Data processing apparatus and data processing method

• US 20040174930A1
• Filed: 03/18/2004
• Published: 09/09/2004
• Est. Priority Date: 11/09/1998
• Status: Active Grant

• Current Assignee: Koji Ohta, Naoki Kobayashi, Tetsujiro Kondo
• Original Assignee: Koji Ohta, Naoki Kobayashi, Tetsujiro Kondo
• Inventors: Kondo, Tetsujiro, Kobayashi, Naoki, Ohta, Koji
• Granted Patent: US 7,088,768 B2
• Time in Patent Office: Days
• US Class Current: 375/224
• CPC Class Codes: H03H 21/0012 Digital adaptive filters
• CPC Class Codes: H04N 5/21 Circuitry for suppressing or minimising disturbance, e.g. moiré or halo

Abstract

A data processing apparatus processes input data and outputs the processed data. The data processing apparatus includes a data processing section for processing the input data by a predetermined processing method and outputting the processed data, an input-data evaluation section for evaluating the input data, an output-data evaluation section for evaluating the output data, and a real-time learning section for controlling such that the processing method is learned in real time according to the evaluation results obtained by the input-data evaluation section and the output-data evaluation section and the data processing section processes the input data according to the learned processing method, and thereby, improves the output data every moment.

Claims

1. A data processing apparatus for processing input data and outputting the processed data, comprising:

data processing means for processing the input data by a predetermined processing method and outputting the processed data;

input-data evaluation means for evaluating the input data;

output-data evaluation means for evaluating the output data, and

real-time learning means for controlling such that the processing method is learned in real time according to the evaluation results obtained by said input-data evaluation means and said output-data evaluation means and said data processing means processes the input data according to the learned processing method.

2. A data processing apparatus according to claim 1, further comprising input-data storage means for storing a predetermined number of time-sequentially input data.

3. A data processing apparatus according to claim 2, wherein said input-data evaluation means calculates the dispersion of the predetermined number of the input data stored by said input-data storage means and evaluates the current input data according to the dispersion.

4. A data processing apparatus according to claim 2, wherein said input-data evaluation means calculates the average of the predetermined number of the input data stored by said input-data storage means, and evaluates the current input data according to the error of each data against the average.

5. A data processing apparatus according to claim 2, wherein said input-data evaluation means calculates the dispersion and the average of the predetermined number of the input data stored by said input-data storage means;

obtains the error of each input data against the average; and

evaluates the current input data according to the dispersion and the error.

6. A data processing apparatus according to claim 1, wherein said output-data evaluation means evaluates the current output data according to the sum of the evaluation of the input data and the evaluation of the output data.

7. A data processing apparatus according to claim 1, further comprising output-data storage means for storing the output data,

wherein said data processing means adds the previous output data stored by said output-data storage means and the current input data to obtain the output data corresponding to the current input data.

8. A data processing apparatus according to claim 7, wherein said real-time learning means learns a predetermined weight coefficient according to the evaluation of the input data and the evaluation of the output data, and

said data processing means obtains the output data corresponding to the current input data according to the weight coefficient.

9. A data processing method for processing input data and outputting the processed data, comprising:

a data processing step for processing the input data by a predetermined processing method and outputting the processed data;

an input-data evaluation step for evaluating the input data;

an output-data evaluation step for evaluating the output data, and

a real-time learning step for controlling such that the processing method is learned in real time according to the evaluation results obtained in said input-data evaluation step and said output-data evaluation step and the input data is processed in said data processing step by the learned processing method.

10. A data processing method according to claim 9, further comprising an input-data storage step for storing a predetermined number of time-sequentially input data.

11. A data processing method according to claim 10, wherein, in said input-data evaluation step, the dispersion of the predetermined number of the input data stored in said input-data storage step is calculated and the current input data is evaluated according to the dispersion.

12. A data processing method according to claim 10, wherein, in said input-data evaluation step, the average of the predetermined number of the input data stored in said input-data storage step is calculated, and the current input data is evaluated according to the error of each data against the average.

13. A data processing method according to claim 10, wherein, in said input-data evaluation step, the dispersion and the average of the predetermined number of the input data stored in said input-data storage step are calculated;

the error of each input data against the average is obtained; and

the current input data is evaluated according to the dispersion and the error.

14. A data processing method according to claim 9, wherein, in said output-data evaluation step, the current output-data is evaluated according to the sum of the evaluation of the input data and the evaluation of the output data.

15. A data processing method according to claim 9, further comprising an output-data storage step for storing the output data,

wherein, in said data processing step, the previous output data stored in said output-data storage step and the current input data are added to obtain the output data corresponding to the current input data.

16. A data processing method according to claim 15, wherein a predetermined weight coefficient is learned according to the evaluation of the input data and the evaluation of the output data in said real-time learning step, and

the output data corresponding to the current input data is obtained according to the weight coefficient in said data processing step.

17. A storage medium storing a computer-controllable program for processing input data and outputting the processed data, the program comprising:

a data processing step for processing the input data by a predetermined processing method and outputting the processed data;

an input-data evaluation step for evaluating the input data;

an output-data evaluation step for evaluating the output data, and

a real-time learning step for controlling such that the processing method is learned in real time according to the evaluation results obtained in said input-data evaluation step and said output-data evaluation step and the input data is processed in said data processing step by the learned processing method.