Data processing apparatus and data processing method

• US 20060126930A1
• Filed: 02/07/2006
• Published: 06/15/2006
• Est. Priority Date: 11/09/1998
• Status: Active Grant

• Current Assignee: Tetsujiro Kondo
• Original Assignee: Tetsujiro Kondo
• Inventors: Kondo, Tetsujiro
• Granted Patent: US 7,747,069 B2
• Time in Patent Office: Days
• US Class Current: 382/155
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 5/002 Denoising; Smoothing noise processing or correction adapted to be used in an image pickup device containing and electronic image sensor H04N23/81, H04N25/60 - H04N25/67
• CPC Class Codes: G06T 5/20 using local operators
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: H04N 5/21 Circuitry for suppressing or minimising disturbance, e.g. moiré or halo
• CPC Class Codes: H04N 5/213 Circuitry for suppressing or minimising impulsive noise for suppressing or minimising disturbance in image signal generation H04N23/81
• CPC Class Codes: H04N 5/46 for receiving on more than one standard at will deflecting circuits of multi-standard receivers H04N3/27
• CPC Class Codes: H04N 7/0135 involving interpolation processes interpolation-based image scaling G06T3/4007; interpolation for video coding H04N19/587, H04N19/59
• CPC Class Codes: H04N 7/0145 the interpolation being class adaptive, i.e. it uses the information of class which is determined for a pixel based upon certain characteristics of the neighbouring pixels

Abstract

A data processing apparatus processes input data and outputs the processed data. The data processing apparatus includes a data processing section and a real-time learning section. The data processing section processes the input data by a predetermined processing method and outputs the processed data. The real-time learning section controls such that the processing method is learned in real time and the data processing section processes the input data by the learned processing method, so that the output data is improved as time elapses.

Claims

1-14. -14. (canceled)

15. Data processing apparatus for processing input data and outputting the processed data, comprising:

a data processor for processing the input data by a predetermined processing method and outputting the processed data as output data;

an evaluator for evaluating a reliability of the output data; and

a real-time learning unit for controlling learning of the processing method, such that the processing method is learned in real time and said data processor processes the input data by the learned processing method, such that the output data improves over time,

wherein the processing method is learned by the real-time learning unit as a function of the reliability of the output data, and the processing method is learned without repeatedly determining the output data value for a given point in time.

16. The data processing apparatus of claim 15, wherein said real-time learning unit learns the processing method according to the input data and the output data.

17. The data processing apparatus of claim 15, wherein said real-time learning unit learns the processing method according to time-sequentially input learning data.

18. The data processing apparatus of claim 17, wherein said real-time learning unit learns the processing method according to data having a value within a predetermined range from the current value among the time-sequentially input learning data.

19. The data processing apparatus of claim 15, further comprising:

a classifier for classifying normal quality data according to a plurality of classes;

a formulator for formulating equations for each class; and

an output unit for solving said equations and outputting prediction coefficients for each class.

20. The data processing apparatus of claim 17, wherein said real-time learning unit learns the processing method while said real-time learning unit ignores past learning data at a predetermined rate.

21. A data processing method for processing input data and outputting the processed data, comprising the steps of:

processing the input data by a predetermined processing method and outputting the processed data as output data;

evaluating a reliability of the output data; and

learning the processing method in real time such that as the input data is processed by the learned processing method, the output data is improved over time,

wherein the processing method is learned as a function of the reliability of the output data, and the processing method is learned without repeatedly determining the output data value for a given point in time.

22. Data processing apparatus for processing input data and outputting the processed data, comprising:

a processor for processing the input data by a linear coupling of the input data and predetermined prediction coefficients and outputting the processed data; and

a real-time learning unit for learning the prediction coefficients in real time so that the output data has higher quality as time elapses and for transferring the prediction coefficients to said processor,

wherein the real-time learning unit includes a formulator for formulating equations to obtain the prediction coefficients based on at least data of higher quality than the input data, and an output unit for solving said equations and outputting the prediction coefficients;

whereby the prediction coefficients are learned without repeatedly determining the output data value for a given point in time.

23. The data processing apparatus of claim 22, wherein said real-time learning unit learns the prediction coefficients according to time-sequentially input learning data.

24. The data processing apparatus of claim 23, wherein said real-time learning unit learns the prediction coefficients while ignoring past learning data at a predetermined rate.

25. The data processing apparatus of claim 22, wherein said formulator formulates the equations based on data of higher quality than the input data and on normal quality data generated by the higher quality data.

26. A data processing method for processing input data and outputting the processed data, comprising the steps of:

processing the input data by a linear coupling of the input data and predetermined prediction coefficients and outputting the processed data; and

learning the prediction coefficients in real time so that the output data has higher quality as time elapses, including the steps of;

formulating equations to obtain the prediction coefficients based on at least higher quality data than the input data, solving said equations, and outputting the prediction coefficients;

whereby the prediction coefficients are learned without repeatedly determining the output data value for a given point in time.

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

processing the input data by a predetermined processing method and outputting the processed data as output data;

evaluating a reliability of the output data; and

learning the processing method in real time so that the output data produced by processing the input data is improved over time,

wherein in said processing method is learned as a function of the reliability of the output data, and whereby the processing method is learned without repeatedly determining the output data value for a given point in time.

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

processing the input data by a linear coupling of the input data and predetermined prediction coefficients and outputting the processed data; and

learning the prediction coefficients in real time so that the output data has higher quality as time elapses, including the steps of;

formulating equations to obtain the prediction coefficients based on at least higher quality data than the input data, solving said equations, and outputting the prediction coefficients;

whereby the prediction coefficients are learned without repeatedly determining the output data value for a given point in time.