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SegmentationBased MotionCompensated Video Coding Using Morphological Filters 1997

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Title: SegmentationBased MotionCompensated Video Coding Using Morphological Filters 1997


1
Segmentation-Based Motion-Compensated Video
Coding Using Morphological Filters (1997)
  • Paper by
  • Demin Wang
  • Claude Labit
  • Joseph Ronsin
  • Presentation by
  • Jayson Smith

2
Overview
  • The paper presents a system for encoding video

The paper presents a system for encoding video
3
Morphological FiltersReview
  • Morphology operates on pixels as a set.
  • The dilation and erosion operations are two
    fundamental operations which use a structuring
    element to create a new set from an existing set.

Fig Gonzalas
4
Morphological FiltersReview (cont)
Opening A by B is the erosion of A by B, followed
by the dilation of the result by B. Closing A by
B is the dilation of A by B, followed by the
erosion of the result by B.
Fig Gonzalas
5
Morphological FiltersReview (cont)
Fig Vincent 6
2D morphological filters for binary images can be
extended to grayscale images by treating the
grayscale as stacks of binary sets.
6
Morphological FiltersReview (cont)
  • Reconstruction is an iteration of the following
    operations
  • Dilate the marker set
  • Mask (logical AND) with the reference set.
  • Process continues until stable.

Fig Vincent 6
7
Morphological FiltersReview (cont)
Fig Vincent 6
Although the prior reconstruction example refers
to binary images, it can be extended to grayscale
as shown above. Here, the reference f is
reconstruted from the marker g. This is called
reconstruction by dilation. Swapping the meanings
of f and g and then eroding the marker will
produce reconstruction by erosion.
8
Region Simplification
  • There are two key problems in using morphological
    filters for images
  • Small regions with high contrast must be
    preserved.
  • There is a tradeoff in structuring elements
    large ones will eliminate important features,
    while small ones will not simplify the image.
  • A hierarchy of structuring elements helps
    alleviate these problems.

9
Region Simplification
  • Spatial Simplification algorithm
  • f(r) frame to be encoded, with r(x,y)
  • ?(f) open/closing by reconstruction of f(r)
    with largest structuring element B0 (12x12
    square)
  • Residual of f(r)- ?(f) is formed, split into
    light and dark
  • Residuals are thresholded to T (8 in experiments)

10
Region Simplification
  • Spatial Simplification algorithm (continued)
  • Perform opening by reconstruction on each
    residual
  • ?B1(gi) is the opening of gi with B1 (6x6 square)
    and ?(rec) (X,Y) is the reconstruction by
    dilation of X under reference Y.
  • f1 ?(f) C1 C2, restoring to ?(f) the
    important high-constrast areas that were lost in
    filtering.
  • Subtract C1 and C2 from G1 and G2, and re-apply
    thresholding and reconstruction using B2 (3x3
    square). Re-apply them to f1.

11
Region Simplification
  • Motion-Preserving Simplification
  • Moving components between two frames appear as
    linear structures on component edges when taking
    the difference between the frames.
  • Algorithm
  • This is the pointwise maximum, and the Bi are
    length-five linear structuring elements
    horizontal, vertical, and both diagonals.
  • f2 is then thresholded by T to remove inter-frame
    noise (luminous disturbance)
  • Create these images

12
Region Simplification
Motion-Preserving Simplification algorithm
(continued)
f3 recovers moving dark, f4 removes dark
13
Image Segmentation
Four steps region growing, region refinement,
region merging, small region elimination
14
Image Segmentation
  • Region growing flat regions of f4 larger than
    3x3 are seeded. Neighboring pixels are added to
    the region if their gray levels are within a low
    threshold. Threshold is increased and process is
    iterated until all pixels are assigned.
  • Region refinement the regions defined above are
    checked against their gray level means, and
    considered homogenous if all pixels deviate from
    the mean by ltT. If not, the regions are split
    until all regions are homogenous.

15
Image Segmentation
  • Region merging the edges around region
    boundaries are checked if the contrast is less
    than T/2, the regions are merged.
  • Small region elimination regions smaller than
    0.4 of the total image area are eliminated.

16
Region Merging by Motion
Pairs of regions are merged if their compensation
errors are similar.
17
DFD Segmentation
The displaced frame difference (DFD) is the
residual of the motion-compensated image the
error left-over once motion has been encoded.
Generally, it has some linear structures and
small spots on a background of 0s.
18
DFD Segmentation
  • Algorithm
  • Form positive and negative indicator images with
    thresholding
  • Open each image with the 4 linear pieces (length
    5), then reconstruct the indicator image
  • This should remove small spots, but could leave
    small holes. The indicator is closed to remove
    these holes using a 3x3 structuring element.
  • Segment each region by the gray-level means,
    forming first reconstruction of D(r).

19
DFD Segmentation
  • Algorithm (continued)
  • The reconstruction error image is iteratively
    segmented in the same fashion until all regions
    are found.
  • Then, the regions are merged regions smaller
    than 0.04 of the size are merged into their most
    similar regions, and adjacent regions with gray
    level mean differences below some threshold are
    merged.

20
Conclusions
  • Encoding method shows segmentation-based video
    compression, as opposed to the more ubiquitous
    texture-based.
  • Method relies heavily on morphological filters.
  • The encoding scheme achieves similar rates to
    that of texture-based encoders
  • (Foreman clip has bit rate of 256kbps, SNR of
    33.12dB in MPEG4, compared with 207kbps, SNR of
    30.11dB in this paper, according to CIF 30fps
    clip in Liang)
  • Resources
  • Digital Image Processing 2nd Ed., R. Gonzalez and
    R. Woods, 2001
  • Morphological Grayscale Reconstruction..., L.
    Vincent, 1993
  • Methods and Needs for Transcoding MPEG-4..., Y.
    Liang, 2002
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