数し、計数した静止連続数を動き量の履歴として出力する動き履歴検出手段と、動き量の履歴に応じて重みを決定する重み決定手段と、入力信号値と、量子化手段から出力される量子化値と、重み決定手段から供給される重みとに基づいて、所定の量子化ビット数で生成可能な複数の量子化候補が算出され、算出された複数の量子化候補に関して、量子化値候補を復号し、復号した量子化値候補と入力信号値との差であるＳ／Ｎ評価値と、入力信号値の空間変動と量子化値候補の復号値の空間変動の差である空間変動評価値および入力信号値の時間変動と量子化値候補の復号値の時間変動の差である時間変動評価値とを重み付け加算した評価値をそれぞれ求め、評価値を最小とする量子化候補を量子化値として選択的に出力する判定手段とを有し、判定手段では、入力信号から得られるブロック毎あるいは画面毎の動き量の履歴の静止連続数の増加に対して、Ｓ／Ｎ評価値に対する重みは一定とし、空間変動評価値に対する重みは減衰し、時間変動評価値に対する重みは大きくするように変化させるようにしたことを特徴とする量子化装置である。
【００１９】
そして、請求項１９に記載の発明は、所定の量子化ビット数の入力信号値が供給され、量子化ビット数より少ないビット数の量子化値を出力する量子化ステップと、
入力信号値から画素毎の動き量を検出し、判定フラグを出力する動き量検出ステップと、
判定フラグが静止である場合、過去の静止連続数を計数し、計数した静止連続数を動き量の履歴として出力する動き履歴検出ステップと、
動き量の履歴に応じて重みを決定する重み決定ステップと、
入力信号値と、量子化ステップから出力される量子化値と、重み決定ステップから供給される重みとに基づいて、所定の量子化ビット数で生成可能な複数の量子化候補が算出され、算出された複数の量子化候補に関して、量子化値候補を復号し、復号した量子化値候補と入力信号値との差であるＳ／Ｎ評価値と、入力信号値の空間変動と量子化値候補の復号値の空間変動の差である空間変動評価値および入力信号値の時間変動と量子化値候補の復号値の時間変動の差である時間変動評価値とを重み付け加算した評価値をそれぞれ求め、評価値を最小とする量子化候補を量子化値として選択的に出力する判定ステップとを有し、
判定ステップでは、入力信号から得られる画素毎の動き量の履歴の静止連続数の増加に対して、Ｓ／Ｎ評価値に対する重みは一定とし、空間変動評価値に対する重みは減衰し、時間変動評価値に対する重みは大きくするように変化させるようにしたことを特徴とする量子化方法である。
また、請求項２０に記載の発明は、所定の量子化ビット数の入力信号値が供給され、量子化ビット数より少ないビット数の量子化値を出力する量子化ステップと、入力信号値からブロック毎あるいは画面毎の動き量を検出し、判定フラグを出力する動き量検出ステップと、判定フラグが静止である場合、過去の静止連続数を計数し、計数した静止連続数を動き量の履歴として出力する動き履歴検出ステップと、動き量の履歴に応じて重みを決定する重み決定ステップと、入力信号値と、量子化ステップから出力される量子化値と、重み決定ステップから供給される重みとに基づいて、所定の量子化ビット数で生成可能な複数の量子化候補が算出され、算出された複数の量子化候補に関して、量子化値候補を復号し、復号した量子化値候補と入力信号値との差であるＳ／Ｎ評価値と、入力信号値の空間変動と量子化値候補の復号値の空間変動の差である空間変動評価値および入力信号値の時間変動と量子化値候補の復号値の時間変動の差である時間変動評価値とを重み付け加算した評価値をそれぞれ求め、評価値を最小とする量子化候補を量子化値として選択的に出力する判定ステップとを有し、判定ステップでは、入力信号から得られるブロック毎あるいは画面毎の動き量の履歴の静止連続数の増加に対して、Ｓ／Ｎ評価値に対する重みは一定とし、空間変動評価値に対する重みは減衰し、時間変動評価値に対する重みは大きくするように変化させるようにしたことを特徴とする量子化方法である。
【００２０】
【作用】
入力画像から検出される動き量の履歴に応じて、各規範の重みを変更することで、『時間へばりつき』パターンの発生を防止する。
【００２１】
【実施例】
以下、この発明に係る量子化装置の一実施例について説明する。この発明は、動き量を検出し、Ｓ／Ｎ評価値、空間変動評価値および／または時間変動評価値に対する重みを適切に制御するものである。そこで、動画像系列において過去から現在までの動き量の履歴（以下、動き履歴と称する）に基づき式（４）の新量子化規範Ｑ２での各規範の重みを変更することで上述の画質劣化の発生を防止する。以下、動き量の幾つかの検出方法について述べる。
【００２２】
動き量検出方法の第１の例は、時間差分絶対値の積算値により動き量を決定する手段である。ｋフレームの座標（ｘ，ｙ）における入力信号値Ｌ_{ｘ ，ｙ} （ｋ）、ｋ−１フレームの入力信号値をＬ_{ｘ ，ｙ} （ｋ−１）とすると、時間差分絶対値が式（１２）で定義され、対象ブロックの画素平均時間差分絶対値Ｍが式（１３）で定義される。勿論、必ずしも画素平均化を行う必要はない。
【００２３】
ΔＴ_ｘ，ｙ ＝Ｌ_ｘ，ｙ （ｋ） −Ｌ_ｘ，ｙ （ｋ−１） （１２）
【００２４】
【数１】

【００２５】
動き量検出方法の第２の例は、各画素の空間傾斜で正規化した時間差分絶対値を用いた動き量の決定法である。一般に、空間変動の大きい部分（輪郭部のような空間傾斜の大きい部分）での時間差分絶対値は大きくなる。一方、空間変動の小さい部分（平坦部のような空間傾斜の小さい部分）での時間差分絶対値は小さくなる。そこで、適正な動き量を算出するために、空間傾斜で正規化された時間差分絶対値を使用する。図１４の画素配置図における空間傾斜の算出例を式（１４）〜式（１８）に示す。
【００２６】
ΔＳ＝（｜ΔＳ_１ ｜＋｜ΔＳ_２ ｜＋｜ΔＳ_３ ｜＋｜ΔＳ_４ ｜）／４ （１４）
但し、｜ΔＳ_１ ｜、｜ΔＳ_２ ｜、｜ΔＳ_３ ｜、｜ΔＳ_４ ｜は、次式で定義されるもので、斜め方向、垂直方向および水平方向の空間傾斜を表す。
【００２７】

【００２８】
このように、算出された座標（ｘ，ｙ）における空間傾斜ΔＳ_ｘ，ｙ により正規化された画素平均動き量は、式（１９）で得られる。
【００２９】
【数２】

【００３０】
また、空間傾斜が極端に小さい画素においては正規化値が小さくなるため、動き量の感度が敏感になり過ぎる傾向がある。そこで、式（２０）のしきい値処理を導入する。すなわち、
ΔＳ_ｘ，ｙ ＜しきい値ＴＨ ： ΔＳ_ｘ，ｙ ＝１．０ （２０）
（ΔＳ_ｘ，ｙ ≧しきい値ＴＨ ： ΔＳ_ｘ，ｙ を変更しない。）
このしきい値処理により適正な動き量を算出することが可能となる。
【００３１】
第３の動き量検出法として、所謂、動きベクトルを使用することも可能である。その例を以下に説明する。一般的に、動きベクトル検出法には大きく分類して次の３種類が挙げられる。
（１）ブロックマッチング法
（２）勾配法
（３）位相相関法
【００３２】
ブロックマッチング法は、パターンマッチングと同じ発想で、現画像のブロック化された領域が、過去の画像中の何処に存在したか、現画像と過去画像の比較を行う。具体例としては、ブロック内対応画素毎の差分絶対値を加算し、ブロック毎の差分絶対値和（あるいは差分の二乗和）が最小となる位置を動きベクトルとするものである。
【００３３】
ブロックマッチング法を用いるブロックデータの構造例を図５に示す。隣接フレーム間での動きベクトルを検出する場合、空間的に対応する位置にブロック（Ｍ画素×Ｎライン）が設定される。ｋフレーム（現フレーム）とｋ−１フレーム（前フレーム）で探索座標分ずらし、すなわち、水平方向で（Ｘ＋Ｍ）画素、垂直方向で（Ｙ＋Ｎ）ラインずらし、各座標位置においてパターンマッチングを行ない、ブロック毎の差分絶対値和（あるいは差分の二乗和）が最小となる座標位置を検出する。
【００３４】
ｋフレームの座標（ｉ，ｊ） の画素レベルをＬ_ｋ （ｉ，ｊ） 、ｋ−１ フレームの座標（ｉ，ｊ） の画素レベルをＬ_ｋ−１ （ｉ，ｊ）とすると、座標（ｘ，ｙ）における評価式の例として式（２１）が挙げられる。
【００３５】
【数３】

【００３６】
図５の例においては、サーチ領域内の各座標についての評価式（式２１）の評価値Ｅを演算する。サーチ点の数はＸ・Ｙ点となる。その中で、評価値Ｅが最小となる座標（ｘ，ｙ）が動きベクトルに対応する。求められた動きベクトルをｖ＝（ｖ_ｘ ， ｖ_ｙ ）とすると、ｖ_ｘ ＝−ｘ、ｖ_ｙ ＝−ｙで与えられる。この手法は演算量が膨大となる欠点があるが、検出精度は良いので広く一般的に用いられている。
【００３７】
勾配法は、ある空間傾斜を持つ画素がある位置まで動くと、動き量に応じた時間差分が発生するというモデルに基づく。よって、時間差分を空間傾斜で割算すれば動きベクトルが得られる。勾配法の基本処理を次に示す。
【００３８】
座標（ｘ，ｙ）における画素値をｇ（ｘ，ｙ）とする。動きベクトルをｖ＝（ｖ_ｘ ，ｖ_ｙ ）とすると、次の時刻の画素値は、ｇ（ｘ−ｖ_ｘ ，ｙ−ｖ_ｙ ）となる。これをテーラー展開すると式（２２）になる。
【００３９】
【数４】

【００４０】
ここで時間差分を式（２３）で表す。
ｄ（ｘ，ｙ）＝ｇ（ｘ−ｖ_ｘ ，ｙ−ｖ_ｙ ）−ｇ（ｘ，ｙ） （２３）
これにより式（２４）が得られる。
ｖ・ｇｒａｄｇ（ｘ，ｙ） 〜 −ｄ（ｘ，ｙ） （２４）
この式（２４）により時間差分と空間勾配から動きベクトルを求めることが出来る。
【００４１】
あるブロック内の画素に対し、最小自乗法を式（２４）に適用し動きベクトルｖについて解くと、式（２５）、式（２６）が得られる。
ｖ_ｘ ＝−（ΣΔ_ｔ Δ_ｘ ）／（ΣΔ_ｘ ^２ ） （２５）
ｖ_ｙ ＝−（ΣΔ_ｔ Δ_ｙ ）／（ΣΔ_ｙ ^２ ） （２６）
Δ_ｔ は時間差分、Δ_ｘ は水平勾配、Δ_ｙ は垂直勾配を表す。
【００４２】
更に簡略化することで式（２７）、式（２８）が得られる。
ｖ_ｘ ＝−｛ΣΔ_ｔ ｓｉｇｎ（Δ_ｘ ）｝／（Σ｜Δ_ｘ ｜） （２７）
ｖ_ｙ ＝−｛ΣΔ_ｔ ｓｉｇｎ（Δ_ｙ ）｝／（Σ｜Δ_ｙ ｜） （２８）
ｓｉｇｎ（ ）は符号を表す。
【００４３】
一般的に勾配法による動き量検出には、式（２７）、式（２８）が用いられる。勾配法の演算量は少ないが、動き量が大きくなると検出される動きベクトルの精度が落ちるという欠点がある。それは前述のモデルが成り立たなくなるからである。しかしながら、実用上は、反復的に動きベクトルを順次検出していくなど、様々な工夫により精度を得るようにしている。
【００４４】
さらに、位相相関法は、現画像と過去画像の同一位置のブロックデータに対し、各々フーリエ変換を施し、周波数領域で位相のずれ量を検出し、その位相項を逆フリーエ変換を用いて動きベクトル値を検出する手法である。この手法の特徴として、精度を確保するためには、ある程度以上の大きいブロックサイズが要求される。そのためフーリエ変換により演算量が膨大となる。また、一般的に大きいブロックの中には複数の動きが存在し、その識別判定が難しくなるという欠点がある。
【００４５】
以上述べた動きベクトル検出法の何れかを適用し、図１の各ブロック毎に動きベクトルｖ＝（ｖ_ｘ ，ｖ_ｙ ） を検出する。こうして決定された画面全体の動きベクトルに対し、例えば、式（２９）で定義される動きベクトルノルムＭを動き量とする。
【００４６】
【数５】

【００４７】
この動き量は、上述の時間差分絶対値を使用する手法と比較すると、回路の負担は大きいが、精度は高い。
【００４８】
この実施例では、以上の手法に基づき検出された対象画素の動き量を過去から現在まで記憶し、その動き履歴により式（４）の各規範重みを制御することで、視覚特性に合致した量子化を実行するものである。規範重み特性の例としては、激しい動き画像ではＳ／Ｎ規範重みαの寄与率を上げることが考えられる。また、背景部などの静止領域においては、シーンチェンジ後などの静止画像開始時にはＳ／Ｎ規範重みα、空間変動規範重みβ、時間変動規範重みγも寄与させるが、静止連続時間が長くなるにつれて、空間変動規範重みβの寄与率を下げ、時間変動規範重みγの寄与率を大きくする。この制御により背景部などの静止領域において、エッジビジネスなどの空間内画質劣化が排除された量子化パターンを安定的に保持することが可能となる。
【００４９】
以上の手法により動き履歴に基づき決定されるＳ／Ｎ規範重みα、空間変動規範重みβ、時間変動規範重みγの特性例を図２、図３および図４に示す。図２の例ではＳ／Ｎ規範重みαは、常に一定としている。本来、Ｓ／Ｎ規範は新規範量子化の暴走を防止するために用いられる。他の規範重みとの関係で寄与率は変化するので、この例においては一定としている。図３の例は、空間変動規範重みβの特性を示している。この空間変動規範重みβは、上述のように静止連続時間により寄与率を減衰させる。
【００５０】
これは、『斜め流れ現象』と呼ばれる決定済み量子化値伝播問題を防止するためである。式（４）の判定においては過去の決定済み量子化値を使用しており、空間変動規範の寄与率が大き過ぎると判定順序に依存した特異な『斜め縞パターン』が発生することがある。この画質劣化を防止するために空間変動規範重みβを制御する。図４は、時間変動規範重みγの特性例である。この例では静止連続時間が長くなるにつれ、重みγを大きくすることで量子化値の時間変動を抑制し、信号安定性を高めた制御を実現する。以上のような規範重みの制御を行うことで所望の処理を実現する。
【００５１】
上述の検出法により得られた動き履歴に応じ、各規範の重みを変更する新規範量子化器の量子化値決定のフローチャートを図６に示す。基本的には、設定された量子化ビット数ｎで生成可能な全ての線形量子化値ｑ（ｉ）に関し、式（４）で定義される新規範評価値を算出し、その最小値を有する量子化値を出力値とする。
【００５２】
ステップ１では、上述したような方法を使用して、検出された対象画素の過去から現在までの動き検出結果から対象画素の動き履歴が生成される。次のステップ２において、生成された動き履歴からその対象画素の重みα、β、γが決定される。例として挙げた図２、図３および図４にそれぞれ示される特性に従って、これらの重みが決定される。そして、ステップ３において、カウンタｑに０が設定される。カウンタｑは、量子化値候補と対応している。次のステップ４では、ｑと対応する量子化値候補について、式（４）を使用した評価値の算出がなされる。その算出された新規範評価値が登録される。
【００５３】
ステップ５のインクリメントでは、カウンタｑに ｀＋１’ が加算され、ステップ６へ制御が移る。ステップ６のｑ＝Ｎでは、ステップ５（インクリメント）において、加算されたカウンタｑがＮと等しいか否かが判別され、ｑ≠Ｎの場合、ステップ４（評価値の算出および登録）へ制御が戻り、ｑ＝Ｎの場合、ステップ７へ制御が移る。すなわち、評価対象の量子化値の最大値が（Ｎ−１）の場合には、この（Ｎ−１）で設定される回数、ステップ４およびステップ５の処理が繰り返され、カウンタｑがＮに等しくなるとき、ループは終了する。
【００５４】
次に、ステップ７の評価値の最小値検出において、量子化値候補の内で最小の新規範評価値を生じさせる量子化値ｑが最終結果として選択される。ステップ８の量子化値ｑ登録において、選択された量子化値ｑが登録され、このフローチャートは終了する。
【００５５】
次に、この発明の量子化装置の処理を実現する一実施例のブロック図を図７に示す。入力端子１１から供給される入力信号値Ｌ（ｉ）、例えば各画素が８ビットに量子化されたディジタル画像信号は、量子化器１２、新規範処理部１３へ供給される。この新規範処理部１３は、動き量検出部１５、動き履歴検出部１６、重み決定部１７、判定部１８、メモリ部１９から構成され、入力端子１１から供給された入力信号値Ｌ（ｉ）は、動き量検出部１５、判定部１８およびメモリ部１９へ供給される。量子化器１２において、供給された入力信号値Ｌ（ｉ）は、８ビットより少ないｎビットへ量子化される。この量子化器１２からは、２^ｎ の数の量子化値候補が発生する。
【００５６】
量子化器１２により生成された線形量子化値ｑ（ｉ）は、ｄ３として判定部１８へ供給される。また、線形量子化値の上下の量子化値も生成され、判定部１８へ供給される。動き量検出部１５により検出された対象画素の動き判定フラグｄ０は、動き履歴検出部１６へ供給され、動き履歴検出部１６において、動き履歴が生成される。その動き履歴はｄ１として、重み決定部１７へ供給され、重み決定部１７では、動き履歴ｄ１に応答して、対象画素の重みが決定される。この重み決定部１７からの重みα、β、γがｄ２として判定部１８へ供給される。
【００５７】
新規範量子化においては、式（４）で定義される新規範Ｑ２が用いられるため、入力信号値Ｌ（ｉ）と決定済み量子化値ｄ４を記憶しておく必要がある。メモリ部１９からは、必要に応じて記憶データｄ４（すなわち、決定済み量子化値）が、判定部１８へ供給される。判定部１８では、上述した図６に示すフローチャートの処理が行われる。すなわち、供給された入力信号値Ｌ（ｉ）、重みｄ２、線形量子化値ｄ３、記憶データｄ４から式（４）の判定が実行された後、最終的な量子化値ｑ（ｉ）が選択され、出力端子１４を介して取り出される。
【００５８】
ここで、上述の動き量検出部１５の一例を図８に示す。この実施例は、局所的な動き量、すなわち画素毎の動き量を検出する手法である。入力信号値Ｌ（ｉ）が入力端子１１から供給され、その入力信号値Ｌ（ｉ）は、減算器２１およびメモリ部２２へ供給される。減算器２１では、入力信号値Ｌ（ｉ）と過去データであるメモリ部２２の出力値ｉ１との間で、例えば画素毎の時間差分値ｉ２が生成され、ＲＯＭ２３へ供給される。ＲＯＭ２３の処理の例としては、時間差分値ｉ２の絶対値化および静止判定などが行われる。その結果、出力ｄ０は局所的な動き量の動き判定フラグとなる。例えば、局所的な動き量は、動き判定フラグｄ０が ｀０’ の場合は静止となり、 ｀１’ の場合は動きとなる。この動き判定フラグｄ０が動き量検出部１５から出力端子２４を介して動き履歴検出部１６へ供給される。
【００５９】
ここで、動き履歴検出部１６の一例を図９のブロック図に示す。動き判定フラグｄ０は、各画素毎に独立アドレスを持つ動き履歴メモリの対応アドレスをアクセスする。そして、動き判定フラグｄ０が静止である場合は、動き履歴メモリに記憶されている過去の静止連続数をＲＯＭ３１により＋１インクリメントし、その値を書き込むことにより、そのアドレスの動き履歴メモリ３２の内容を更新する。その処理が図９中に示す動き履歴メモリ３２の出力ｄ１からＲＯＭ３１へのループである。また、動き判定フラグｄ０が静止でない場合は、ＲＯＭ３１によりｉ６として０が発生し、その値を書き込むことにより動き履歴メモリ３２の内容をクリアする。このような処理により、対象範囲の過去から現在までの動き履歴を生成することができる。この例における動き履歴とは、対象範囲の過去から現在までの静止連続時間に対応する。この動き履歴検出部１６の出力として、動き履歴ｄ１が重み決定部１７へ伝送される。
【００６０】
そして、重み決定部１７の一例を図１０のブロック図に示す。動き履歴検出部１６から動き履歴ｄ１が入力端子３３を介して供給され、その動き履歴ｄ１に基づき上述した図２、図３および図４に示すような特性の各規範の重みが決定される。動き履歴ｄ１は、ＲＯＭ４１、４２、４３へ供給され、ＲＯＭ４１は、Ｓ／Ｎ規範重みαの特性が記憶され、動き履歴ｄ１に応じて重みαがｄ２として、出力端子４４を介して判定部１８へ供給される。ＲＯＭ４２は、空間変動規範重みβの特性が記憶され、動き履歴ｄ１に応じて重みβがｄ２として、出力端子４５を介して判定部１８へ供給される。同様に、ＲＯＭ４３は、空間変動規範重みγの特性が記憶され、動き履歴ｄ１に応じて重みγがｄ２として、出力端子４６を介して判定部１８へ供給される。すなわち、動き履歴ｄ１に対応する各重みα、β、γは、ｄ２として、次の判定部１８へ供給される。
【００６１】
次に、判定部１８の一例を図１１のブロック図に示す。先ず、新規範評価部５３において、上述した式（４）の新規範評価値を最小化する量子化値の検出が行われる。ハードワイヤによる検出も可能であるが、ＣＰＵ等を用いた演算による検出でも実行される。この検出のためには、式（４）に必要な様々な信号が要求される。そこで、入力端子１１から入力信号値Ｌ（ｉ）、入力端子５２を介して量子化器１２からの線形量子化値ｑ（ｉ）がｄ３として、入力端子５１を介してメモリ部１９からの記憶データ（すなわち、決定済み量子化値）ｄ４、入力端子４４、４５、４６から規範重みα、β、γがｄ２として新規範評価部５３へ供給される。
【００６２】
新規範評価部５３で用いられる式（４）によると、メモリ部１９には、上述したように入力信号値Ｌ（ｉ）と決定済み量子化値ｑ（ｉ）を記憶しておく必要があり、新規範評価部５３における処理に応じてメモリ部１９からも必要な信号が記憶データｄ４として供給される。新規範評価部５３で検出された新規範最小化量子化値の選択コードｉ１１は、選択器５７へ供給される。量子化器１２からの線形量子化値ｄ３は、線形量子化値と上下の量子化値も伝送されるので、その３つの量子化値は、最終量子化値候補としてレジスタ５４、５５、５６に保持される。これらの量子化値ｉ１２、ｉ１３、ｉ１４は、選択器５７に入力され、上述の選択コードｉ１１により適切な値が選択され、判定部１８の決定済み量子化値ｑ（ｉ）として、出力端子１４から取り出される。
【００６３】
ここで、上述の動き量検出部１５の他の例を図１２に示す。この例は、大局的な動き量、すなわちブロック毎あるいは１画面毎の動き量を検出する方式である。入力信号値Ｌ（ｉ）が入力端子１１から供給され、その入力信号値Ｌ（ｉ）は、減算器６１およびメモリ部６２へ供給される。メモリ部６２では、入力信号値Ｌ（ｉ）に対して、例えば１フレームの遅延がなされ、その出力ｉ２１と入力信号値Ｌ（ｉ）は、減算器６１において、上述の式（１２）で示されるような画素毎の時間差分ｉ２２が生成される。時間差分ｉ２２は、ＲＯＭ６３により適切なデータｉ２３へ変換され、加算器６４へ供給される。一例として、時間差分ｉ２２は、このＲＯＭ６３において、差分値の絶対値化などが施され、データｉ２３として出力される。
【００６４】
加算器６４では、データｉ２３とレジスタ６５から供給される積算値ｉ２５との加算が行われ、その値ｉ２４は、レジスタ６５へ供給される。レジスタ６５では、積算値ｉ２５がＲＯＭ６６および加算器６４へ供給され、１ブロック毎に絶対値差分の積算がなされる。このレジスタ６５から出力される絶対値差分の積算値は、１ブロックに限らず、１画面の絶対値差分の積算値を出力することも可能である。ＲＯＭ６６の処理の例としては、積算値ｉ２５の静止判定が行われ、その結果、出力ｄ０は対象範囲の動き判定フラグとなる。例えば、大局的な動き量は、動き判定フラグｄ０が ｀０’ の場合、静止となり、 ｀１’ の場合、動きとなる。この動き判定フラグｄ０が動き量検出部１５から出力端子６７を介して動き履歴検出部１６へ供給される。
【００６５】
この新規範量子化装置により、従来の量子化装置の画質劣化を低減し、視覚特性に合致する量子化画像を得ることができる。
【００６６】
図７中の量子化器１２において、全ての量子化値を発生するのと異なり、Ｓ／Ｎ規範に基づいて選択された基準の量子化値を生成し、この量子化値と、その上下の量子化値の３種類の量子化値候補について、判定部１８が新規範評価値をそれぞれ求め、この３個の新規範評価値の中で最小のものと対応する量子化値候補を選択的に出力する構成としても良い。
【００６７】
なお、この発明は、空間変動評価値と時間変動評価値との一方のみを使用して新規範評価値を構成するようにしても良い。例えば、ディジタルオーディオ信号のような場合には、Ｓ／Ｎ評価値と時間変動評価値とを使用した評価値を使用することができる。
【００６８】
【発明の効果】
この発明によれば、視覚特性を考慮した量子化が行えることで、量子化境界レベル近傍の入力信号値の変化が拡大されることによる空間方向、あるいは時間方向の画質劣化を低減することができる。
【００６９】
また、この発明によれば、動き量が大きい場合、時間変動評価値に対する重みγを減少させることによって、重みγの値を適切なものとすることができ、冒頭に述べたような画質劣化を防止することが可能となる。
【００７０】
さらに、この発明によれば、元の量子化ビット数より少ないビット数でも良好な画像を得ることができ、例えばディジタル入力信号の圧縮を良好に得ることができる。
【図面の簡単な説明】
【図１】この発明に係る動き量検出のための画面分割の一例を示す略線図である。
【図２】時間変動評価値に対する重みの動き量に応じた変化の特性例を示す略線図である。
【図３】空間変動評価値に対する重みの動き量に応じた変化の特性例を示す略線図である。
【図４】Ｓ／Ｎ評価値に対する重みの動き量に応じた変化の特性例を示す略線図である。
【図５】動きベクトル検出方法の一例を説明するための略線図である。
【図６】この発明に係る可変重みの新規範量子化装置の判定部の一例を示すフローチャートである。
【図７】この発明に係る新規範量子化装置の一実施例を示すブロック図である。
【図８】この発明に係る動き量検出部の一例を示すブロック図である。
【図９】この発明に係る動き履歴検出部の一例を示すブロック図である。
【図１０】この発明に係る重み決定部の一例を示すブロック図である。
【図１１】この発明に係る判定部の一例を示すブロック図である。
【図１２】この発明に係る動き量検出部の他の例を示すブロック図である。
【図１３】入力信号に対して３ビット量子化が行われた一例を示す略線図である。
【図１４】画素の配置の一例を示すブロック図である。
【符号の説明】
１２ 量子化器
１３ 新規範処理部
１５ 動き量検出部
１６ 動き履歴検出部
１７ 重み決定部
１８ 判定部
１９ メモリ部[0001]
[Industrial application fields]
For example, when quantizing a digital image signal, the present invention provides a spatial variation criterion and / or a temporal variation in consideration of visual characteristics with respect to a quantization error minimum criterion defined by an input signal value and a quantized decoded value. The present invention relates to a quantization apparatus and a quantization method that take into account the norms.
[0002]
[Prior art]
For example, a digital image signal in which each pixel is represented by 8 bits is input, and the amount of data is compressed by quantizing (requantizing) each pixel with a bit number smaller than 8 bits. Conventionally, as this quantization, the quantization value is selected so that the quantization error between the input signal value and the quantized decoded value is minimized. In this quantization apparatus, when there is no extreme bias in the distribution of the input signal level, the integrated value of the quantization error is minimized and the S / N (Signal / Noise) ratio is the best. Therefore, in the conventional quantization apparatus, the quantization value is determined based on the S / N standard. The norm means a rule to be taken, that is, the S / N norm means a rule for selecting a quantized value that minimizes a quantization error.
[0003]
Here, the S / N norm Q1 is expressed by Expression (1). L (i) represents the input signal value, L (i) ^ represents the quantized decoded value, q (i) represents the quantized value, and n represents the number of quantized bits. Expressions (2) and (3) show a quantization expression and a decoding expression for a general 8-bit quantized input digital image signal.
[0004]
Q1 = MIN [| L (i) ^-L (i) |] (1)
q (i) = L (i) / (255/2ⁿ(2)
L (i) ^ = q (i) .255 / 2ⁿ                  (3)
[0005]
In equation (1), MIN [] means selecting a quantized value that minimizes the value in []. The quantization error of the quantized decoded value of each quantized value with respect to the input signal value is compared, and the quantized value is selected based on the S / N criterion Q1. FIG. 13 shows an example of 3-bit quantization. A conventional S / N normative quantization apparatus quantizes an input signal into 8 gradations, and outputs a median value of each quantization section as a quantized decoding value.
[0006]
According to FIG. 13, image quality degradation is observed in a flat region where the change of the input signal is small. That is, although the input signal slightly fluctuates across the region boundary level, the quantized decoded value is expanded to signal fluctuation corresponding to the quantization step width. Such image quality degradation occurs both in the spatial direction and in the time direction, and the edge portion of the image looks rough, that is, causes edge business and temporal degradation. Thus, when human visual characteristics are taken into account, quantization based on the S / N standard is not necessarily optimal. In particular, human visual characteristics are considered to be sensitive to spatial or temporal signal changes in the input signal, but conventional quantizers perform quantization based on the input signal value itself. There is a drawback that image quality deterioration due to signal change is conspicuous.
[0007]
In order to overcome the disadvantages of the conventional quantization apparatus based only on the S / N criterion, the present applicant has first described a quantization apparatus using the new category Q2 in consideration of human visual characteristics as described above. It has been proposed (see JP-A-6-169257). The new norm Q2 is shown in equation (4).
Q2 = MIN [α [S / N] + β [ΔS] + γ [ΔT]] (4)
[0008]
Here, [S / N] represents an S / N evaluation value, [ΔS] represents a spatial fluctuation evaluation value, [ΔT] represents a time fluctuation evaluation value, and α, β, and γ are weights. Represents. The new category Q2 represented by the equation (4) is obtained by calculating a new category evaluation value obtained by weighting and adding the S / N evaluation value, the spatial variation evaluation value, and the time variation evaluation value for a plurality of quantization value candidates. This is a rule for selecting a quantized value candidate that minimizes the value as an output quantized value. FIG. 14 shows a pixel arrangement diagram of spatially corresponding k frames and (k−1) frames. When quantizing a pixel having a value of Lx (k) of k frames, each evaluation value used in the new quantization criterion Q2 is expressed by the following equation.
[0009]

However, [ΔS₁  ], [ΔS₂  ], [ΔS₃  ], [ΔS₄  ] Is defined by the following equation.
[0010]

[0011]
The S / N evaluation value [S / N] is a quantization error similar to that evaluated by the conventional quantization apparatus. The spatial variation evaluation value [ΔS] is obtained by calculating the signal change amount of the quantized decoded value in space (that is, the slope of the quantized decoded value in space) and the signal change amount of the input signal (that is, the input signal value in space). (Slope). When the signal change amount of the quantized decoded value is calculated, there is a processing limitation that the comparison is performed using the quantized value of the past pixel that has already been determined by the new category Q2. In FIG. 14, regarding the quantization target pixel Lx (k), the processed pixels are the four neighboring pixels La (k), Lb (k), Lc (k), and Ld (k). ΔS₁  ], [ΔS₂  ], [ΔS₃  ], [ΔS₄  ] Are each required.
[0012]
The time variation evaluation value [ΔT] relates to the change amount between frames of the input signal and the frame of the quantized decoding value with respect to the pixel Lx (k−1) of the previous frame at the same position as the quantization target pixel Lx (k). The signal change amount is compared. As described above, MIN [] in Equation (4) means that a quantized value candidate that minimizes the evaluation value in [] is selected as the final quantized value. As a result, image quality degradation that is a problem in the conventional quantization apparatus is reduced.
[0013]
This is shown in FIG. That is, in the conventional quantization apparatus, when the input signal slightly fluctuates near the quantization boundary level, the quantized decoded value has been expanded to the signal fluctuation corresponding to the quantization step width. In the novel paraquantization apparatus based on Equation (4), this signal variation is suppressed, and a stable quantized decoding value is obtained. In this way, the intended image quality improvement can be achieved by the new paraquantizer.
[0014]
[Problems to be solved by the invention]
Comparing the above-mentioned novel quantizer with the conventional quantizer, the image quality degradation is considerably reduced. However, unique image quality degradation due to the new paraquantization structure occurs. One of these is the “time sticking” pattern. This image quality degradation occurs when the contribution rate of the time fluctuation evaluation value in equation (4) is too high.
[0015]
That is, when an object with a large signal change in space such as an object contour moves, the evaluation value of time variation becomes large in the evaluation value, and a quantized value that follows the time change of the input signal value is selected. . On the other hand, when the flat portion where the signal change in the space is small moves, the time fluctuation evaluation value also becomes small. When this flat part moves, if the contribution rate of the time fluctuation evaluation value to the overall evaluation value is not appropriate, the same quantized value as the past is selected even in the moving part, and it does not change in time. ] Pattern will occur. More specifically, when an object with a relatively large area in the screen moves, the contour of the quantized decoded value image moves, but the flat portion in the object does not move, and the viewer sees You will have a sense of incongruity.
[0016]
Accordingly, an object of the present invention is to provide a quantization apparatus and a quantization method capable of preventing the above-described “time sticking” phenomenon, which is a deterioration in image quality unique to quantization by a novel model.
[0017]
[Means for Solving the Problems]
According to the first aspect of the present invention, an input signal value having a predetermined number of quantization bits is supplied, and the number of quantization bits isFewer bitsQuantization that outputs the quantized value ofMeans and,A motion amount detecting means for detecting a motion amount for each pixel from the input signal value and outputting a determination flag; and when the determination flag is stationary, the past number of continuous continuations is counted, and the counted number of still continuations is calculated as a motion amount. Supplied from a motion history detecting means for outputting as a history, a weight determining means for determining a weight according to the history of the amount of motion, an input signal value, a quantized value output from the quantizing means, and a weight determining means Based on the weight, a plurality of quantization candidates that can be generated with a predetermined number of quantization bits are calculated and calculatedRegarding multiple quantization candidates,Decoded quantized value candidate and decodedQuantization value candidatesAnd the input signal valueS / N evaluation value that is the difference between the input signal value and the spatial variation evaluation value that is the difference between the spatial variation of the input signal value and the decoded value of the quantized value candidateandQuantization candidates that obtain evaluation values by weighting and adding the time fluctuation evaluation value that is the difference between the time fluctuation of the input signal value and the time fluctuation of the decoded value of the quantization value candidate, and minimize the evaluation valueTheDetermination means for selectively outputting as a quantized value,In judging means,input signalFor each pixel obtained fromHistory of movementFor the increase in the number of stationaryS / N evaluation valueThe weight for is constantSpatial variation evaluation valueThe weight for is attenuated,The weight for the time fluctuation evaluation value isTo make it biggerChangeI didThis is a quantizing device.
[0018]
And claims10In the invention described in the above, an input signal value having a predetermined number of quantization bits is supplied, and the number of quantization bits isFewer bitsQuantization that outputs the quantized value ofMeans and,Motion amount detection means that detects the amount of motion for each block or screen from the input signal value and outputs a judgment flag, and when the judgment flag is stationary, counts the number of past stationary continuations, From a motion history detecting means for outputting as a history of motion amount, a weight determining means for determining weight according to the history of motion amount, an input signal value, a quantized value output from the quantizing means, and a weight determining means Based on the supplied weight, a plurality of quantization candidates that can be generated with a predetermined number of quantization bits are calculated and calculated.Regarding multiple quantization candidates,Decoded quantized value candidate and decodedQuantization value candidatesAnd the input signal valueS / N evaluation value that is the difference between the input signal value and the spatial variation evaluation value that is the difference between the spatial variation of the input signal value and the decoded value of the quantized value candidateandQuantization candidates that obtain evaluation values by weighting and adding the time fluctuation evaluation value that is the difference between the time fluctuation of the input signal value and the time fluctuation of the decoded value of the quantization value candidate, and minimize the evaluation valueTheDetermination means for selectively outputting as a quantized value,In judging means,input signalFor each block or screen obtained fromHistory of movementFor the increase in the number of stationaryS / N evaluation valueThe weight for is constantSpatial variation evaluation valueThe weight for is attenuated,The weight for the time fluctuation evaluation value isTo make it biggerChangeI didThis is a quantizing device.
[0019]
And claims19In the invention described in the above, an input signal value having a predetermined number of quantization bits is supplied, and the number of quantization bits isFewer bitsQuantization that outputs the quantized value ofStep and,
A motion amount detection step of detecting a motion amount for each pixel from the input signal value and outputting a determination flag;
When the determination flag is stationary, a motion history detection step that counts the number of past stationary continuations and outputs the counted number of stationary continuations as a history of motion amount;
A weight determination step for determining a weight according to a history of the amount of movement;
Based on the input signal value, the quantization value output from the quantization step, and the weight supplied from the weight determination step, a plurality of quantization candidates that can be generated with a predetermined number of quantization bits are calculated. WasRegarding multiple quantization candidates,Decoded quantized value candidate and decodedQuantization value candidatesAnd the input signal valueS / N evaluation value that is the difference between the input signal value and the spatial variation evaluation value that is the difference between the spatial variation of the input signal value and the decoded value of the quantized value candidateandThe time fluctuation evaluation value, which is the difference between the time fluctuation of the input signal value and the time fluctuation of the decoded value candidate decoding value,TheQuantization candidates that minimize the evaluation value by obtaining the weighted evaluation valueTheA determination step of selectively outputting as a quantized value,
In the determination step, the weight for the S / N evaluation value is constant, the weight for the space fluctuation evaluation value is attenuated, and the time fluctuation evaluation is performed with respect to an increase in the number of still continuous motion amount history for each pixel obtained from the input signal. The weight for the value was changed to increase.This is a quantization method characterized by the above.
According to a twentieth aspect of the present invention, an input signal value having a predetermined number of quantization bits is supplied, a quantization step for outputting a quantization value having a smaller number of bits than the number of quantization bits, and a block from the input signal value A motion amount detection step for detecting a motion amount for each or each screen and outputting a determination flag; and when the determination flag is still, count the number of past still continuations, and use the counted number of still continuations as a history of motion amount A motion history detection step to output, a weight determination step to determine a weight according to the motion amount history, an input signal value, a quantization value output from the quantization step, and a weight supplied from the weight determination step Based on the above, a plurality of quantization candidates that can be generated with a predetermined number of quantization bits are calculated, the quantization value candidates are decoded with respect to the calculated plurality of quantization candidates, and the decoded quantization value candidates are input. The S / N evaluation value that is the difference from the signal value, the spatial variation evaluation value that is the difference between the spatial variation of the input signal value and the decoded value of the quantized value candidate, and the temporal variation and quantized value of the input signal value A determination step of obtaining an evaluation value obtained by weighting and adding a time variation evaluation value that is a difference in time variation of the candidate decoded values, and selectively outputting a quantization candidate that minimizes the evaluation value as a quantization value. In the determination step, the weight for the S / N evaluation value is constant and the weight for the space fluctuation evaluation value is attenuated with respect to the increase in the number of still continuous motion amount histories for each block or screen obtained from the input signal. The quantization method is characterized in that the weight for the time fluctuation evaluation value is changed so as to increase.
[0020]
[Action]
By changing the weight of each standard according to the history of the amount of motion detected from the input image, the occurrence of the “time sticking” pattern is prevented.
[0021]
【Example】
Hereinafter, an embodiment of a quantization apparatus according to the present invention will be described. The present invention detects the amount of motion, and appropriately controls the weights for the S / N evaluation value, the space fluctuation evaluation value, and / or the time fluctuation evaluation value. Therefore, the above-described image quality degradation is achieved by changing the weight of each criterion in the new quantization criterion Q2 of Expression (4) based on the history of motion amount from the past to the present (hereinafter referred to as motion history) in the video sequence. Preventing the occurrence of Hereinafter, several methods for detecting the amount of motion will be described.
[0022]
The first example of the motion amount detection method is means for determining the motion amount based on the integrated value of the time difference absolute values. Input signal value L at coordinates (x, y) of k frame_{x, y}  (K), the input signal value of the k−1 frame is L_{x, y}  Assuming (k-1), the time difference absolute value is defined by equation (12), and the pixel average time difference absolute value M of the target block is defined by equation (13). Of course, it is not always necessary to perform pixel averaging.
[0023]
ΔT_{x, y}= L_{x, y}(K) -L_{x, y}(K-1) (12)
[0024]
[Expression 1]

[0025]
A second example of the motion amount detection method is a motion amount determination method using the time difference absolute value normalized by the spatial inclination of each pixel. In general, the absolute value of the time difference at a portion with a large spatial variation (a portion with a large spatial inclination such as a contour portion) becomes large. On the other hand, the absolute value of the time difference in a portion with a small spatial variation (a portion with a small spatial inclination such as a flat portion) becomes small. Therefore, in order to calculate an appropriate amount of motion, the time difference absolute value normalized by the spatial inclination is used. Formulas (14) to (18) show examples of calculating the spatial inclination in the pixel arrangement diagram of FIG.
[0026]
ΔS = (| ΔS₁  | + | ΔS₂  | + | ΔS₃  | + | ΔS₄  |) / 4 (14)
However, | ΔS₁  |, | ΔS₂  |, | ΔS₃  |, | ΔS₄  | Is defined by the following equation and represents the spatial inclination in the oblique direction, the vertical direction, and the horizontal direction.
[0027]

[0028]
Thus, the spatial inclination ΔS at the calculated coordinates (x, y)_{x, y}The pixel average motion amount normalized by is obtained by Expression (19).
[0029]
[Expression 2]

[0030]
In addition, since the normalized value is small in a pixel having an extremely small spatial inclination, the sensitivity of the motion amount tends to be too sensitive. Therefore, the threshold processing of Expression (20) is introduced. That is,
ΔS_{x, y}<Threshold TH: ΔS_{x, y}= 1.0 (20)
(ΔS_{x, y}≧ Threshold value TH: ΔS_{x, y}Do not change. )
An appropriate amount of motion can be calculated by this threshold processing.
[0031]
A so-called motion vector can also be used as the third motion amount detection method. An example of this will be described below. In general, the motion vector detection methods are roughly classified into the following three types.
(1) Block matching method
(2) Gradient method
(3) Phase correlation method
[0032]
The block matching method has the same idea as pattern matching and compares the current image with the past image where the block area of the current image exists in the past image. As a specific example, the absolute value of the difference for each corresponding pixel in the block is added, and the position where the sum of absolute differences (or the sum of squared differences) for each block is minimized is used as the motion vector.
[0033]
An example of the structure of block data using the block matching method is shown in FIG. When detecting a motion vector between adjacent frames, a block (M pixels × N lines) is set at a spatially corresponding position. The search coordinate is shifted by k frames (current frame) and k-1 frame (previous frame), that is, (X + M) pixels in the horizontal direction and (Y + N) lines are shifted in the vertical direction, and pattern matching is performed at each coordinate position. A coordinate position where the sum of absolute differences (or the sum of squares of differences) is minimized is detected.
[0034]
Set the pixel level of the coordinates (i, j) of k frame to L_k(I, j), k−1 The pixel level of the coordinates (i, j) of the frame is set to L_k-1  Assuming that (i, j), equation (21) is given as an example of an evaluation equation at coordinates (x, y).
[0035]
[Equation 3]

[0036]
In the example of FIG. 5, the evaluation value E of the evaluation formula (Formula 21) for each coordinate in the search area is calculated. The number of search points is XY points. Among them, the coordinate (x, y) that minimizes the evaluation value E corresponds to the motion vector. The obtained motion vector is represented by v = (v_x, V_y), V_x= -X, v_y= −y. Although this method has a drawback that the calculation amount is enormous, it is widely used because of its high detection accuracy.
[0037]
The gradient method is based on a model in which when a pixel having a certain spatial gradient moves to a certain position, a time difference corresponding to the amount of movement is generated. Therefore, a motion vector can be obtained by dividing the time difference by the spatial gradient. The basic processing of the gradient method is as follows.
[0038]
Let g (x, y) be the pixel value at the coordinates (x, y). Let the motion vector be v = (v_x, V_y), The pixel value at the next time is g (x−v_x, Y-v_y) When this is developed by the Taylor, Equation (22) is obtained.
[0039]
[Expression 4]

[0040]
Here, the time difference is expressed by Expression (23).
d (x, y) = g (x−v_x, Y-v_y) -G (x, y) (23)
Thereby, Formula (24) is obtained.
v · gradg (x, y) to −d (x, y) (24)
From this equation (24), the motion vector can be obtained from the time difference and the spatial gradient.
[0041]
When the least square method is applied to the equation (24) and the motion vector v is solved for pixels in a certain block, equations (25) and (26) are obtained.
v_x=-(ΣΔ_tΔ_x) / (ΣΔ_x ²  (25)
v_y=-(ΣΔ_tΔ_y) / (ΣΔ_y ²  (26)
Δ_tIs the time difference, Δ_xIs the horizontal slope, Δ_yRepresents the vertical gradient.
[0042]
Further simplification yields equations (27) and (28).
v_x=-{ΣΔ_tsign (Δ_x)} / (Σ | Δ_x|) (27)
v_y=-{ΣΔ_tsign (Δ_y)} / (Σ | Δ_y|) (28)
sign () represents a sign.
[0043]
In general, equations (27) and (28) are used for detecting the amount of motion by the gradient method. Although the calculation amount of the gradient method is small, there is a drawback that the accuracy of the detected motion vector is lowered when the amount of motion is large. This is because the aforementioned model is no longer valid. However, in practice, accuracy is obtained by various devices such as sequentially detecting motion vectors repeatedly.
[0044]
Furthermore, the phase correlation method performs Fourier transform on block data at the same position in the current image and the past image, detects the amount of phase shift in the frequency domain, and uses the inverse free Fourier transform to convert the phase term into a motion vector. This is a method for detecting a value. As a feature of this method, a large block size of a certain level or more is required to ensure accuracy. Therefore, the amount of calculation becomes enormous by Fourier transform. In general, there are a plurality of movements in a large block, which makes it difficult to identify and determine the movement.
[0045]
Any one of the motion vector detection methods described above is applied, and the motion vector v = (v_x, V_y) Is detected. For the motion vector of the entire screen thus determined, for example, the motion vector norm M defined by Expression (29) is used as the motion amount.
[0046]
[Equation 5]

[0047]
Compared with the above-described method using the time difference absolute value, the amount of movement is high in accuracy although the burden on the circuit is large.
[0048]
In this embodiment, the amount of motion of the target pixel detected based on the above method is stored from the past to the present, and the reference weights of Equation (4) are controlled by the motion history, so that the quantum that matches the visual characteristics is stored. Is to execute. As an example of the normative weight characteristic, it is conceivable to increase the contribution ratio of the S / N normative weight α in a severe motion image. In a still area such as a background portion, the S / N standard weight α, the spatial variation standard weight β, and the time variation standard weight γ are also contributed at the start of a still image after a scene change or the like. Then, the contribution rate of the space variation norm weight β is lowered and the contribution rate of the time variation norm weight γ is increased. With this control, it is possible to stably hold a quantization pattern in which image quality degradation in space such as an edge business is eliminated in a static region such as a background portion.
[0049]
Examples of the characteristics of the S / N norm weight α, the spatial variation norm weight β, and the time variation norm weight γ determined based on the motion history by the above method are shown in FIGS. In the example of FIG. 2, the S / N standard weight α is always constant. Essentially, the S / N criterion is used to prevent a new paraquantization runaway. Since the contribution rate changes in relation to other reference weights, it is constant in this example. The example of FIG. 3 shows the characteristics of the spatial variation reference weight β. As described above, the spatial variation reference weight β attenuates the contribution rate by the stationary continuous time.
[0050]
This is to prevent the problem of propagation of the quantized value called “oblique flow phenomenon”. In the determination of Expression (4), a past determined quantized value is used, and if the contribution rate of the spatial variation criterion is too large, a unique “diagonal stripe pattern” depending on the determination order may occur. In order to prevent this image quality deterioration, the spatial variation reference weight β is controlled. FIG. 4 is a characteristic example of the time variation reference weight γ. In this example, as the stationary continuous time becomes longer, the weight γ is increased to suppress the temporal fluctuation of the quantized value, thereby realizing control with improved signal stability. Desired processing is realized by controlling the reference weight as described above.
[0051]
FIG. 6 shows a flowchart for determining the quantized value of the new paraquantizer that changes the weight of each criterion according to the motion history obtained by the above-described detection method. Basically, for all linear quantization values q (i) that can be generated with the set number of quantization bits n, a new category evaluation value defined by equation (4) is calculated and has the minimum value. Let the quantized value be the output value.
[0052]
In step 1, using the method as described above, a motion history of the target pixel is generated from the motion detection results of the detected target pixel from the past to the present. In the next step 2, the weights α, β, and γ of the target pixel are determined from the generated motion history. These weights are determined according to the characteristics shown in FIGS. 2, 3 and 4 respectively. In step 3, 0 is set to the counter q. The counter q corresponds to a quantized value candidate. In the next step 4, an evaluation value is calculated using Equation (4) for the quantized value candidate corresponding to q. The calculated new category evaluation value is registered.
[0053]
In the increment of step 5, ｀ +1 'is added to the counter q, and the control moves to step 6. In step 6 (q = N), it is determined in step 5 (increment) whether or not the added counter q is equal to N. If q ≠ N, the control proceeds to step 4 (calculation and registration of evaluation value). Returning, if q = N, control is transferred to step 7. That is, when the maximum value of the quantization value to be evaluated is (N−1), the number of times set in (N−1), the processes of step 4 and step 5 are repeated, and the counter q is set to N. When equal, the loop ends.
[0054]
Next, in the minimum value detection of the evaluation value in step 7, the quantization value q that generates the smallest new category evaluation value among the quantization value candidates is selected as the final result. In the quantization value q registration in step 8, the selected quantization value q is registered, and this flowchart ends.
[0055]
Next, FIG. 7 shows a block diagram of an embodiment for realizing the processing of the quantization apparatus of the present invention. An input signal value L (i) supplied from the input terminal 11, for example, a digital image signal in which each pixel is quantized to 8 bits, is supplied to the quantizer 12 and the new model processing unit 13. The new model processing unit 13 includes a motion amount detection unit 15, a motion history detection unit 16, a weight determination unit 17, a determination unit 18, and a memory unit 19, and an input signal value L (i) supplied from the input terminal 11. Is supplied to the motion amount detection unit 15, the determination unit 18, and the memory unit 19. In the quantizer 12, the supplied input signal value L (i) is quantized to n bits smaller than 8 bits. From this quantizer 12, 2ⁿThe number of quantization value candidates is generated.
[0056]
The linear quantized value q (i) generated by the quantizer 12 is supplied to the determination unit 18 as d3. In addition, upper and lower quantized values of the linear quantized value are also generated and supplied to the determination unit 18. The motion determination flag d0 of the target pixel detected by the motion amount detection unit 15 is supplied to the motion history detection unit 16, and the motion history detection unit 16 generates a motion history. The motion history is supplied to the weight determining unit 17 as d1, and the weight determining unit 17 determines the weight of the target pixel in response to the motion history d1. The weights α, β, γ from the weight determination unit 17 are supplied to the determination unit 18 as d2.
[0057]
In the new normative quantization, since the new category Q2 defined by the equation (4) is used, it is necessary to store the input signal value L (i) and the determined quantized value d4. From the memory unit 19, the storage data d4 (that is, the determined quantized value) is supplied to the determination unit 18 as necessary. In the determination part 18, the process of the flowchart shown in FIG. 6 mentioned above is performed. That is, after the determination of Expression (4) is executed from the supplied input signal value L (i), weight d2, linear quantized value d3, and stored data d4, the final quantized value q (i) is selected. And is taken out via the output terminal 14.
[0058]
Here, an example of the above-described motion amount detection unit 15 is shown in FIG. In this embodiment, a local motion amount, that is, a motion amount for each pixel is detected. An input signal value L (i) is supplied from the input terminal 11, and the input signal value L (i) is supplied to the subtractor 21 and the memory unit 22. In the subtractor 21, for example, a time difference value i <b> 2 for each pixel is generated between the input signal value L (i) and the output value i <b> 1 of the memory unit 22, which is past data, and is supplied to the ROM 23. As an example of the processing of the ROM 23, an absolute value of the time difference value i2 and a stillness determination are performed. As a result, the output d0 becomes a motion determination flag of a local motion amount. For example, the local motion amount is stationary when the motion determination flag d0 is ｀ 0 ', and is motion when 動 き 1'. This motion determination flag d0 is supplied from the motion amount detector 15 to the motion history detector 16 via the output terminal 24.
[0059]
Here, an example of the motion history detector 16 is shown in the block diagram of FIG. The motion determination flag d0 accesses the corresponding address of the motion history memory having an independent address for each pixel. If the motion determination flag d0 is still, the past number of still images stored in the motion history memory is incremented by +1 by the ROM 31, and the value is written to write the contents of the motion history memory 32 at that address. Update. The process is a loop from the output d1 of the motion history memory 32 to the ROM 31 shown in FIG. When the motion determination flag d0 is not stationary, 0 is generated as i6 by the ROM 31, and the content of the motion history memory 32 is cleared by writing the value. With such processing, it is possible to generate a motion history from the past to the present of the target range. The motion history in this example corresponds to a stationary continuous time from the past to the present of the target range. As an output of the motion history detection unit 16, the motion history d 1 is transmitted to the weight determination unit 17.
[0060]
An example of the weight determination unit 17 is shown in the block diagram of FIG. The motion history d1 is supplied from the motion history detection unit 16 via the input terminal 33, and the weights of the respective norms of characteristics as shown in FIGS. 2, 3 and 4 are determined based on the motion history d1. The motion history d1 is supplied to the ROMs 41, 42, and 43. The ROM 41 stores the characteristics of the S / N standard weight α, and the weight α is d2 in accordance with the motion history d1, and the determination unit 18 is set via the output terminal 44. Supplied to. The ROM 42 stores the characteristics of the spatial variation norm weight β, and the weight β is set to d2 according to the motion history d1 and supplied to the determination unit 18 via the output terminal 45. Similarly, the ROM 43 stores the characteristics of the spatial variation reference weight γ, and the weight γ is set to d2 according to the motion history d1 and is supplied to the determination unit 18 via the output terminal 46. That is, the weights α, β, and γ corresponding to the motion history d1 are supplied to the next determination unit 18 as d2.
[0061]
Next, an example of the determination unit 18 is shown in the block diagram of FIG. First, the new category evaluation unit 53 detects a quantized value that minimizes the new category evaluation value of Equation (4) described above. Although detection by hard wire is possible, detection by calculation using a CPU or the like is also executed. For this detection, various signals required for Equation (4) are required. Therefore, the input signal value L (i) from the input terminal 11 and the linear quantized value q (i) from the quantizer 12 via the input terminal 52 are set as d3 and stored from the memory unit 19 via the input terminal 51. The reference weights α, β, and γ are supplied to the new category evaluation unit 53 from the data (ie, the determined quantized value) d4 and the input terminals 44, 45, and 46 as d2.
[0062]
According to the equation (4) used in the new normative evaluation unit 53, the memory unit 19 needs to store the input signal value L (i) and the determined quantized value q (i) as described above. The necessary signal is also supplied as the stored data d4 from the memory unit 19 in accordance with the processing in the new model evaluation unit 53. The selection code i11 of the new category minimized quantization value detected by the new norm evaluation unit 53 is supplied to the selector 57. Since the linear quantized value d3 from the quantizer 12 is also transmitted with the linear quantized value and the upper and lower quantized values, the three quantized values are stored in the registers 54, 55, and 56 as final quantized value candidates. Retained. These quantized values i12, i13, i14 are input to the selector 57, an appropriate value is selected by the above-described selection code i11, and as the determined quantized value q (i) of the determination unit 18, the output terminal 14 Taken from.
[0063]
Here, another example of the above-described motion amount detection unit 15 is shown in FIG. In this example, a global motion amount, that is, a motion amount for each block or one screen is detected. The input signal value L (i) is supplied from the input terminal 11, and the input signal value L (i) is supplied to the subtractor 61 and the memory unit 62. In the memory unit 62, for example, a delay of one frame is made with respect to the input signal value L (i), and the output i21 and the input signal value L (i) are shown in the subtractor 61 by the above equation (12). A time difference i22 for each pixel is generated. Time difference i22Is converted into appropriate data i 23 by the ROM 63 and supplied to the adder 64. As an example, the time difference i22 is converted into an absolute value of the difference value in the ROM 63 and output as data i23.
[0064]
The adder 64 adds the data i23 and the integrated value i25 supplied from the register 65, and the value i24 is supplied to the register 65. In the register 65, the integrated value i25 is supplied to the ROM 66 and the adder 64, and the absolute value difference is integrated for each block. The absolute value difference integrated value output from the register 65 is not limited to one block, and an absolute value difference integrated value for one screen can be output. As an example of the processing of the ROM 66, stillness determination of the integrated value i25 is performed, and as a result, the output d0 becomes a motion determination flag for the target range. For example, the global motion amount is stationary when the motion determination flag d0 is ｀ 0 ', and is motion when 動 き 1'. The motion determination flag d0 is supplied from the motion amount detector 15 to the motion history detector 16 via the output terminal 67.
[0065]
With this novel paraquantization apparatus, it is possible to reduce the image quality degradation of the conventional quantization apparatus and obtain a quantized image that matches the visual characteristics.
[0066]
Unlike the case where all quantized values are generated in the quantizer 12 in FIG. 7, a reference quantized value selected based on the S / N criterion is generated, and this quantized value and its upper and lower With respect to the three types of quantized value candidates of the quantized value, the determination unit 18 obtains a new category evaluation value, and selectively selects a quantized value candidate corresponding to the smallest one of the three new category evaluation values. It may be configured to output.
[0067]
In the present invention, the new category evaluation value may be configured using only one of the space fluctuation evaluation value and the time fluctuation evaluation value. For example, in the case of a digital audio signal, an evaluation value using an S / N evaluation value and a time fluctuation evaluation value can be used.
[0068]
【The invention's effect】
According to the present invention, by performing quantization in consideration of visual characteristics, it is possible to reduce image quality degradation in the spatial direction or the temporal direction due to the expansion of the change in the input signal value near the quantization boundary level. .
[0069]
Further, according to the present invention, when the amount of motion is large, the weight γ can be made appropriate by reducing the weight γ with respect to the time fluctuation evaluation value, and image quality degradation as described at the beginning can be achieved. It becomes possible to prevent.
[0070]
Furthermore, according to the present invention, a good image can be obtained even with a smaller number of bits than the original number of quantization bits, for example, compression of a digital input signal can be obtained well.
[Brief description of the drawings]
FIG. 1 is a schematic diagram showing an example of screen division for motion amount detection according to the present invention.
FIG. 2 is a schematic diagram illustrating an example of a change characteristic according to a motion amount of a weight with respect to a time fluctuation evaluation value.
FIG. 3 is a schematic diagram illustrating a characteristic example of a change according to a motion amount of a weight with respect to a space fluctuation evaluation value.
FIG. 4 is a schematic diagram illustrating a characteristic example of a change according to a motion amount of a weight with respect to an S / N evaluation value.
FIG. 5 is a schematic diagram for explaining an example of a motion vector detection method;
FIG. 6 is a flowchart showing an example of a determination unit of the new variable quantizer with variable weight according to the present invention.
FIG. 7 is a block diagram showing an embodiment of a novel paraquantization apparatus according to the present invention.
FIG. 8 is a block diagram showing an example of a motion amount detection unit according to the present invention.
FIG. 9 is a block diagram showing an example of a motion history detection unit according to the present invention.
FIG. 10 is a block diagram showing an example of a weight determination unit according to the present invention.
FIG. 11 is a block diagram illustrating an example of a determination unit according to the present invention.
FIG. 12 is a block diagram showing another example of the motion amount detection unit according to the present invention.
FIG. 13 is a schematic diagram illustrating an example in which 3-bit quantization is performed on an input signal;
FIG. 14 is a block diagram illustrating an example of a pixel arrangement.
[Explanation of symbols]
12 Quantizer
13 New Code Processing Department
15 Motion detection unit
16 Motion history detector
17 Weight determination unit
18 Judgment part
19 Memory section

Claims (20)

Given is supplied an input signal value of the number of quantization bits, a quantization means for outputting quantized values fewer bits than the number of quantization bits,
A motion amount detecting means for detecting a motion amount for each pixel from the input signal value and outputting a determination flag;
When the determination flag is stationary, motion history detecting means for counting the past number of stationary continuations and outputting the counted number of stationary continuations as a history of motion amount;
A weight determining means for determining a weight according to the history of the motion amount;
Based on the input signal value, the quantization value output from the quantization means, and the weight supplied from the weight determination means, a plurality of quantization candidates that can be generated with a predetermined number of quantization bits are calculated. For the plurality of calculated quantization candidates, the quantized value candidate is decoded , an S / N evaluation value that is a difference between the decoded quantized value candidate and the input signal value, and the input signal value spatial variation and spatial variation evaluation value and the time change and time change evaluation is the difference in time variation of the decoded values of the quantization value candidates of said input signal value which is the difference of the spatial variation of the decoded values of the quantization value candidates A determination means for respectively obtaining an evaluation value obtained by weighted addition of the value, and selectively outputting the quantization candidate that minimizes the evaluation value as a quantization value;
In the determination means, the weight for the S / N evaluation value is constant and the weight for the spatial variation evaluation value is attenuated with respect to an increase in the number of still continuous movement amount history for each pixel obtained from the input signal , quantizer which is characterized in that so as to vary as weight increase with respect to the time change evaluation value. The quantization device according to claim 1,
In the determination means,
The weight for the S / N evaluation value is constant and the weight for the spatial variation evaluation value is attenuated with respect to an increase in the number of stationary continuations of the motion amount history for each pixel detected based on the time difference amount of the input signal. , quantization and wherein the varied to weight increase with respect to the time change evaluation value. The quantization device according to claim 1,
In the determination means,
The S / N evaluation value has a constant weight with respect to the increase in the number of stationary continuations of the motion amount history for each pixel detected based on the time difference amount of the input signal normalized by the spatial gradient, and the spatial variation A quantization apparatus characterized in that a weight for an evaluation value is attenuated and a weight for the time variation evaluation value is increased . The quantization device according to claim 1,
In the determination means,
The above S / N evaluation value with respect to the increase in the number of stationary continuations of the motion amount history for each pixel detected based on the time difference amount of the input signal normalized by the spatial gradient through the threshold processing The quantization apparatus is characterized in that the weight for the space fluctuation evaluation value is constant, the weight for the space fluctuation evaluation value is attenuated, and the weight for the time fluctuation evaluation value is increased . The quantization device according to claim 1,
In the determination means,
The weight for the S / N evaluation value is constant and the weight for the spatial variation evaluation value is increased with respect to the increase in the number of stationary continuations of the motion amount history detected for each pixel based on the motion vector detected for each pixel. Is quantized, and the weight is changed so as to increase the weight for the time fluctuation evaluation value. The quantization device according to claim 1,
In the determination means,
The quantization apparatus characterized in that the space fluctuation evaluation value and the time fluctuation evaluation value are obtained using an input signal value and a decoded value of the quantization value output from the determination means . The quantization device according to claim 1,
A quantization apparatus, wherein an input signal is a digital video signal, and a spatial fluctuation evaluation value is obtained using an input signal value and a decoded value in the same field or the same frame. The quantization device according to claim 1,
A quantization apparatus, wherein an input signal is a digital video signal, and a time fluctuation evaluation value is obtained by using an input signal value and a decoded value of a current frame, an input signal value and a decoded value of a previous frame. The quantization apparatus according to claim 1,
The movement history detection means includes
If the judgment flag is not stationary, clear the number of stationary continuations being counted
A quantizer characterized by that. Given is supplied an input signal value of the number of quantization bits, a quantization means for outputting quantized values fewer bits than the number of quantization bits,
A motion amount detection means for detecting a motion amount for each block or screen from the input signal value and outputting a determination flag;
When the determination flag is stationary, motion history detecting means for counting the past number of stationary continuations and outputting the counted number of stationary continuations as a history of motion amount;
A weight determining means for determining a weight according to the history of the motion amount;
Based on the input signal value, the quantization value output from the quantization means, and the weight supplied from the weight determination means, a plurality of quantization candidates that can be generated with a predetermined number of quantization bits are calculated. For the plurality of calculated quantization candidates, the quantized value candidate is decoded , an S / N evaluation value that is a difference between the decoded quantized value candidate and the input signal value, and the input signal value spatial variation and spatial variation evaluation value and the time change and time change evaluation is the difference in time variation of the decoded values of the quantization value candidates of said input signal value which is the difference of the spatial variation of the decoded values of the quantization value candidates A determination means for respectively obtaining an evaluation value obtained by weighted addition of the value, and selectively outputting the quantization candidate that minimizes the evaluation value as a quantization value;
Above determination means, with respect to the stationary continuous increase in the number of movements of the history of block or each screen obtained from the input signal, the weight for the S / N evaluation value was constant, the weights for the spatial change evaluation value attenuated, quantizer which is characterized in that as weight is changed so as to increase relative to the time change evaluation value. The quantization apparatus according to claim 10, wherein
In the determination means,
The weight for the S / N evaluation value is constant and the weight for the spatial fluctuation evaluation value with respect to the increase in the number of stationary continuations of the movement amount history for each block or screen detected based on the time difference amount of the input signal. Is quantized, and the weight is changed so as to increase the weight for the time fluctuation evaluation value. The quantization apparatus according to claim 10, wherein
In the determination means,
The weight for the S / N evaluation value is constant with respect to the increase in the number of stationary continuations of the motion amount history for each block or screen detected based on the time difference amount of the input signal normalized by the spatial gradient, A quantization apparatus , wherein a weight for the space fluctuation evaluation value is attenuated and a weight for the time fluctuation evaluation value is increased . The quantization apparatus according to claim 10, wherein
In the determination means,
With respect to the increase in the number of stationary continuations of the motion amount history for each block or each screen detected based on the time difference amount of the input signal normalized by the spatial gradient through the threshold processing , the above S / A quantization apparatus characterized in that a weight for an N evaluation value is constant, a weight for the space fluctuation evaluation value is attenuated, and a weight for the time fluctuation evaluation value is increased . The quantization apparatus according to claim 10, wherein
In the determination means,
The weight for the S / N evaluation value is constant with respect to the increase in the number of stationary continuations of the motion amount history for each block or screen detected based on the motion vector detected for each block or screen. A quantization apparatus characterized in that a weight for a space fluctuation evaluation value is attenuated and a weight for the time fluctuation evaluation value is increased . The quantization apparatus according to claim 10, wherein
In the determination means,
The quantization apparatus characterized in that the space fluctuation evaluation value and the time fluctuation evaluation value are obtained using an input signal value and a decoded value of the quantization value output from the determination means . The quantization apparatus according to claim 10, wherein
A quantization apparatus, wherein an input signal is a digital video signal, and a spatial fluctuation evaluation value is obtained using an input signal value and a decoded value in the same field or the same frame. The quantization apparatus according to claim 10, wherein
A quantization apparatus, wherein an input signal is a digital video signal, and a time fluctuation evaluation value is obtained by using an input signal value and a decoded value of a current frame, an input signal value and a decoded value of a previous frame. The quantization apparatus according to claim 10, wherein
The movement history detection means includes
If the determination flag is not stationary, clear the number of stationary continuations being counted
A quantizer characterized by that. Given is supplied an input signal value of the number of quantization bits, a quantization step of outputting the quantized value of fewer bits than the number of quantization bits,
A motion amount detection step of detecting a motion amount for each pixel from the input signal value and outputting a determination flag;
When the determination flag is stationary, a motion history detection step that counts the number of past stationary continuations and outputs the counted number of stationary continuations as a history of motion amount; and
A weight determination step for determining a weight according to the history of the amount of movement;
Based on the input signal value, the quantization value output from the quantization step, and the weight supplied from the weight determination step, a plurality of quantization candidates that can be generated with a predetermined number of quantization bits are calculated. For the plurality of calculated quantization candidates, the quantized value candidate is decoded , an S / N evaluation value that is a difference between the decoded quantized value candidate and the input signal value, and the input signal value spatial variation and spatial variation evaluation value and the time change and time change evaluation is the difference in time variation of the decoded values of the quantization value candidates of said input signal value which is the difference of the spatial variation of the decoded values of the quantization value candidates A determination step of obtaining an evaluation value obtained by weighted addition of the value and selectively outputting the quantization candidate that minimizes the evaluation value as a quantization value,
In the determination step, the weight for the S / N evaluation value is constant and the weight for the spatial variation evaluation value is attenuated with respect to an increase in the number of stationary continuations of the motion amount history for each pixel obtained from the input signal. A quantization method characterized in that the weight for the time variation evaluation value is changed to be increased. A quantization step of supplying an input signal value having a predetermined number of quantization bits and outputting a quantization value having a number of bits smaller than the number of quantization bits;
A motion amount detection step for detecting a motion amount for each block or screen from the input signal value and outputting a determination flag;
When the determination flag is stationary, a motion history detection step that counts the number of past stationary continuations and outputs the counted number of stationary continuations as a history of motion amount; and
A weight determination step for determining a weight according to the history of the amount of movement;
Based on the input signal value, the quantization value output from the quantization step, and the weight supplied from the weight determination step, a plurality of quantization candidates that can be generated with a predetermined number of quantization bits are calculated. For the plurality of calculated quantization candidates, the quantized value candidate is decoded, an S / N evaluation value that is a difference between the decoded quantized value candidate and the input signal value, and the input signal value Variation evaluation value which is the difference between the spatial variation of the quantized value candidate and the decoded value of the quantized value candidate, and the temporal variation evaluation which is the difference of the temporal variation of the input signal value and the decoded value of the quantized value candidate A determination step of obtaining an evaluation value obtained by weighted addition of the value and selectively outputting the quantization candidate that minimizes the evaluation value as a quantization value,
In the determination step, the weight for the S / N evaluation value is constant and the weight for the spatial variation evaluation value is constant with respect to the increase in the number of still continuous motion amount histories for each block or screen obtained from the input signal. A quantization method, wherein the quantization method is attenuated and the weight for the time fluctuation evaluation value is increased.

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