System and method for controlling image compression based on image emphasis

• US 20030059121A1
• Filed: 07/23/2001
• Published: 03/27/2003
• Est. Priority Date: 07/23/2001
• Status: Active Grant

• Current Assignee: Monument Peak Ventures, LLC (Dominion Harbor Enterprises, LLC)
• Original Assignee: Eastman Kodak Company
• Inventors: Etz, Stephen P., Rabbani, Majid, Savakis, Andreas E.
• Granted Patent: US 6,832,006 B2
• Time in Patent Office: Days
• US Class Current: 382/239
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06V 20/10 Terrestrial scenes scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59
• CPC Class Codes: G06V 40/162 using pixel segmentation or colour matching
• CPC Class Codes: H04N 19/115 Selection of the code volume for a coding unit prior to coding
• CPC Class Codes: H04N 19/126 Details of normalisation or weighting functions, e.g. normalisation matrices or variable uniform quantisers
• CPC Class Codes: H04N 19/136 Incoming video signal characteristics or properties
• CPC Class Codes: H04N 19/17 the unit being an image region, e.g. an object
• CPC Class Codes: H04N 19/172 the region being a picture, frame or field
• CPC Class Codes: H04N 19/186 the unit being a colour or a chrominance component
• CPC Class Codes: H04N 19/60 using transform coding
• CPC Class Codes: H04N 19/63 using sub-band based transform, e.g. wavelets
• CPC Class Codes: H04N 19/85 using pre-processing or post-processing specially adapted for video compression

Abstract

A method to automatically vary the compression of images by ranking images within clusters based upon image emphasis. The ranking process computes one or more quantities related to one or more features in each image. The features can include the content of images. The invention processes the quantities with a reasoning algorithm that is trained based on opinions of one or more human observers. The invention applies the quantities to the images to produce the ranking and variably compresses the images depending upon the ranking. The images having a low ranking and are compressed more than images that have a high ranking.

Claims

10. A method automatically compressing images comprising:

(a) ranking images within said clusters based upon image emphasis,

wherein said ranking includes;

(1) computing one or more quantities related to one or more features in each image, said features including a content of said images;

(2) processing said quantities with a reasoning algorithm that is trained based on opinions of one or more human observers; and

(3) applying said quantities to said images to produce said ranking; and

(b) variably compressing said images depending upon said ranking such that first images having a first ranking are compressed more than second images having a second ranking higher than said first ranking.

11. The method as claimed in claim 10, wherein said features include at least one of people-related features and subject-related features.

12. The method as claimed in claim 10, wherein said computing further includes computing one or more quantities related to one or more objective features pertaining to objective measures of the digital image.

13. The method as claimed in claim 12, wherein said objective features include at least one of colorfulness and sharpness.

14. The method as claimed in claim 12, wherein said objective features include a representative quality in terms of color content.

15. The method as claimed in claim 12, wherein said objective features include a uniqueness of picture aspect format.

16. The method as claimed in claim 10, wherein said reasoning algorithm is trained from ground truth studies of candidate images.

17. The method as claimed in claim 10, wherein the reasoning algorithm comprises a Bayesian network.

18. The method as claimed in claim 10, wherein said content of said images is controlled using a quality factor whose value is related to image emphasis/appeal.

19. The method as claimed in claim 10, wherein content of said images is controlled based on output file size whose value is related to image emphasis/appeal.

20. The method as claimed in claim 10, wherein said content of said images is controlled using a visual quality of an output image.

21. A method automatically compressing images comprising:

(a) ranking images within said clusters based upon image emphasis,

wherein said ranking includes;

(1) computing one or more quantities related to one or more objective features in each image, said objective features including a content of said images;

(2) processing said quantities with a reasoning algorithm that is trained based on opinions of one or more human observers; and

(3) applying said quantities to said images to produce said ranking; and

(b) variably compressing said images depending upon said ranking.

1. A method automatically compressing images comprising:

(a) ranking images within said clusters based upon image emphasis,

wherein said ranking includes;

(1) computing one or more quantities related to one or more features in each image, said features including a content of said images;

(2) processing said quantities with a reasoning algorithm that is trained based on opinions of one or more human observers; and

(3) applying said quantities to said images to produce said ranking; and

(b) variably compressing said images depending upon said ranking.

2. The method as claimed in claim 1, wherein said features include at least one of people-related features and subject-related features.

3. The method as claimed in claim 1, wherein said computing further includes computing one or more quantities related to one or more objective features pertaining to objective measures of the digital image.

4. The method as claimed in claim 3, wherein said objective features include at least one of colorfulness and sharpness.

5. The method as claimed in claim 3, wherein said objective features include a representative quality in terms of color content.

6. The method as claimed in claim 3, wherein said objective features include a uniqueness of picture aspect format.

7. The method as claimed in claim 1, wherein said reasoning algorithm is trained from ground truth studies of candidate images.

8. The method as claimed in claim 1, wherein the reasoning algorithm comprises a Bayesian network.

9. The method as claimed in claim 1, wherein said compressing of said images is varied such that first images having a first ranking are compressed more than second images having a second ranking higher than said first ranking.

22. The method as claimed in claim 21, wherein said objective features include at least one of people-related objective features and subject-related objective features.

23. The method as claimed in claim 21, wherein said objective objective features include at least one of colorfulness and sharpness.

24. The method as claimed in claim 21, wherein said objective objective features include a representative quality in terms of color content.

26. The method as claimed in claim 21, wherein said reasoning algorithm is trained from ground truth studies of candidate images.

27. The method as claimed in claim 21, wherein the reasoning algorithm comprises a Bayesian network.

28. The method as claimed in claim 21, wherein said compressing of said images is varied such that first images having a first ranking are compressed more than second images having a second ranking higher than said first ranking.

24-1. The method as claimed in claim 21, wherein said objective objective features include a uniqueness of picture aspect format.