Apparatus and methods for recompression of digital images

• US 8,908,984 B2
• Filed: 10/05/2010
• Issued: 12/09/2014
• Est. Priority Date: 10/05/2009
• Status: Active Grant

• Current Assignee: Beamr Imaging Ltd.
• Original Assignee: I.C.V.T Ltd.
• Inventors: Carmel, Sharon, Gill, Dror, Shoham, Tamar
• Primary Examiner(s): TSAI, TSUNG YIN
• Application Number:
  US13/500,473
• Publication Number:
  US 20120201476A1
• Time in Patent Office: 1,526 Days
• US Class Current: 382/239
• CPC Class Codes: H04N 19/124 Quantisation
• 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 19/14 Coding unit complexity, e.g. amount of activity or edge presence estimation H04N19/146 takes precedence
• CPC Class Codes: H04N 19/154 Measured or subjectively estimated visual quality after decoding, e.g. measurement of distortion use of rate-distortion criteria H04N19/147
• CPC Class Codes: H04N 19/17 the unit being an image region, e.g. an object
• CPC Class Codes: H04N 19/176 the region being a block, e.g. a macroblock
• CPC Class Codes: H04N 19/182 the unit being a pixel
• CPC Class Codes: H04N 19/192 the adaptation method, adaptation tool or adaptation type being iterative or recursive
• CPC Class Codes: H04N 19/50 using predictive coding H04N19/61 takes precedence
• CPC Class Codes: H04N 19/60 using transform coding
• CPC Class Codes: H04N 19/625 using discrete cosine transform [DCT]
• CPC Class Codes: H04N 19/86 involving reduction of coding artifacts, e.g. of blockiness

Abstract

A system and method for generating a second reduced size digital image from a first digital image, the method including iteratively compressing the first digital image to an extent determined by a quality measure comprising at least a blockiness measure quantifying added artifactual edges along coding block boundaries of the second image and/or use of a quantization matrix generated by computing a weighted average of the quantization matrix of the first digital image and a scaled second quantization matrix.

Claims

1. A method of enabling an image-adaptive, perceptually-lossless and near-maximal image compression, including:

compressing a first image giving rise to a second image;

implementing a measure of added artifactual edges along coding block boundaries, including;

computing a difference image between the first and second images;

defining a plurality of regions within said difference image, each of said plurality of regions comprising a plurality of image blocks;

within said region, computing for each boundary between two coding blocks a respective added-blockiness score indicating an extent of added artifactual edges along the respective boundary;

computing a region quality score for each of said plurality of regions, based on the added-blockiness scores computed for each boundary within the region, giving rise to a respective plurality of region quality scores; and

computing an image quality score for the second image based on one or more of said plurality of region quality scores; and

determining, based at least upon said image quality score, whether a perceptual quality criterion is met by said second image.

2. The method according to claim 1, wherein said first image is an input image and said second image is a provisionally compressed image, and said compressing is carried out using provisional compression parameters, and wherein a search, or a further iteration of a search, for improved compression parameters is invoked if it is determined that the image quality score for the second image does not lie within a predetermined perceptual quality range.

3. The method according to claim 2 and also comprising, if it is determined that the image quality score for the second image does not meet a perceptual quality criterion, recomputing said provisional compression parameters and repeating said provisional compression, said search and said determining.

4. The method according to claim 2, wherein said compressing comprises, at each individual iteration from among said iterations, adapting a compression parameter based on at least one iteration previous to said individual iteration.

5. The method according to claim 4, wherein said compressing comprises, at each individual iteration from among said iterations, adapting a compression parameter based on all iterations previous to said individual iteration.

6. A method according to claim 1 and also comprising providing compression instructions based at least in part on said plurality of region quality scores.

7. A method according to claim 6 wherein at least two region quality scores are computed for a corresponding set of at least two regions in the output image and wherein said providing compression instructions is based at least in part on said at least two region quality scores.

8. A method according to claim 1 wherein the extent to which said first image is iteratively compressed is also determined by a quality measure other than said added artifactual edges measure.

9. A method according to claim 8 wherein said quality measure comprises a textural measure quantifying textural similarities between the first and second images.

10. A method according to claim 8 wherein said quality measure comprises a local similarity measure quantifying local similarities between the first and second images.

11. A method according to claim 1 wherein said first image comprises a compressed image such that said compressing comprises a process of recompression.

12. A method according to claim 11 wherein said first image comprises a disjoint block level encoded image compressed by independent coding of disjoint blocks in a precursor Image.

13. A method according to claim 12 wherein said disjoint block level encoded image comprises a JPEG encoded image.

14. A method according to claim 11 wherein said second image comprises a disjoint block level encoded image generated from said first digital image by independently coding disjoint blocks in the first image.

15. A method according to claim 14 wherein said disjoint block level encoded image comprises a JPEG encoded image.

16. A method according to claim 1 wherein said second image comprises an intra-prediction encoded image.

17. A method according to claim 16 wherein said intra-prediction encoded image comprises an H.264 image.

18. A method according to claim 1 wherein said first image comprises an intra-prediction encoded image.

19. A method according to claim 18 wherein said intra-prediction encoded image comprises an H.264 image.

20. A method according to claim 1 wherein said image compression comprises JPEG coding or H.264 High Profile coding and said coding blocks comprise 8 pixel×

8 pixel blocks.

21. A method according to claim 1 wherein said image compression comprises H.264 coding and said coding blocks comprise 4 pixel×

4 pixel blocks or 16 pixel×

16 pixel blocks.

34. A non-transitory computer usable medium having a computer readable program code embodied therein, said computer readable program code adapted to be executed to implement the method of claim 1.

22. An image compressing method comprising iteratively compressing a first digital image into a second reduced size digital image to an extent determined by a plurality of quality measures including a local similarity measure quantifying local similarities between the first image and the second reduced size image and by-a texture change measure between the first image and the second reduced size image, wherein said plurality of quality measures are mutually normalized and said extent depends on a product, and wherein said first image is provisionally compressed using provisional compression parameters and if said product passes a first threshold then said provisional compression parameters are used for subsequent compression.

23. A method according to claim 22 wherein said extent is determined such that a first texture change between said first and second images is tolerated if the local similarity measure is high whereas only a second texture change between said first and second images, smaller than said first texture change, is tolerated if the local similarity measure is low.

24. A method according to claim 22 wherein said first image comprises a compressed image such that said iterative compressing comprises a process of recompression.

25. A method according to claim 24 wherein said texture measure comprises a quantitative measure of changes in texture of corresponding areas between the first and second digital images and wherein said recompression comprises pixel-group-based coding defining groups of pixels and wherein said groups define said corresponding areas.

26. A method according to claim 25 wherein said compression comprises H.264 coding and said areas comprise 4 pixel×

4 pixel blocks.

27. A method according to claim 24 and wherein said iteratively compressing includes a plurality of iterations.

28. A method according to claim 22 wherein said extent depends on a weighted geometric mean of said mutually normalized plurality of quality measures.

29. A method according to claim 22 and wherein said iteratively compressing includes a plurality of iterations.

30. A computerized method that iteratively compresses a first digital image into a second reduced size digital image to an extent determined by a texture change measure including:

performing at least one initial compression using at least one initial compression parameter;

defining a plurality of corresponding regions within the first digital image and the second reduced size digital image;

evaluating said initial compression by computing, for each the corresponding regions, a region textural change measure between the first digital image and the second reduced size digital image;

computing an image textural change measure using one or more of the region quality measures;

determining whether the texture change measure falls within a textural change measure interval; and

if said textural change measure falls within the textural change measure interval terminating the method, and otherwise, compressing further using a further compression parameter larger or smaller than said initial compression parameter if said textural change measure falls on one or another side of the textural change measure interval respectively.

31. A method according to claim 30 and wherein said iteratively compressing includes a plurality of iterations.

32. A computerized system that iteratively compresses a first digital image into a second reduced size digital image to an extent determined by a local similarity measure quantifying local similarities between the first image and the second reduced size image and by a texture change measure between the first image and the second reduced size image, wherein said plurality of quality measures are mutually normalized and said extent depends on a product, and wherein said first digital image is provisionally compressed using provisional compression parameters and if said product passes a first threshold then said provisional compression parameters are used for subsequent compression.

33. A system according to claim 32 wherein said first digital image is iteratively compressed by a plurality of iterations.

35. A system for enabling an image-adaptive, perceptually-lossless and near-maximal image compression, the system having a processor configured to:

compress a first image giving rise to a second image;

implement a measure of added artifactual edges along coding block boundaries, including;

compute a difference image between the first and second images;

define a plurality of regions within said difference image, each of said plurality of regions comprising a plurality of image blocks;

within said region, compute for each boundary between two coding blocks a respective added-blockiness score indicating an extent of added artifactual edges along the respective boundary;

compute a region quality score for each of said plurality of regions, based on the added-blockiness scores computed for each boundary within the region, giving rise to a respective plurality of region quality scores; and

compute an image quality score for the second image based on at least one of said plurality of region quality scores; and

determine based at least upon said image quality score, whether a perceptual quality criterion is met by said second image.

36. The system of claim 35, wherein said first image is an input image and said second image is a provisionally compressed image, and said compression is carried out using provisional compression parameters, and wherein a search, or a further iteration of a search, for improved compression parameters is invoked if it is determined that the image quality score for the second image does not lie within a predetermined perceptual quality range.

37. The system of claim 36, wherein said processor is further configured, if it is determined that the image quality score for the second image does not meet a perceptual quality criterion, to recompute said provisional compression parameters and repeat said provisional compression, said search and said determine.

38. The system of claim 36, wherein said compression comprises utilizing the processor, at each individual iteration from among said iterations, to adapt a compression parameter based on at least one iteration previous to said individual iteration.

39. The system of claim 38, wherein said compression comprises utilizing the processor, at each individual iteration from among said iterations, to adapt a compression parameter based on all iterations previous to said individual iteration.

40. The system of claim 35, wherein said processor is further configured to provide compression instructions based at least in part on said plurality of region quality scores.

41. The system of claim 40, wherein at least two region quality scores are computed for a corresponding set of at least two regions in the output image and wherein said provide compression instructions is based at least in part on said at least two region quality scores.

42. The system of claim 35, wherein the extent to which said first image is iteratively compressed is also determined by a quality measure other than said added artifactual edges measure.

43. The system of claim 42, wherein said quality measure comprises a textural measure quantifying textural similarities between the first and second images.

44. The system of claim 42, wherein said quality measure comprises a local similarity measure quantifying local similarities between the first and second images.

45. The system of claim 35, wherein said first image comprises a compressed image such that said compress comprises a process of recompression.

46. The system of claim 45, wherein said first image comprises a disjoint block level encoded image compressed by independent coding of disjoint blocks in a precursor image.

47. The system of claim 46, wherein said disjoint block level encoded image comprises a JPEG encoded image.

48. The system of claim 45, wherein said second image comprises a disjoint block level encoded image generated from said first digital image by independently coding disjoint blocks in the first image.

49. The system of claim 48, wherein said disjoint block level encoded image comprises a JPEG encoded image.

50. The system of claim 35, wherein said second image comprises an intra-prediction encoded image.

51. The system of claim 35, wherein said first image comprises an intra-prediction encoded image.

52. An image compressing system having a processor configured to iteratively compress a first digital image into a second reduced size digital image to an extent determined by a plurality of quality measures including a local similarity measure quantifying local similarities between the first image and the second reduced size image and a texture change measure between the first image and the second reduced size image, wherein said plurality of quality measures are mutually normalized and said extent depends on a product, and wherein said processor is configured to provisionally compress said first image using provisional compression parameters and if said product passes a first threshold then to use said provisional compression parameters for subsequent compression.

53. The system of claim 52 wherein said extent is determined such that a first texture change between said first and second images is tolerated if the local similarity measure is high whereas only a second texture change between said first and second images, smaller than said first texture change, is tolerated if the local similarity measure is low.

54. The system of claim 52 wherein said first image comprises a compressed image such that said iterative compression comprises a process of recompression.

55. The system of claim 54 wherein said texture measure comprises a quantitative measure of changes in texture of corresponding areas between the first and second digital images and wherein said recompression comprises pixel-group-based coding defining groups of pixels and wherein said groups define said corresponding areas.