ISO/IEC TR 15938-8:2002/Amd 1:2004
(Amendment)Information technology — Multimedia content description interface — Part 8: Extraction and use of MPEG-7 descriptions — Amendment 1: Extensions of extraction and use of MPEG-7 descriptions
Information technology — Multimedia content description interface — Part 8: Extraction and use of MPEG-7 descriptions — Amendment 1: Extensions of extraction and use of MPEG-7 descriptions
Technologies de l'information — Interface de description du contenu multimédia — Partie 8: Extraction et utilisation des descriptions MPEG-7 — Amendement 1: Extensions d'extraction et utilisation des descriptions MPEG-7
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Standards Content (Sample)
TECHNICAL ISO/IEC
REPORT TR
15938-8
First edition
2002-12-15
AMENDMENT 1
2004-11-15
Information technology — Multimedia
content description interface —
Part 8:
Extraction and use of MPEG-7
descriptions
AMENDMENT 1: Extensions of extraction
and use of MPEG-7 descriptions
Technologies de l'information — Interface de description du contenu
multimédia —
Partie 8: Extraction et utilisation des descriptions MPEG-7
AMENDEMENT 1: Extensions d'extraction et utilisation des descriptions
MPEG-7
Reference number
ISO/IEC TR 15938-8:2002/Amd.1:2004(E)
©
ISO/IEC 2004
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ISO/IEC TR 15938-8:2002/Amd.1:2004(E)
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ISO/IEC TR 15938-8:2002/Amd.1:2004(E)
Foreword
ISO (the International Organization for Standardization) and IEC (the International Electrotechnical
Commission) form the specialized system for worldwide standardization. National bodies that are members of
ISO or IEC participate in the development of International Standards through technical committees
established by the respective organization to deal with particular fields of technical activity. ISO and IEC
technical committees collaborate in fields of mutual interest. Other international organizations, governmental
and non-governmental, in liaison with ISO and IEC, also take part in the work. In the field of information
technology, ISO and IEC have established a joint technical committee, ISO/IEC JTC 1.
International Standards are drafted in accordance with the rules given in the ISO/IEC Directives, Part 2.
The main task of the joint technical committee is to prepare International Standards. Draft International
Standards adopted by the joint technical committee are circulated to national bodies for voting. Publication as
an International Standard requires approval by at least 75 % of the national bodies casting a vote.
In exceptional circumstances, the joint technical committee may propose the publication of a Technical Report
of one of the following types:
type 1, when the required support cannot be obtained for the publication of an International Standard,
despite repeated efforts;
type 2, when the subject is still under technical development or where for any other reason there is the
future but not immediate possibility of an agreement on an International Standard;
type 3, when the joint technical committee has collected data of a different kind from that which is
normally published as an International Standard (“state of the art”, for example).
Technical Reports of types 1 and 2 are subject to review within three years of publication, to decide whether
they can be transformed into International Standards. Technical Reports of type 3 do not necessarily have to
be reviewed until the data they provide are considered to be no longer valid or useful.
Attention is drawn to the possibility that some of the elements of this document may be the subject of patent
rights. ISO and IEC shall not be held responsible for identifying any or all such patent rights.
Amendment 1 to ISO/IEC TR 15938-8:2002 was prepared by Joint Technical Committee ISO/IEC JTC 1,
Information technology, Subcommittee SC 29, Coding of audio, picture, multimedia and hypermedia
information.
NOTE This document preserves the sectioning of ISO/IEC TR 15938-8:2002. The text and figures given in this
document are currently being considered as additions and/or modifications to those corresponding sections in
ISO/IEC TR 15938-8:2002.
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ISO/IEC TR 15938-8:2002/Amd.1:2004(E)
Information technology — Multimedia content description
interface —
Part 8:
Extraction and use of MPEG-7 descriptions
AMENDMENT 1: Extensions of extraction and use of MPEG-7
descriptions
Add after subclause 5.6:
5.7 GofGopFeature
This datatype is used to describe a certain visual feature representative of a series of video frames or
collection of pictures. It is obtained by aggregating the visual descriptors extracted from each video frame or
image in the collection.
5.7.1 Feature Extraction
First, the extraction algorithm computes a descriptor of the visual feature for each frame in the sequence or
each image in the collection. The extraction is specified in the subclauses corresponding to the descriptor
used (e.g. for HomogeneousTexture, subclause 4.3.1.1 is used). Once the values of the frame/image-based
descriptors are computed, a instance of GofGopFeature is derived by the aggregation procedure
corresponding to the descriptor used; as defined in ISO/IEC 15938-3.
There are three aggregation methods (i.e. Average, Median, SplitMerge) as follows:
� Average:
Each component of descriptors in the GOF or GOP is summed and then averaged to compose the
aggregated description
� Median:
Each component of descriptors in the GOF or GOP is sorted and then the middle value is selected to
compose the aggregated description.
� SplitMerge:
The DominantColor descriptors from different images are aggregated by merging of the clusters
(“Value” elements) of different descriptors based on their proximity in colour space (the clusters within
the same descriptor are also included as a special case, although if the extraction algorithm from
4.2.3.1 is followed, their distance will be greater than DISTANCE_MIN specified below). The merging
procedure is performed iteratively, starting with the closest pair and repeating until only a small number
of combined clusters remains. The outline of this algorithm is as follows:
closest_distance=0
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ISO/IEC TR 15938-8:2002/Amd.1:2004(E)
While (number_of_clusters > MAX_NUM_OF_CLUSTERS or
closest_distance < DISTANCE_MIN) {
1. find two closest clusters
2. merge these two clusters
}
The distance between clusters is defined as the Euclidean distance between cluster centres,
DISTANCE_MIN is the same as in 4.2.3.1 and MAX_NUM_OF_CLUSTERS is equal to 8.
Merging of the clusters is performed as follows. The representative colour value for the merged cluster
is a weighted average of the colour values of the component clusters, where the weights are the
relative pixel counts in the clusters.
m = w m +w m
1 1 2 2
Merging of the colour variances is based on the assumption that each colour component is
independent and for each component we assume that we are calculating the variance of a weighted
sum of two Gaussian distributions. This leads to the following formula for the variance of the merged
2
cluster σ :
2
2 2 2
σ = w σ +w σ +w w()m −m ,
1 1 2 2 1 2 1 2
2 2
where σ ,σ are the variances of the component clusters, m ,m are their means and w ,w are
1 2 1 2 1 2
w = W1/(W1+W2), w = W2/(W1+W2)
1 2
where W1 and W2 are the unquantised weights for sub-descriptors.
5.7.2 Similarity Matching Criteria
Matching of GofGopFeature is performed using the descriptors’ matching function appropriate to the
descriptor used. Only GofGopFeature descriptors characterizing the same feature can be compared. For
example, GofGopFeature using the HomogeneousTexture descriptor for two different sequences can be
compared. Some descriptors allow multiple aggregation methods, for example, the Color Layout or Edge
Histogram descriptors. Matching of GofGopFeature describing the same feature but derived with a different
aggregation method is possible.
5.7.3 DDL instantiation examples
In the following two examples, an instance of ColorLayout is embedded in the GofGopFeature datatype.
In the first example, there is no specification of aggregation method.
48
34
32
12 10 13 9 10
14 15
16 12
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ISO/IEC TR 15938-8:2002/Amd.1:2004(E)
In the second example, “Average” is used to aggregate descriptions.
48
34
32
15 11 13 9 8
14 15
16 12
In the following example, an instance of DominantColor is embedded in the GofGopFeature datatype.
0
5
0 89 203
0 1 1
14
120 43 74
0 1 0
12
243 212 27
1 0 0
In the following two examples, an instance of EdgeHistogram is embedded in the GofGopFeature datatype.
In the first example, there is no specification of aggregation method
2 6 4 4 2 1 7 5 3 2 1 6 4 2 2 2 5 4
5 3 1 5 5 6 5 2 6 5 4 4 1 6 4 4 4 0 6 3 5
2 1 5 5 6 6 4 2 3 6 7 3 2 5 5 7 3 2 4 4 7
1 5 6 4 6 1 5 7 4 5 1 6 4 6 5 1 3 4 7 6
In the second example, “Average” is used to aggregate descriptions.
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ISO/IEC TR 15938-8:2002/Amd.1:2004(E)
2 6 4 4 2 1 7 5 3 2 1 6 4 2 2 2 5 4 5 3 1
5 5 6 5 2 6 5 4 4 1 6 4 4 4 0 6 3 5 2 1 5
5 6 6 4 2 3 6 7 3 2 5 5 7 3 2 4 4 7 1 5 6
4 6 1 5 7 4 5 1 6 4 6 5 1 3 4 7 6
In the following two examples, an instance of HomogeneousTexture is embedded in the GofGopFeature
datatype.
In the first example, there is no specification of aggregation method.
19
20
103 87 99 130 97 73 112 109 122 132 108 102 105 113
106 141 103 111 78 76 82 117 88 70 69 61 48 68 48
53
106 84 94 130 94 75 107 104 117 128 100 99 97 107 92
132 90 106 76 64 78 110 83 65 64 52 39 72 35 47
In the second example, “Median” is used to aggregate descriptions.
19
20
103 87 99 130 97 73 112 109 122 132 108 102 105 113
106 141 103 111 78 76 82 117 88 70 69 61 48 68 48
53
106 84 94 130 94 75 107 104 117 128 100 99 97 107 92
132 90 106 76 64 78 110 83 65 64 52 39 72 35 47
5.7.3 Conditions of Usage
There are no specific conditions and limitations on the use of this container datatype.
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ISO/IEC TR 15938-8:2002/Amd.1:2004(E)
Add after subclause 6.8:
6.9 Color Temperature
The color temperature of an image specifies the color of illumination in the scene of the image. It is expressed
by Kelvin (K) temperature scale in the [1667K, 25000K] range. Using this, the color temperature descriptor
describes the perceptual temperature feeling of an image. It targets the perception-based image browsing that
enables viewers to navigate and match images based on the temperature perception (i.e. hot, warm,
moderate, and cool) of the image.
This descriptor is also useful when a user would like to change the illumination of scene (i.e. still images or
video) in favor of the user’s preference. For example, some people might want to see warmer images (e.g.
taken under incandescent lights) than original images while some people might want to see cooler images
(e.g. taken under bright daylights). Those effects can be automatically achieved by adjusting the color
temperature.
6.9.1 Color Temperature Browsing
6.9.1.1 Feature Extraction
The (correlated) color temperature of the scene-illumination in the image is extracted as follows.
Note: In this section, several references are made to sRGB, perceived illuminant, and (correlated) color
temperature and its reciprocal scale. All information on these subjects can be found in [AMD1-1][AMD1-2]
[AMD1-3][AMD1-4][AMD1-5].
6.9.1.1.1 The Overall View of Color Temperature Extraction Algorithm
1) Linearizing input image: RGB � R G B
l l l
2) Converting R G B into XYZ
l l l
3) Removing pixels that have the pixel value smaller than the low luminance threshold(T )
ll
4) Averaging XYZ value for all remained pixels: X Y Z
a a a
5) Calculating the self-luminous threshold: X , Y ,Z If X , Y ,Z have the same values with the
T T T T T T
s s s s s s
previous values, go to procedure 7), else remove pixels that have the pixel value bigger than the self-
luminous threshold and repeat procedure 4) to 6)
6) Averaging XYZ value for all pixels remained, estimating it as the illuminant tri-stimulus values, and
computing the scene-illuminant chromaticity coordinates (x , y ) in CIE 1931 diagram
s s
7) Converting the scene-illuminant chromaticity (x , y ) into color temperature T
s s c
(1) Calculating the chromaticity coordinates (u , v ) in CIE 1960 UCS diagram from (x , y )
s s s s
(2) Finding two adjacent isotemperature lines from (u , v ) and obtaining the distance from those lines
s s
(3) Computing the correlated color temperature using the distance ratio
6.9.1.1.2 The Detail of Extraction Algorithm
1) Linearizing input image: Obtain the linearized R G B from the inverse gamma correction of the input
l l l
RGB, which is the gamma-corrected for display devices
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ISO/IEC TR 15938-8:2002/Amd.1:2004(E)
Note, it is assumed that an input image RGB is a gamma-corrected non-linear sR’G’B’ in the range of
0~255(8bit) in the following equations.
' ' '
if R (i, j),G ()i, j ,B ()i, j ≤ 0.03928× 255.0 ,
sRGB sRGB sRGB
'
R sRGB()i, j
R ()i, j = ÷12.92
sRGB
255
'
G ()i, j
sRGB
G ()i, j = ÷12.92 ,
sRGB
255
'
B ()i, j
sRGB
()
B i, j = ÷12.92
sRGB
255
' ' '
else R sRGB(i, j),G sRGB()i, j ,B sRGB()i, j > 0.03928× 255.0 ,
2.4
'
()
R sRGB i, j
+ 0.055
255
R()i, j = R ()i, j =
l sRGB
1.055
2.4
'
G ()i, j
sRGB
+ 0.055
255
G()i, j =G (i, j) = ,
l sRGB
1.055
2.4
'
()
B sRGB i, j
+ 0.055
255
B()i, j = B ()i, j =
l sRGB
1.055
where (i,j) is the index for pixels
2) Converting linearized R G B into CIE 1931 tristimulus XYZ with conversion matrix M
l l l
X()i, j R()i, j
l
Y()i, j = Μ • G()i, j ,
l
Z()i, j B()i, j
l
0.4124 0.3576 0.1805
where conversion matrix M = 0.2126 0.7152 0.0722 .
0.0193 0.1192 0.9505
3) Removing pixels that have the pixel value smaller than the low luminance threshold(T )
ll
Y(i, j)
ll
,
otherwise, p(i, j) = 255
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ISO/IEC TR 15938-8:2002/Amd.1:2004(E)
where p(i, j) is the label for each pixel at the location (i, j).
4) Averaging XYZ value for all pixels remained, which have p(i, j) = 255 : X Y Z . row * col intuitively
a a a
means the number of all pixels remained.
row−1col−1
1
X = X (i, j),
a ∑∑
(row×col)
i=0 j=0
row−1col−1
1
Y = Y(i, j),
∑∑
a
(row×col)
i=0 j=0
row−1col−1
1
Z = Z(i, j).
a ∑∑
(row×col)
i=0 j=0
Calculating the self-luminous threshold: X , Y ,Z
T T T
s s s
X = f ×k ×X ,
T a
s
Y = f ×k ×Y , ,
T a
s
Z = f ×k ×Z .
T a
s
where f*k* X Y Z means the estimated illuminant level [AMD1-4].
a a a
6) If X , Y ,Z have the same values with the previous values, go to procedure 7), else remove pixels
T T T
s s s
that have the pixel value bigger than the self-luminous threshold and repeat procedure 4) to 6)
If (X (t) = X (t −1), Y (t) =Y (t −1),Z (t) = Z (t −1) ) { go to 7) }
T T T T T T
s s s s s s
else {
X (i, j) > X or Y(i, j) >Y or Z(i, j) > Z , p(i, j) = 0
T T T
s s s
otherwise, p(i, j) = 255
}
repeat 4) ~ 6)
where t means the iteration time for the T and the initial values are set to
s
X (0) = 0, Y (0) = 0,Z (t) = 0 .
T T T
s s s
7) Averaging the XYZ value for all pixels remained, estimating it as an illuminant tri-stimulus value, and
computing the scene-illuminant chromaticity coordinates (x , y ) in CIE 1931 diagram. Again, row * col
s s
intuitively means the number of all pixels remained, which have p(i, j) = 255.
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ISO/IEC TR 15938-8:2002/Amd.1:2004(E)
row−1col−1
1
X = X (i, j),
s ∑∑
(row×col)
i=0 j=0
row−1col−1
1
Y = Y (i, j),
s ∑∑
(row×col)
i=0 j=0
row−1col−1
1
Z = Z(i, j).
s ∑∑
(row×col)
i=0 j=0
X
s
x = ,
s
X +Y +Z
s s s
Y
s
y = .
s
X +Y +Z
s s s
8) Converting the scene-illuminant chromaticity (x , y ) into color temperature T .
s s c
(1) Calculating the chromaticity coordinates (u , v ) in CIE 1960 UCS diagram from (x , y ).
s s s s
4x
s
u = ,
s
− 2x +12y + 3
s s
6y
s
v = .
s
− 2x +12y + 3
s s
(2) Finding two adjacent isotemperature lines [Mori et al (1968)] from (u , v ) and obtaining the
s s
distance from those lines: if (u , v ) is located between i-th and i+1-th isotemperature line then di / di+1
s s
< 0
(v −v ) −t (u −u )
s i i s i
d = ,
i
1
2
2
(1+t )
i
where (u, v ), t: chromaticity coordinates and slope for representing the i-th isotemperature line
i i i
(Table AMD1-1 - Isotemperature lines: Calculated in accordance with the method proposed by Mori
et al.(1968): The color temperatures between 1667K and 25000K and corresponding parameters(u,
i
v, t ) are marked with blue fonts) and d : distance between (u , v ) and the ith isotemperature line.
i i i s s
(3) Calculating the correlated color temperature using the ratio of distance
−1
1 d 1 1
i
T = + − ,
c
T d −d T T
i i i +1 i +1 i
where T is the color temperature for the cross point of the i-th isotemperature line with the daylight
I
locus. The color temperatures less than 1667K and larger than 25000K are tuned to 1667K and
25000K, respectively.
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ISO/IEC TR 15938-8:2002/Amd.1:2004(E)
Table AMD1-1 — Isotemperature lines: Calculated in accordance with the method proposed by Mori et
al.(1968)
i Reciprocal TemperatureT
u v t
i i i
Megakelvin (K)
1 0 Infinity 0.18006 0.26352 -0.24341
2 10 100,000 0.18066 0.26589 -0.25479
3 20 50,000 0.18133 0.26846 -0.26876
4 30 33,333 0.18208 0.27119 -0.28539
5 40 25,000 0.18293 0.27407 -0.30470
6 50 20,000 0.18388 0.27709 -0.32675
7 60 16,667 0.18494 0.28021 -0.35156
8 70 14,286 0.18611 0.28342 -0.37915
9 80 12,500 0.18740 0.28668 -0.40955
10 90 11,111 0.18880 0.28997 -0.44278
11 100 10,000 0.19032 0.29326 -0.47888
12 125 8,000 0.19462 0.30141 -0.58204
13 150 6,667 0.19962 0.30921 -0.70471
14 175 5,714 0.20525 0.31647 -0.84901
15 200 5,000 0.21142 0.32312 -1.0182
16 225 4,444 0.21807 0.32909 -1.2168
17 250 4,000 0.22511 0.33439 -1.4512
18 275 3,636 0.23247 0.33904 -1.7298
19 300 3,333 0.24010 0.34308 -2.0637
20 325 3,077 0.24702 0.34655 -2.4681
21 350 2,857 0.25591 0.34951 -2.9641
22 375 2,677 0.26400 0.35200 -3.5814
23 400 2,500 0.27218 0.35407 -4.3633
24 425 2,353 0.28039 0.35577 -5.3762
25 450 2,222 0.28863 0.35714 -6.7262
26 475 2,105 0.29685 0.35823 -8.5955
27 500 2,000 0.30505 0.35907 -11.324
28 525 1,905 0.31320 0.35968 -15.628
29 550 1,818 0.32129 0.36011 -23.325
30 575 1,739 0.32931 0.36038 -40.770
31 600 1,667 0.33724 0.36051 -116.45
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ISO/IEC TR 15938-8:2002/Amd.1:2004(E)
6.9.1.1.3 Optimal Interval Determination for Color Temperature Browsing Categories
To find the optimal range of color temperature values for each browsing category, interval classifiers for fuzzy
categories based on rough information systems were used.
6.9.1.2 Browsing Method
1. For the hot image browsing, the browser starts displaying the images from the lowest sub-range in the
hot color temperature range and continues displaying the images in the subsequent sub-ranges.
2. For the warm and moderate image browsing, the browser starts displaying images in the middle sub-
range and continues displaying the images in the sub-ranges near the middle sub-ranges.
For the cool image browsing, the browser starts displaying the images from the highest sub-range in the cool
color temperature range and continues displaying the images in the subsequent sub-ranges in a descending
order.
The following is a pseudo-code in HTML format for color temperature browsing in the web browser using DOM and
JavaScript. This code reads the XML document and generates corresponding DOM objects. Assume that the XML
document of image DB is composed of image elements, which are again composed of an image link and its color
temperature browsing type. This code produces category buttons on the web window. If one of the 4 category buttons is
pushed, it will return the images belonging to the chosen category. Here, SortAscendingOrder(), SortDecendingOrder(),
and RearrangeNeartoFar() functions are left out to implementers where one can easily implement them.
color temperature browsing
Color Temperature Browsing
HOT
WARM
MODERATE
COOL
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ISO/IEC TR 15938-8:2002/Amd.1:2004(E)
6.9.2 Display Preference Control
People would often like to change the quality of the image or scene shown in display devices toward a way, which he/she
prefers most. For example, people often control several buttons such as hue, brightness, and contrast to get the most
natural and preferable scene for them. The color temperature provides the effective and efficient way to support the
display preference control.
6.9.2.1 Decoding of Color Temperature
Color temperature of image could be obtained as follows:
Decode the first 2bits to identify the color temperature range of the category (e.g. 00: hot -> [1667K, 2250K],
Tlb = 1667, Tub = 2250).
6 6 6 6
1) RTlb = 10 / Tlb, RTub = 10 / Tub (e.g. RTlb = 10 / 1667 = 599.88, RTub = 10 / 2250 = 444.444).
2) Uniformly quantize [599.88, 444.444] into 64 sub-ranges.
3) Decode the last 6bits (e.g. 000001 -> 2nd sub-range) and pick the corresponding range (e.g. [597.0149,
595.0227]).
4) Calculate a mean ((597.0149 + 595.0227)/2 = 596.0188) and a representative color temperature of the
6
range (i.e. 10 / 596.0188 ≈ 1678K).
6.9.2.1.1 Color Temperature Conversion for Display Preference Control
6.9.2.1.1.1 Color Temperature Conversion Overview
Input Image
User
Temperature
(RGB)
R ’G ’B ’ T
i i i u
R G B XYZ T
Linearize RGB → XYZ Calculate Temperature
i i i i
Color
(Inverse Conversion Temperature Mapping
Gamma) T
t
Calculation of the coefficient for Color
Temperature Conversion
M
c
Temperature XYZ → RGB
X’Y’Z’
XYZ
Conversion Conversion
R’G’B’
Gamma
Output Image R G B
o o o
correction
(RGB)
Figure AMD1-1 — Block diagram for the color temperature conversion
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ISO/IEC TR 15938-8:2002/Amd.1:2004(E)
The overall flow of the color temperature conversion is shown in Figure AMD1-1 - Block diagram for the color
temperature conversion. The color temperature mapping functions, calculation of the coefficient matrix, and
image conversion are described in detail in the following subclauses.
6.9.2.1.2 Color Temperature Mapping Functions
When we obtain a user preferred color temperature against a training image (w/ a reference color
temperature) through some user interface, we need
...
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