Data processing apparatus

A data processing method for processing coded data including decoding information used for decoding in predetermined units, said data processing method comprising:

• storing predetermined tap coefficients determined in advance by a learning process on a learning signal for each class of data of interest;
• generating a prediction tap and a class tap based upon (a) extracting decoded data in a predetermined positional relationship with data of interest within the decoded data such that said coded data is decoded and (b) extracting decoding information in predetermined units according to the position of said data of interest in a unit which contains said data of interest;
• classifying said data of interest and said decoding information of said predetermined units on the basis of (a) said class tap and (b) the position of said data of interest in said unit, and outputting class code as a result of thereof;
• performing a predetermined prediction computation using (a) said tap coefficient corresponding to the class obtained as a result of the classification and (b) said prediction tap;
• and
• determining a prediction value corresponding to the decoded data,
• wherein the number of classes, corresponding to each decoding information of said predetermined units, are determined based on the position of said data of interest in said unit.

A data processing apparatus for learning a predetermined tap coefficient used to process coded data including decoding information used for decoding in predetermined units, said data processing apparatus comprising:

• student data generation means for generating decoded data as student data serving as a student by coding teacher serving as a teacher into said coded data having decoding information in predetermined units and by decoding the coded data;
• prediction tap generation means for generating a prediction tap used to predict teacher data by extracting said decoded data in a predetermined positional relationship with subject data of interest within said decoded data as the student data and by extracting said decoding information in said predetermined units according to a position of said subject data in said predetermined units;
• memory means for storing predetermined tap coefficients determined in advance by learning;
• learning means for learning so that a prediction error of the prediction value of said teacher data obtained by performing a predetermined prediction computation by using said prediction tap and said stored tap coefficient statistically becomes a minimum, and for determining said tap coefficient;
• class tap generation means for generating a class tap used for classification for classifying said subject data by extracting said decoded data in a predetermined positional relationship with said subject data and by extracting said decoding information in predetermined units according to a position of said subject data in said predetermined unit;
• and
• classification means for performing classification on said subject data on the basis of said class tap,
• wherein said learning means determines said tap coefficient for each class obtained as a result of classification by said classification means and the number of classes, corresponding to each decoding information, are determined based on the position of said subject data in a unit which contains said subject data.

A data processing method for learning a predetermined tap coefficient used to process coded data including decoding information used for decoding in predetermined units, said data processing method comprising:

• a student data generation step of generating decoded data as student data serving as a student by coding teacher serving as a teacher into coded data having said decoding information in predetermined units and by decoding the coded data;
• a prediction tap generation step of generating a prediction tap used to predict teacher data by extracting said decoded data in a predetermined positional relationship with subject data of interest within said decoded data as the student data and by extracting said decoding information in said predetermined units according to a position of said subject data in said predetermined units;
• storing predetermined tap coefficients determined in advance by learning;
• a learning step of learning so that a prediction error of the prediction value of said teacher data obtained by performing a predetermined prediction computation by using said prediction tap and said stored tap coefficient statistically becomes a minimum, and for determining said tap coefficient;
• class tap generation step for generating a class tap used for classification for classifying said subject data by extracting said decoded data in a predetermined positional relationship with said subject data and by extracting said decoding information in predetermined units according to a position of said subject data in said predetermined unit;
• and
• classification step for performing classification on said subject data on the basis of said class tap,
• wherein said learning step determines said tap coefficient for each class obtained as a result of classification by said classification step and the number of classes, corresponding to each decoding information, are determined based on the position of said subject data in a unit which contains said subject data.