Abstract: In this paper, a novel deinterlacing algorithm is
proposed. The proposed algorithm approximates the distribution of the
luminance into a polynomial function. Instead of using one
polynomial function for all pixels, different polynomial functions are
used for the uniform, texture, and directional edge regions. The
function coefficients for each region are computed by matrix
multiplications. Experimental results demonstrate that the proposed
method performs better than the conventional algorithms.
Abstract: Flexible macroblock ordering (FMO), adopted in the
H.264 standard, allows to partition all macroblocks (MBs) in a frame
into separate groups of MBs called Slice Groups (SGs). FMO can not
only support error-resilience, but also control the size of video packets
for different network types. However, it is well-known that the number
of bits required for encoding the frame is increased by adopting FMO.
In this paper, we propose a novel algorithm that can reduce the bitrate
overhead caused by utilizing FMO. In the proposed algorithm, all MBs
are grouped in SGs based on the similarity of the transform
coefficients. Experimental results show that our algorithm can reduce
the bitrate as compared with conventional FMO.
Abstract: In this paper, we introduce a novel algorithm for object tracking in video sequence. In order to represent the object to be tracked, we propose a spatial color histogram model which encodes both the color distribution and spatial information. The object tracking from frame to frame is accomplished via center voting and back projection method. The center voting method has every pixel in the new frame to cast a vote on whereabouts the object center is. The back projection method segments the object from the background. The segmented foreground provides information on object size and orientation, omitting the need to estimate them separately. We do not put any assumption on camera motion; the proposed algorithm works equally well for object tracking in both static and moving camera videos.