Method and apparatus for color processing with neural networks

• US 5,200,816 A
• Filed: 07/19/1991
• Issued: 04/06/1993
• Est. Priority Date: 06/25/1991
• Status: Expired due to Fees

• Current Assignee: Scitex Corporation Ltd. (Suny Cellular Communication Ltd.)
• Original Assignee: Scitex Corporation Ltd. (Suny Cellular Communication Ltd.)
• Inventors: Rose, Oded M.
• Primary Examiner(s): BRINICH, STEPHEN M
• Application Number:
  US07/734,310
• Time in Patent Office: 627 Days
• Field of Search: 358/75-80, 358/433
• US Class Current: 358/518
• CPC Class Codes: H04N 1/6019 using look-up tables H04N1/6025 takes precedence
• CPC Class Codes: H04N 1/6033 using test pattern analysis H04N1/6055 takes precedence

Abstract

A method and apparatus for constructing, training and utilizing an artificial neural network (also termed herein a "neural network", an ANN, or an NN) in order to transform a first color value in a first color coordinate system into a second color value in a second color coordinate system.

Claims

1. A method for providing a neural network including the steps of:

providing a neural network structure including a plurality of neurons for receiving a first color value from among a first set of color values which first color value is to be transformed into a corresponding second color value from among a second set of color values and for providing an output indication of the corresponding second color value; and

training the neural network structure on a plurality of ordered pairs, each ordered pair comprising a first color value from among the first set of color values and a corresponding second color value from among the second set of color values.

2. A method according to claim 1 wherein one or both of the plurality of first color values and the plurality of second color values is a systematic representation of the respectively corresponding one of the first and second sets of color values.

3. A method according to claim 2 wherein one of the first and second sets of color values is defined within a color coordinate system and wherein each region of a predetermined size within the order coordinate system is represented by at least one color value within the plurality of color values corresponding to the set of color values defined within the color coordinate system.

4. A method according to claim 2 wherein one of the first and second sets of color values is defined within a color coordinate system which may be partitioned into regions and wherein each region within the partition is represented by at least one color value within the plurality of color values corresponding to the set of color values defined within the color coordinate system.

5. A method according to claim 1 and wherein said first set of color values is identical to the second set of color values.

6. A method according to claim 1 and wherein each second color value, represented in visual form, is substantially identical in appearance to the corresponding first color value, represented in visual form.

7. A method according to claim 1 and wherein said plurality of neurons comprises:

an input layer of neurons comprising at least one input neuron;

at least one hidden layer of neurons each comprising at least one hidden neuron; and

an output layer of neurons comprising at least one output neuron.

8. A method according to claim 7 wherein the first color value is also to be transformed into a corresponding third color value from among a third set of color values, the neural network structure is operative to provide an output indication of the corresponding second and third color values, the output layer of neurons comprises at least a first plurality of output neurons corresponding to the dimension of the second color value and a second plurality of output neurons corresponding to the dimension of a third color value and each ordered pair comprises a first color value and a corresponding concatenation of a second color value and a third color value.

9. A method according to claim 8 wherein the appearance of the second color value, when represented using a predetermined first color output device, is similar to the appearance of the third color value, when represented using a predetermined second color output device.

10. A method according to claim 7 wherein each neuron in the at least one hidden layer and in the output layer comprises summing means for computing a weighted sum of a plurality of inputs.

11. A method according to claim 7 wherein each neuron in the at least one hidden layer and in the output layer also comprises means for computing a nonlinear function of the output of said summing means.

12. A method according to claim 11 and wherein the nonlinear functions corresponding to substantially all of the neurons in the at least one hidden layer and in the output layer are equal.

13. A method according to claim 1 wherein said neural network structure is a feed-forward network structure.

14. A method according to claim 1 wherein each ordered pair is characterized in that there is a predetermined relationship between the second color value and the corresponding first color value.

15. A method according to claim 4 wherein the plurality of regions comprises regions of non-equal size.

16. A method according to claim 4 wherein the plurality of regions comprises regions of equal size.

17. A method according to claim 1 and also comprising the step of subsequently employing the neural network for transforming a first color value from the first plurality of color values into a second color value from the second plurality of color values.

18. A method according to claim 17 and also comprising the step of employing the second color value obtained by transforming the first color value in order to control operation of a color processing system to be calibrated.

19. A method according to claim 1 wherein at least one interneuron connection is defined between a pair of neurons from among the plurality of neurons in the neural network and wherein said step of training comprises the steps of:

providing the first color value of an individual ordered pair to the neural network;

back-propagating an error value indicative of the difference between the second color value of said individual ordered pair and the output of the neural network for the first color value; and

modifying at least one of the at least one interneuron connections.

20. A method according to claim 19 wherein each of the at least one interneuron connections defines a weight associated therewith and said step of modifying comprises the step of changing the value of the weight.

21. Apparatus for providing a neural network including:

a neural network structure including a plurality of neurons for receiving a first color value from among a first set of color values which first color value is to be transformed into a corresponding second color value from among a second set of color values and for providing an output indication of the corresponding second color value; and

means for training the neural network structure on a plurality of ordered pairs, each ordered pair comprising a first color value from among the first set of color values and a corresponding second color value from among the second set of color values.

22. Apparatus according to claim 21 wherein one or both of the plurality of first values and the plurality of second color values is a systematic representation of the respectively corresponding one of the first and second sets of color values.

23. A neural network comprising:

a trained neural network structure including a plurality of neurons for receiving a first color value from among a first set of color values which first color value is to be transformed into a corresponding second color value from among a second set of color values and for providing an output indication of the corresponding second color value.

24. A neural network according to claim 23 wherein the trained neural network structure was trained on a plurality of ordered pairs, each ordered pair comprising a first color value from among the first set of color values and a corresponding second color value from among the second set of color values, one or both of the plurality of first color values and the plurality of second color values being a systematic representation of the respective corresponding one of the first and second sets of color values.

25. A neural network according to claim 23 wherein the second color value includes a black component and the neural network structure is characterized in that at least some of the color content of the first color value is transformed to the black component of the second color value.

26. A method for transforming a first color value from among a first set of color values into a second color value from among a second set of color values, the method comprising the steps of:

providing a trained neural network structure including a plurality of neurons for receiving a first color value from among the first set of color values which first color value is to be transformed into a corresponding second color value from among the second set of color values and for providing an output indication of the corresponding second color value; and

employing the trained neural network structure in order to transform a first color value from among the first set of color values into a second color value from among the second set of color values.

27. A method according to claim 26 wherein the trained neural network structure was trained on a plurality of ordered pairs, each ordered pair comprising a first color value from among the first set of color values and a corresponding second color value from among the second set of color values, one or both of the plurality of first values and the plurality of second color values being a systematic representation of the respective corresponding one of the first and second sets of color values.

28. A method according to claim 26 and also comprising the step of controlling operation of a color processing system to be calibrated using the second color value obtained in said employing step.

29. A method according to claim 28 wherein said step of controlling comprises the step of using said color writing device to create upon a second substrate a duplicate of an analog representation of a color image upon a first substrate.

30. A method according to claim 28 wherein said step of controlling comprises the step of using said color processing system to create an input copy of a color image which, when processed by said calibrated system, will result in a given output copy of said color image.

31. A method according to claim 28 wherein said color processing system to be calibrated comprises a color reading device operative to convert an analog representation of a color image into a digital representation thereof.

32. A method according to claim 28 wherein said color processing system to be calibrated comprises a color writing device operative to convert a digital representation of a color image into an analog representation thereof.

33. A method according to claim 32 wherein said color writing device comprises a color monitor display.

34. A method for constructing a look-up table relating a first multiplicity of values to a second multiplicity of values comprising the steps of:

providing an artificial neural network relating the first multiplicity of values to the second multiplicity of values;

providing a plurality of look-up table addresses;

operating the artificial neural network on the plurality of look-up table addresses, thereby to obtain a plurality of processed look-up table addresses; and

storing the plurality of processed look-up table addresses as the contents of the look-up table.

35. A method according to claim 34 wherein the first and second multiplicities of values respectively comprise first and second multiplicities of color values.

36. A method according to claim 34 wherein the step of providing an artifical neural network comprises the steps of:

providing artificial neural network training data representing the first and second multiplicity of values; and

employing the artifical neural network training data to train an artifical neural network.

37. Apparatus for constructing a look-up table relating a first multiplicity of values to a second multiplicity of values comprising:

an artifical neural network relating the first multiplicity of values to the second multiplicity of values;

means for operating the artificial neural network on a plurality of look-up table addresses, thereby to obtain a plurality of processed look-up table addresses; and

means for storing the plurality of processed look-up table addresses as the contents of the look-up table.

38. Digital storage apparatus comprising:

a representation of a look-up table relating a first multiplicity of values to a second multiplicity of values, the look-up table having been constructed by the following method;

providing an artificial neural network relating the first multiplicity of values to the second multiplicity of values;

providing a plurality of look-up table addresses;

operating the artificial neural network on the plurality of look-up table addresses, thereby to obtain a plurality of processed look-up table addresses; and

storing the plurality of processed look-up table addresses as the contents of the look-up table.

39. A method for constructing apparatus for sampling the color processing characteristics of a color processing device, said color processing device being operative to convert a first representation of a color image into a second representation thereof,

the method comprising the step of repeating at least once the steps of:

providing first and second representations of a color image, said representations respectively comprising a first multiplicity of first color values and a second multiplicity of second color values corresponding thereto, said first and second representations being characterized in that processing said first representation with said color processing device defines said second representation;

providing an artificial neural network which, when operated on each individual one of the second multiplicity of second color values, gives a value substantially equal to the corresponding one of said first multiplicity of first color values; and

operating the artifical neural network on the first representation of the color image, thereby to provide a third representation thereof.

40. A system for constructing apparatus for sampling the color processing characteristics of a color processing device, said color processing device being operative to convert a first representation of a color image to a second representation thereof, the system comprising:

means for providing first and second representations of a color image, said representations respectively comprising a first multiplicity of first color values and a second multiplicity of second color values corresponding thereto, said first and second representations being characterized in that processing said first representations with said color processing device defines said second representation;

an artificial neural network which, when operated on each individual one of the second multiplicity of second color values, gives a value substantially equal to the corresponding one of said first multiplicity of first color values; and

means for operating the artifical neural network on the first representation of the color image, thereby to provide a third representation thereof.