1,986 research outputs found
Extended Successive Elimination Algorithm for Fast Optimal Block Matching Motion Estimation
In this paper, we propose an extended successive elimination algorithm (SEA) for fast optimal block matching motion estimation (ME). By reinterpreting the typical sum of absolute differences measure, we can obtain additional decision criteria whether to discard the impossible candidate motion vectors. Experimental results show that the proposed algorithm reduces the computational complexity up to 19.85% on average comparing with the multilevel successive elimination algorithm. The proposed algorithm can be used with other SEA to improve the ME performance
New results on exhaustive search algorithm for motion estimation using adaptive partial distortion search and successive elimination algorithm
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A Review Paper On Motion Estimation Techniques
Motion estimation (ME) is a primary action for video compression. Actually, it leads to heavily to the compression efficiency by eliminating temporal redundancies. This approach is one among the critical part in a video encoder and can take itself greater than half of the coding complexity or computational coding time. Several fast ME algorithms were proposed as well as realized. In this paper, we offers a brief review on various motion estimation techniques mainly block matching motion estimation techniques. The paper additionally presents a very brief introduction to the whole flow of video motion vector calculation
Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)
Block matching (BM) motion estimation plays a very important role in video
coding. In a BM approach, image frames in a video sequence are divided into
blocks. For each block in the current frame, the best matching block is
identified inside a region of the previous frame, aiming to minimize the sum of
absolute differences (SAD). Unfortunately, the SAD evaluation is
computationally expensive and represents the most consuming operation in the BM
process. Therefore, BM motion estimation can be approached as an optimization
problem, where the goal is to find the best matching block within a search
space. The simplest available BM method is the full search algorithm (FSA)
which finds the most accurate motion vector through an exhaustive computation
of SAD values for all elements of the search window. Recently, several fast BM
algorithms have been proposed to reduce the number of SAD operations by
calculating only a fixed subset of search locations at the price of poor
accuracy. In this paper, a new algorithm based on Artificial Bee Colony (ABC)
optimization is proposed to reduce the number of search locations in the BM
process. In our algorithm, the computation of search locations is drastically
reduced by considering a fitness calculation strategy which indicates when it
is feasible to calculate or only estimate new search locations. Since the
proposed algorithm does not consider any fixed search pattern or any other
movement assumption as most of other BM approaches do, a high probability for
finding the true minimum (accurate motion vector) is expected. Conducted
simulations show that the proposed method achieves the best balance over other
fast BM algorithms, in terms of both estimation accuracy and computational
cost.Comment: 22 Pages. arXiv admin note: substantial text overlap with
arXiv:1405.4721, arXiv:1406.448
Block Matching Algorithms for the Estimation of Motion in Image Sequences: Analysis
Several video coding standards and techniques have been introduced for multimedia applications, particularly the h.26x series for video processing. These standards employ motion estimation processing to reduce the amount of data that is required to store or transmit the video. The motion estimation process is an inextricable part of the video coding as it removes the temporal redundancy between successive frames of video sequences. This paper is about these motion estimation algorithms, their search procedures, complexity, advantages, and limitations. A survey of motion estimation algorithms including full search, many fast, and fast full search block-based algorithms has been presented. An evaluation of up-to-date motion estimation algorithms, based on several empirical results on several test video sequences, is presented as well
Surveillance centric coding
PhDThe research work presented in this thesis focuses on the development of techniques
specific to surveillance videos for efficient video compression with higher processing
speed. The Scalable Video Coding (SVC) techniques are explored to achieve higher
compression efficiency. The framework of SVC is modified to support Surveillance
Centric Coding (SCC). Motion estimation techniques specific to surveillance videos
are proposed in order to speed up the compression process of the SCC.
The main contributions of the research work presented in this thesis are divided into
two groups (i) Efficient Compression and (ii) Efficient Motion Estimation. The
paradigm of Surveillance Centric Coding (SCC) is introduced, in which coding aims
to achieve bit-rate optimisation and adaptation of surveillance videos for storing and
transmission purposes. In the proposed approach the SCC encoder communicates
with the Video Content Analysis (VCA) module that detects events of interest in
video captured by the CCTV. Bit-rate optimisation and adaptation are achieved by
exploiting the scalability properties of the employed codec. Time segments
containing events relevant to surveillance application are encoded using high spatiotemporal
resolution and quality while the irrelevant portions from the surveillance
standpoint are encoded at low spatio-temporal resolution and / or quality. Thanks to
the scalability of the resulting compressed bit-stream, additional bit-rate adaptation is
possible; for instance for the transmission purposes. Experimental evaluation showed
that significant reduction in bit-rate can be achieved by the proposed approach
without loss of information relevant to surveillance applications.
In addition to more optimal compression strategy, novel approaches to performing
efficient motion estimation specific to surveillance videos are proposed and
implemented with experimental results. A real-time background subtractor is used to
detect the presence of any motion activity in the sequence. Different approaches for
selective motion estimation, GOP based, Frame based and Block based, are
implemented. In the former, motion estimation is performed for the whole group of
pictures (GOP) only when a moving object is detected for any frame of the GOP.
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While for the Frame based approach; each frame is tested for the motion activity and
consequently for selective motion estimation. The selective motion estimation
approach is further explored at a lower level as Block based selective motion
estimation. Experimental evaluation showed that significant reduction in
computational complexity can be achieved by applying the proposed strategy. In
addition to selective motion estimation, a tracker based motion estimation and fast
full search using multiple reference frames has been proposed for the surveillance
videos.
Extensive testing on different surveillance videos shows benefits of
application of proposed approaches to achieve the goals of the SCC
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