EP0780776A1
Expired / Lapsed
EP
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Apparatus for detecting a cut in a video
Assignee: Siemens Corporate Research Inc
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Filing: 1996-11-19
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Granted: —
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Expires: 2016-11-19
Abstract
Abstract Apparatus for detecting a cut in a video comprises arrangements for acquiring video images from a source, for deriving from the video images a pixel-based difference metric, for deriving from the video images a distribution-based difference metric, and for measuring video content of the video images to provide up-to-date test criteria. Arrangements are included for combining the pixel-based difference metric and the distribution-based difference metric, taking into account the up-to-date test criteria provided so as to derive a scene change candidate signal and for filtering the scene change candidate signal so as to generate a scene change frame list.
Inventors
- 👤 Shih-Ping Liou
- 👤 David L. Loching
- 👤 Farshid Arman
Bibliographic Details
Application Number
—
Jurisdiction
EP
Data Source
google_patents
Last Synchronized
2026-11-09 16:54
CPC Classifications
G; G11; G11B; G11B27/00; G11B27/10; G11B27/19; G11B27/28; G06; G06F; G06F16/00; G06F16/70; G06F16/74; G06F16/745; G06F16/78; G06F16/783; G06F16/7847; G11B27/34
Claims Analysis
Total Claims: 26
Claim 1
Independent Claim
101 words
A method for detecting a cut in a video, comprising
the steps of: (a) acquiring video images from a source; (b) deriving from said video images a pixel-based
difference metric; (c) deriving from said video images a
distribution-based difference metric; (d) measuring video content of said video images to
provide up-to-date test criteria; (e) combining said pixel-based difference metric and
said distribution-based difference metric, taking
into account said up-to-date test criteria
provided in step (d) so as to derive a scene
change candidate signal; and (f) filtering said scene change candidate signal so
as to generate a scene change frame list.
Claim 2
Dependent Claim
42 words
A method for detecting a cut in a video in accordance
with claim 1, wherein said pixel-based difference metric
for each frame is the summation of an absolute frame
difference representative of image intensity value at
selected pixel locations in a frame.
Claim 3
Dependent Claim
63 words
A method for detecting a cut in a video in accordance
with claim 1, wherein said pixel-based difference metric
for each frame t is the sum of an absolute frame
difference, ij ¦ f t ij - f t -1 ij ¦ where f t / ij represents the intensity value at pixel location ( i , j )
in frame t .
Claim 4
Dependent Claim
51 words
A method for detecting a cut in a video in accordance
with claim 1, wherein each image is divided into a number
of sub-regions and wherein said distribution-based
difference metric is a Kolmogorov-Smirnov test metric,
except that one each is computed herein for the entire
image as well as its sub-regions.
Claim 5
Dependent Claim
53 words
A method for detecting a cut in a video in accordance
with claim 1, wherein each image is equally divided into
four sub-regions and wherein said distribution-based
difference metric is a Kolmogorov-Smirnov test metric,
except that one each is computed herein for the entire
image as well as said four equally divided sub-regions.
Claim 6
Dependent Claim
44 words
A method for detecting a cut in a video in accordance
with claim 1, wherein said step of measuring video content
of said video images to provide up-to-date test criteria
provides said step (e) with the ability to automatically
adjust to different video contents.
Claim 7
Dependent Claim
37 words
A method for detecting a cut in a video in accordance
with claim 1, wherein said video images are DC images
represented by the base frequency in the Discrete Cosine
Transform coefficients characterizing the underlying image
frame.
Claim 8
Dependent Claim
75 words
A method for detecting a cut in a video in accordance
with claim 1, wherein said step of measuring video content
of said video images to provide up-to-date test criteria
comprises collecting statistics from each DC image and each
pair of DC images to represent current video content, being
an image contrast and the motion estimate. The image
contrast estimate is computed based on a recursive scheme
to suppress the influences of sudden lighting changes.
Claim 9
Dependent Claim
42 words
A method for detecting a cut in a video in accordance
with claim 1, wherein said collecting statistics from each
DC image and each pair of DC images to represent current
video content represent an image contrast estimate and a
motion estimate.
Claim 10
Dependent Claim
34 words
A method for detecting a cut in a video in accordance
with claim 1, wherein said image contrast estimate is
computed based on a recursive scheme to suppress the
influences of sudden lighting changes.
Claim 11
Dependent Claim
53 words
A method for detecting a cut in a video in accordance
with claim 8, wherein said image contrast estimate is
derived in accordance with the following: contrast t =(1-τ) contrast t -1 +τσ t -1 where σ t -1 is the intensity variance estimate of the DC
image at time t -1 .
Claim 12
Dependent Claim
21 words
A method for detecting a cut in a video in accordance
with claim 8, wherein said image contrast estimate equals
0.6.
Claim 13
Dependent Claim
78 words
A method for detecting a cut in a video in accordance
with claim 8, wherein said motion estimate is computed as
follows: motion t =(1-τ) motion t -1 +τ ij ( ¦ f ij t -1 - f ij t -2 ¦ N ) where f t -1 / ij is the intensity value at pixel location
( i , j ) of the DC image at time t -1 , N is the size of
the image.
Claim 14
Dependent Claim
18 words
A method for detecting a cut in a video in accordance
with claim 11, wherein τ equals 0.6.
Claim 15
Dependent Claim
83 words
A method for detecting a cut in a video in accordance
with claim 8, wherein said image contrast and said motion
estimates are applied to a fuzzy engine to compute a new
significance level for the hierarchical Kolmogorov-Smirnov
test, said fuzzy engine using a quadratic membership
function, where each contrast measurement is divided into
classes, from low to high, and each motion estimate is
divided into classes, from slow to fast, and each
significance level is divided into classes from high to
low.
Claim 16
Dependent Claim
77 words
A method for detecting a cut in a video in accordance
with claim 15, wherein each contrast measurement is divided
into four classes, low, middle, high, and extremely high,
each motion estimate into three classes, slow, middle, and
fast, and each significance level into five classes, high,
middle high, middle, middle low, and low, and wherein the
fuzzy rules are stated in a simple IF/THEN format, where
values are combined using AND (minimum) or OR (maximum)
operations.
Claim 17
Dependent Claim
52 words
A method for detecting a cut in a video in accordance
with claim 16, including *a step of defuzzifying said fuzzy
rules to yield a crisp final output value, by finding the
center of gravity of the combined output shape, whereby all
rules are ensured of contributing to the final crisp
result.
Claim 18
Dependent Claim
72 words
A method for detecting a cut in a video in accordance
with claim 1, wherein in said step (e) of combining said
pixel-based difference metric and said distribution-based
difference metric, taking into account said up-to-date test
criteria provided in step (d) so as to derive a scene
change candidate signal, said pixel-based difference
metrics are treated as time series signals, where both
visually abrupt cuts and duplication of frames create
observation outliers.
Claim 19
Dependent Claim
148 words
A method for detecting a cut in a video in accordance
with claim 18, wherein said pixel-based difference metric
is treated as a time series signal, where both visually
abrupt cuts and the duplication of frames create
observation outliers obeying the equation d t = f ( d t - r , d t - r +1 ,···, d t ) + u t if t ≠ q d t = f ( d t - r , d t - r +1 ,···, d t ) + u t +Δ otherwise where t represents the time index, Δ is the outlier, f ( d t - r , d t - r +1 ,···, d t ) models the trend in the series, and u t = r =1 p α r d t - r + z t ( t = p +1,···, n ) .
Claim 20
Dependent Claim
109 words
Apparatus for detecting a cut in a video, comprising: (a) means for acquiring video images from a source; (b) means for deriving from said video images a
pixel-based difference metric; (c) means for deriving from said video images a
distribution-based difference metric; (d) means for measuring video content of said video
images to provide up-to-date test criteria; (e) means for combining said pixel-based difference
metric and said distribution-based difference
metric, taking into account said up-to-date test
criteria provided in step (d) so as to derive a
scene change candidate signal; and (f) means for filtering said scene change candidate
signal so as to generate a scene change frame
list.
Claim 21
Dependent Claim
53 words
Apparatus for detecting a cut in a video in accordance
with claim 20, including means for presenting two
cross-section images of said video images, whereof one is
a horizontal cross section image in a horizontal direction
and the other is a vertical cross section image in a
vertical direction of the video volume.
Claim 22
Dependent Claim
43 words
Apparatus for detecting a cut in a video in accordance
with claim 21, wherein each cross-section image is
constructed by sampling one row (or column) from every
image, and reducing the amount of information from a
two-dimensional image to two one-dimensional image strips.
Claim 23
Dependent Claim
56 words
Apparatus for detecting a cut in a video in accordance
with claim 22, wherein said horizontal and vertical
cross-section images are combined into one image segmented
into two bands according to a list of detected shots,
whereby a level of abstraction is presented that is enough
to reveal whether there is a missed or misdetected shot.
Claim 24
Dependent Claim
55 words
Apparatus for detecting a cut in a video in accordance
with claim 20, including means for presenting at least two
cross-section images of said video images, whereof one is
a horizontal cross section image in a horizontal direction
and the other is a vertical cross section image in a
vertical direction of the video volume.
Claim 25
Dependent Claim
45 words
Apparatus for detecting a cut in a video in accordance
with claim 24, wherein each cross-section image is
constructed by sampling one row (or column) from every
image, and reducing the amount of information from a
two-dimensional image to a plurality of one-dimensional
image strips.
Claim 26
Dependent Claim
65 words
Apparatus for detecting a cut in a video in accordance
with claim 25, wherein said at least two cross-section
images, including said horizontal and vertical
cross-section images, are combined into one image segmented
into a plurality of bands according to a list of detected
shots, whereby a level of abstraction is presented that is
enough to reveal whether there is a missed or misdetected
shot.
References Cited (Backward Citations)
US5032905A
Cited
EP0615245A2
Cited
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