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IEEE SIGNAL PROCESSING MAGAZINE [179] MARCH 2015
Deblocking filtering aims to remove
the blocking artifacts caused by block
transform and quantization. The basic unit
for the deblocking filter is an 8 # 8 block.
For each 8 # 8 block, the deblocking filter
is used only if the boundary belongs to
either of the CU, PU, or TU boundaries.
After the deblocking filter, an SAO fil-
ter is applied to reduce the mean sample
distortion of a region, where an offset is
added to the reconstructed sample to
reduce ringing artifacts and contouring
artifacts. There are two kinds of offset:
edge offset (EO) and band offset (BO)
mode. For the EO mode, the encoder can
select and signal a vertical, horizontal,
downward-diagonal, or upward-diagonal
filtering direction. For BO mode, an off-
set value that directly depends on the
amplitudes of the reconstructed samples
is added to the reconstructed samples.
ALF is the last stage of in-loop filtering.
There are two stages in this process. The
first stage is filter coefficient derivation. To
train the filter coefficients, the encoder
classifies reconstructed pixels of the lumi-
nance component into 16 categories, and
one set of filter coefficients is trained for
each category using Wiener–Hopf equa-
tions to minimize the mean squared error
between the original frame and the recon-
structed frame. To reduce the redundancy
between these 16 sets of filter coefficients,
the encoder will adaptively merge them
based on the rate-distortion performance.
At its maximum, 16 different filter sets can
be assigned for the luminance component
and only one for the chrominance compo-
nents. The second stage is a filter decision,
which includes both the frame level and
LCU level. First, the encoder decides
whether frame-level adaptive loop filtering
is performed. If frame level ALF is on, then
the encoder further decides whether the
LCU level ALF is performed.
SMART SCENE VIDEO CODING
More and more videos being captured in
specific scenes (such as surveillance video
and videos from the classroom, home,
courthouse, etc.) are characterized by a
temporally stable background. The redun-
dancy originating from the background
could be further reduced. AVS2 developed
a background picture model-based coding
method [20], which is illustrated in
Figure 9. G-pictures and S-pictures are
defined to further exploit the temporal
redundancy and facilitate video event gen-
eration such as object segmentation and
motion detection. The G-picture is a spe-
cial I-picture, which is stored in a separate
background memory. The S-picture is a
special P-picture, which can be only pre-
dicted from a reconstructed G-picture or a
virtual G-picture, which does not exist in
the actual input sequence but is modeled
from input pictures and encoded into the
stream to act as a reference picture.
The G-picture is initialized by back-
ground initialization and updated by
background modeling with methods such
as median filtering, fast implementation
Raw
Video
DCT&Q
Entropy
Coding
Bit
Stream
G-Picture
Initialization
Background
Modeling
Decoder
IQ and IDCT
Reconstruction
Buffer
Loop Filter
Reference
Memory
Background
Memory
–
+
ME
Background
Reference Selection
Background
Difference Prediction
MC/
Intraprediction
S-Picture Decision
[FIG9] A background picture-based scene coding in AVS2.
[FIG10] Examples of the background picture and the difference frame between the original picture and the background picture:
(a) original picture, (b) difference frame, and (c) background picture.
(a) (b) (c)
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