IEEE Consumer Electronics Magazine - September/October 2022 - 42

Digital Imaging and Display Technologies
will require the transmissionof more data. Greater
CPU power to process the 3-D signal for stereo TV
will also be needed. The overall cost of implementation
will be high. It would be useful, therefore, to
be able to convert 2-D images to 3-D. Hence, in this
study, we investigated a 2D-to-3D conversion technique
for stereo broadcasting. A major advantage
of this approach is the low cost of implementation,
as the signal can be broadcast in the same
manner as any other 2-D channel. 3-D TV not only
requires image gray data but also depth information.
In order to watch 3-D images, there are two
main methods, one with glasses and one without.
Based on depth-based image rendering (DBIR)1
theory, polaroid glasses can be worn to view a stereo
video, but it is inconvenient. Recently, an
advanced technique has been developed which
allows the viewer to watch a 3-D video without
using special glasses.2 The proposed technique
involves a novel optical system comprised of a
multi-image combining optical system that is
embedded in a conventional display.
The depth value can be calculated using various
algorithms, which affect the 3-D performance.
2D-to-3D image conversions have been
widely discussed in the past 10 years3-7. Example
3-D models have been proposed for car
models, chair models, plane models, monorail
models, and face models,5 which create a new 3D
model from scratch for each new object in an
image. Another approach, termed object-based
stereoscopic video generation, has been proposed,6
which is based on optical-flow occlusion
and region-growing method for object segmentation.
Furthermore, a proposed hybrid depth estimation
scheme using content-based matching
has been shown to improve performance. The
depth map is generated using a learning-based
method,7 which employs the nearest neighbor
concept and is pretrained on a clustered dataset.
In this study, we were interested in developing
a technique capable of displaying realtime
3-D video using a conventional TV channel.
First, we developed a low-complexity 3-D
depth generation algorithm. Based on the proposed
algorithm, a low-cost embedded system
was adopted to perform the 2D-to-3D conversion
system for real-time implementation.
Examples of 3-D videos generated in experiments
are provided.
42
PROPOSED 3-D CONVERSION
METHOD
To achieve a better stereo effect, the objects
in an image can be segmented. Then, according
to their locations, objects can be assigned their
own depth value. On a TV display, most objects
are moving. The motion of the continuous frames
can be calculated using the summation of the
absolute differential of each color between the
previous Nth frame and the current frame, where
the result is named Pði;jÞ
t
ces, the number of N increases to cover enough
of the data related to the moving objects.8 For
this adaptive processing, Pði;jÞ
t can be accumulated
for an entire frame, where the result is
APtN for the Nth frame. When the moving speed
is low, the estimated APtN would not be higher
than a threshold (TH). In this event, the object is
not segmented completely. Adaptive processing
is used to increase the number of N by one for
each procedure. The APtNvalue is calculated
until the APtNis higher than TH, to cover more
object information. A maximum of N¼ 4 is used
to limit the computational complexity. Finally,
the differential image Pði;jÞ
t
is truncated to a
binary for segmentation.
The processing flow of object segmentation
is shown in Figure 1. To reduce noise signal
interference with the object detection, the
adaptive truncated TH is adapted. First, the
local mean is calculated within a 35window,
where LMij is the local mean for fij pixel. The
moving object can be truncated to the binary
according to the adaptive TH Dði;jÞ
th ¼ 256
LMði;jÞw. One can adjust w to control the sensitivity
of object detection. When LMði;jÞis
higher, which indicates that there are some
object pixels within this window, Dði;jÞ
lower, and the current Pði;jÞ
th would be
t pixel would have a
higher probability of being truncated to a high
level. When Pði;jÞ
t 


IEEE Consumer Electronics Magazine - September/October 2022
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