APPARATUS AND METHODS FOR RECOMPRESSION OF DIGITAL IMAGES

• US 20120201476A1
• Filed: 10/05/2010
• Published: 08/09/2012
• 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
• Granted Patent: US 8,908,984 B2
• Time in Patent Office: 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 region within said difference image, said region 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 based on the added-blockiness scores computed for each boundary within the region; and

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

2. The method according to claim 1, wherein said implementing comprises:

defining a plurality of regions within the difference image, each region comprising a plurality of image blocks,

computing a region quality score for each of said plurality of regions, giving rise to a respective plurality of region quality scores; and

computing an image quality score for the second image based on said plurality of region quality scores, and

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

3. 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 further iteration of a search for improved compression parameters is invoked if it is determined that the quality score for the second image does not lie within a predetermined perceptual quality range.

4. The method according to claim 3 and also comprising, if it is determined that the 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.

5. The method according to claim 3, 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.

6. The method according to claim 5, 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.

7. A method according to claim 1 and also comprising providing compression instructions based at least in part on the region quality score.

8. A method according to claim 7 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.

9. 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.

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

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

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

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

14. A method according to claim 12 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 13 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 1 wherein said first image comprises an intra-prediction encoded 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.

24. A method according to claim 1 and also comprising independent disjoint block-level recompression of the first image generated by independent coding of disjoint blocks in a precursor image, the first image having at least one first quantization matrix associated therewith, including performing at least one iterative independent disjoint block-level compression operation on the first image thereby to generate a recompressed second image including generating a new quantization matrix by scaling said at least one first quantization matrix by at least one scaling factor and using said new quantization matrix for said independent disjoint block-level compression.

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

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

8 pixel blocks.

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

70. A method according to claim 1, wherein a search, within a set of suitable MQF values, for a suitable MQF value for scaling a quantization matrix to be used for recompression of an individual first image is performed.

73. A method according to claim 12, wherein a search, within a set of suitable MQF values, for a suitable MQF value for scaling a quantization matrix to be used for recompression of an individual first image is performed.

78. A method according to claim 70 wherein said quantization matrix is used to compute a final quantization matrix which recompresses said individual first image.

86. A method according to claim 1 and also comprising independent disjoint block-level recompression of a first image generated by independent coding of disjoint blocks in a precursor image, the first image having at least one first quantization matrix associated therewith, said independent recompression comprising performing at least one iterative independent disjoint block-level compression operation on the first image thereby to generate a re-compressed second image including generating a new quantization matrix and using said new quantization matrix for said independent disjoint block-level compression,

wherein said new quantization matrix is generated by scaling at least one second quantization matrix by at least one scaling factor, thereby to provide at least one scaled matrix, and subsequently computing at least one additional quantization matrix as a weighted average of said at least one scaled matrix with the at least one first quantization matrix.

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

18. A 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 and by a texture change measure.

19. A method according to claim 18 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.

22. A method according to claim 18 wherein said first image comprises a compressed image such that said iterative compressing comprises a process of recompression.

23. A method according to claim 22 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.

25. A method according to claim 23 wherein said compression comprises H.264 coding and said areas comprise 4 pixel×

4 pixel blocks.

27. A method according to claim 18 and also comprising independent disjoint block-level recompression of the first image generated by independent coding of disjoint blocks in a precursor image, the first image having at least one first quantization matrix associated therewith, including performing at least one iterative independent disjoint block-level compression operation on the first image thereby to generate a recompressed second image including generating a new quantization matrix by scaling said at least one first quantization matrix by at least one scaling factor and using said new quantization matrix for said independent disjoint block-level compression.

36. A method according to claim 18 wherein said plurality of quality measures are mutually normalized and said extent depends on a product.

37. A method according to claim 36 wherein said first image is provisionally compressed using provisional compression parameters and if said product passes a first threshold and at least one of said plurality of quality measures independently passes a second threshold, then said provisional compression parameters are used for subsequent compression.

55. A method according to claim 18 wherein said plurality of quality measures are mutually normalized and said extent depends on a weighted geometric mean of said mutually normalized plurality of quality measures.

59. A method according to claim 18 and wherein said iteratively compressing includes a plurality of iterations.

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

74. A method according to claim 18, wherein a search, within a set of suitable MQF values, for a suitable MQF value for scaling a quantization matrix to be used for recompression of an individual first image is performed.

75. A method according to claim 22, wherein a search, within a set of suitable MQF values, for a suitable MQF value for scaling a quantization matrix to be used for recompression of an individual first image is performed.

87. A method according to claim 18 and also comprising independent disjoint block-level recompression of a first image generated by independent coding of disjoint blocks in a precursor image, the first image having at least one first quantization matrix associated therewith, said independent recompression comprising performing at least one iterative independent disjoint block-level compression operation on the first image thereby to generate a re-compressed second image including generating a new quantization matrix and using said new quantization matrix for said independent disjoint block-level compression,

wherein said new quantization matrix is generated by scaling at least one second quantization matrix by at least one scaling factor, thereby to provide at least one scaled matrix, and subsequently computing at least one additional quantization matrix as a weighted average of said at least one scaled matrix with the at least one first quantization matrix.

26. 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;

evaluating said initial compression by computing a textural change measure;

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.

58. A method according to claim 26 and also comprising independent disjoint block-level recompression of the first image generated by independent coding of disjoint blocks in a precursor image, the first image having at least one first quantization matrix associated therewith, including performing at least one iterative independent disjoint block-level compression operation on the first image thereby to generate a recompressed second image including generating a new quantization matrix by scaling said at least one first quantization matrix by at least one scaling factor and using said new quantization matrix for said independent disjoint block-level compression.

76. A method according to claim 26, wherein a search, within a set of suitable MQF values, for a suitable MQF value for scaling a quantization matrix to be used for recompression of an individual first image is performed.

84. A method according to claim 26 and wherein said iteratively compressing includes a plurality of iterations.

88. A method according to claim 26 and also comprising independent disjoint block-level recompression of a first image generated by independent coding of disjoint blocks in a precursor image, the first image having at least one first quantization matrix associated therewith, said independent recompression comprising performing at least one iterative independent disjoint block-level compression operation on the first image thereby to generate a re-compressed second image including generating a new quantization matrix and using said new quantization matrix for said independent disjoint block-level compression,

wherein said new quantization matrix is generated by scaling at least one second quantization matrix by at least one scaling factor, thereby to provide at least one scaled matrix, and subsequently computing at least one additional quantization matrix as a weighted average of said at least one scaled matrix with the at least one first quantization matrix.

28. A method for independent disjoint block-level recompression of a first image generated by independent coding of disjoint blocks in a precursor image, the first image having at least one first quantization matrix associated therewith, the method comprising:

performing at least one iterative independent disjoint block-level compression operation on the first image thereby to generate a recompressed second image including generating a new quantization matrix by scaling said at least one first quantization matrix by at least one scaling factor and using said new quantization matrix for said independent disjoint block-level compression.

29. A method according to claim 28 wherein said independent disjoint block-level recompression comprises JPEG-to-JPEG recompression and said independent disjoint block-level recompression comprises a JPEG operation.

30. A method according to claim 28 wherein said scaling comprises multiplying all entries in said at least one quantization matrix by a single numerical value.

31. A method according to claim 28 wherein said scaling includes multiplying by the scaling factor to obtain a product and approximating said product by a nearby integer.

32. A method according to claim 28 and also comprising evaluating perceptual quality obtained by an individual independent disjoint block-level compression operation and, if unsatisfactory, initiating an additional independent disjoint block-level compression operation using a different scaling factor than that used in said individual independent disjoint block-level compression operation.

33. A method according to claim 32 wherein said perceptual quality is considered unsatisfactory both if said quality is unacceptably low and if said quality is high enough to justify use of a higher scaling factor.

38. A method according to claim 28 wherein said performing comprises using an initial value for said scaling factor, computing a quality measure characterizing a provisional independent disjoint block-level compression operation on the first image based on said initial value, and if said quality measure falls within a target quality interval terminating the method, and otherwise, compressing further using a subsequent value which is greater or smaller than said initial value for said scaling factor if said quality measure falls on one or another side of the target quality interval respectively.

34. A computerized system that generates a second reduced size digital image from a first digital image by iteratively compressing said first digital image to an extent determined by a blockiness measure quantifying absence of blockiness of the second image.

54. A system according to claim 34 wherein said first digital image is iteratively compressed by a plurality of iterations.

35. 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 and by a texture change measure.

89. A system according to claim 35 wherein said first digital image is iteratively compressed by a plurality of iterations.

39. A method for independent disjoint block-level recompression of a first image generated by independent coding of disjoint blocks in a precursor image, the first image having at least one first quantization matrix associated therewith, the method comprising:

performing at least one iterative independent disjoint block-level compression operation on the first image thereby to generate a re-compressed second image including generating a new quantization matrix and using said new quantization matrix for said independent disjoint block-level compression,

wherein said new quantization matrix is generated by scaling at least one second quantization matrix by at least one scaling factor, thereby to provide at least one scaled matrix, and subsequently computing at least one additional quantization matrix as a weighted average of said at least one scaled matrix with the at least one first quantization matrix.

40. A method according to claim 39, wherein said at least one second quantization matrix comprises at least one default quantization matrix specified in the JPEG standard.

41. A method according to claim 39, wherein said at least one second quantization matrix comprises at least one optimal quantization matrix computed for the image.

42. A method according to claim 39, wherein at least one second quantization matrix is the same as at least one first quantization matrix.

43. A method according to claim 39 wherein said independent disjoint block-level recompression comprises JPEG-to-JPEG recompression and said independent disjoint block-level recompression comprises a JPEG operation.

44. A method according to claim 39 wherein said scaling comprises multiplying all entries in said at least one quantization matrix by a single numerical value.

45. A method according to claim 39 wherein said scaling includes multiplying by the scaling factor to obtain a product and approximating said product by a nearby integer.

46. A method according to claim 39 wherein said weighted average is computed by:

multiplying each entry in said at least one scaled quantization matrix by a corresponding entry of an at least one weighting matrix, said weighting matrix entries comprising values between 0 and 1, to produce a first weighted component;

multiplying each entry in said at least one first quantization matrix by a value of one minus the value of the corresponding entry of an at least one weighting matrix to produce a second weighted component; and

adding the said first weighted component to the said second weighted component values to produce the weighted average element value of the at least one additional quantization matrix.

47. A method according to claim 46 wherein all elements of said at least one weighting matrix are identical.

48. A method according to claim 47 wherein all elements of said at least one weighting matrix equal to 0.5.

49. A method according to claim 47 wherein all elements of said at least one weighting matrix equal to 1.

50. A method according to claim 39 and also comprising evaluating perceptual quality obtained by an individual independent disjoint block-level compression operation and, if unsatisfactory, initiating an additional independent disjoint block-level compression operation using a different scaling factor than that used in said individual independent disjoint block-level compression operation.

51. A method according to claim 50 wherein said perceptual quality is considered unsatisfactory both if said quality is unacceptably low and if said quality is high enough to justify use of a different scaling factor.

77. A method according to claim 39, wherein a search, within a set of suitable MQF values, for a suitable MQF value for scaling a quantization matrix to be used for recompression of an individual first image is performed.

85. A method according to claim 39 and wherein said iteratively compressing includes a plurality of iterations.

57. A computer program product, comprising a computer usable medium having a computer readable program code embodied therein, said computer readable program code adapted to be executed to implement any of the recompression methods shown and described herein.

61. An image recompression method comprising:

Recompressing various images using a recompression process based on at least one parameter;

Wherein recompression of at least one individual image from among said various images includes performing a search, within a set of suitable values for said parameter, for a suitable parameter value to be used for recompression of said individual first image, including selecting one parameter value in said set over at least one other parameter value which is found to be less suited for recompression of said individual first image.

62. A method according to claim 61 wherein recompression of at least one individual image from among said various images includes:

performing a search, within a set of suitable MQF values, for a MQF value suitable for said individual image; and

employing said MQF value in scaling a quantization matrix used in recompression of said individual first image.

63. A method according to claim 62 wherein said search is characterized by its initial MQF value, and wherein said initial MQF value is determined at least partly based on at least one characteristic of the first image.

64. A method according to claim 62 wherein said search is characterized by its initial step size and said initial step size is determined at least partly based on at least one characteristic of the first image.

65. A method according to claim 64 wherein said at least one first image characteristic includes a quantization matrix characterizing the first image.

66. A method according to claim 61 wherein at least one parameter of said search is determined at least partly based on knowledge accumulated in the course of relevant previously performed image recompression processes.

67. A method according to claim 66 wherein said parameter includes an initial MQF value.

68. A method according to claim 66 wherein said parameter includes an initial step size.

69. A method according to claim 66 wherein said relevant previously performed image recompression processes include recompressions of images having a quantization matrix at least similar to the quantization matrix of the individual first image.

71. A method according to claim 62 wherein said quantization matrix is used to compute a final quantization matrix which recompresses said individual first image.

72. A method according to claim 64 wherein the initial step size is selected to be small or large, based on how closely or disparately MQF values used for images sharing a quantization matrix, cluster around a selected initial MQF value.

79. An image recompression system comprising:

An image recompression subsystem operative for recompressing various images using a recompression process based on at least one recompression parameter;

Wherein said subsystem includes a recompression parameter definer operative for performing a search, within a set of suitable values for said parameter, for a suitable parameter value to be used for recompression of at least one individual first image from among said various images, including selecting one parameter value in said set over at least one other parameter value which is found to be less suited for recompression of said individual first image.

80. An image recompression system comprising:

An image recompression subsystem operative for recompressing various images using a recompression process based on at least one recompression parameter; and

a historical archive of recompression parameter values in association with at least one image characteristic of images found historically to be suitable for recompression using said parameter values;

and wherein said recompression subsystem is operative for determining a suitable parameter value to be used for recompression of at least one individual first image from among said various images by finding in said historical archive at least one recompression parameter values associated with image characteristics at least similar at least one corresponding image characteristic of said individual first image.

81. A system according to claim 80 wherein said recompression parameter includes an initial MQF value initializing a search for a better MQF value.

82. A system according to claim 80 wherein said image characteristic comprises a quantization matrix.

83. A method according to claim 80 herein said recompression parameter includes an initial step size characterizing a search for a better MQF value.

91. A recompression method comprising:

Compressing each individual block in at least one image plane of an image to be re-compressed, including;

JPEG-coding said individual block, using a global quantization matrix, thereby to obtain a proposed output block of integers; and

For at least one individual region in the image plane;

generating a regional compression parameter defining a region-specific level of compression which will guarantee no more than a controlled amount of perceptual degradation of said region;

generating a regional quantization matrix from said regional compression parameter; and

for at least one particular block in said individual region, determining whether at least one entry would have been zero if said JPEG-coding step applied to said particular block had used said regional quantization matrix rather than said global quantization matrix and if so, zeroing said at least one entry in the proposed output block JPEG-coded from said particular block.

92. A method according to claim 91 wherein said JPEG-coding comprises DCT-transforming said individual block, quantizing each DCT-transformed block by dividing each entry therewithin by a corresponding entry in the global quantization matrix, thereby to obtain a block of numbers, and rounding said numbers to obtain a proposed output block of integers.

93. A method according to claim 91 wherein said regional compression parameter is generated by performing a search, within a set of suitable values for said regional compression parameter, for a suitable parameter value to be used for recompression of said region.

94. A method according to claim 91 wherein said regional compression parameter comprises an MQF value.

95. A method according to claim 93 wherein said search is computed for at least one first region and subsequently for at least one additional region adjacent to said at least one first region and wherein, when the search is performed for said additional region, said suitable parameter value initiating the search is based at least partly on a final regional compression parameter value generated by said search for said at least one first region.

96. A method according to claim 91 and also comprising computing said region-specific level of compression is computed, for a first region comprising a plurality of image blocks, including:

compressing the first region giving rise to a second region, using a current level of compression;

using a measure of added artifactual edges along coding block boundaries to compute a region quality score for said first region;

and comparing said region quality score to a desired quality threshold including increasing or decreasing said current level of compression if said region quality score is respectively higher or lower than said quality threshold and returning to said compressing including using said increased or decreased level of compression as a current level; and

using said current level of compression as said region-specific level of compression if said region quality score is predeterminedly close to said quality threshold.

97. A method according to claim 96 wherein said using a measure comprises:

computing a difference image between the first and second images;

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; and

computing a region quality score based on the added-blockiness scores computed for each boundary within the region.

98. A method according to claim 93 and also including selecting one parameter value in said set over at least one other parameter value which is found to be less suited for recompression of said region.

99. A method according to claim 95 wherein said at least one first region comprises a region adjacent said additional region along a row and a region adjacent said additional region along a column and wherein said suitable parameter value initiating the search is based at least partly on combining final regional compression parameter values generated by said search for said regions adjacent along said row and said column.

100. A method according to claim 96 wherein said measure is based on a computation of local-similarity and texture.

101. A method according to claim 97 wherein said measure is also based on a computation of local similarity and texture.