5,778 research outputs found
New Fast Block Matching Algorithm Using New Hybrid Search Pattern And Strategy To Improve Motion Estimation Process In Video Coding Technique
Up until today, video compression algorithm has been applied in various video applications ranging from video conferencing to video telephony. Motion Estimation or ME is deemed as one of the effective and popular techniques in video compression. As one of its techniques, the Block Matching Algorithm or BMA is widely employed in majority of well-known video codes due to its simplicity and high compression efficiency. As such, it is crucial to find different approaches of fast BMAs as the simplest and straightforward BMA is not a good fit for implementation of real-time video coding because of its high computational complexity. The aims for this study is to develop and design a new hybrid search pattern and strategy for new fast BMAs that can further improve the ME process in terms of estimation accuracy and video image quality, searching speed and computational complexity. There are 6 main designs that the algorithms proposed namely the Orthogonal-Diamond Search Algorithm with Small Diamond Search Pattern (ODS-SDSP), the Orthogonal-Diamond Search Algorithm with Large Diamond Search Pattern (ODS-LDSP), the Diamond-Orthogonal Search Algorithm with Small Diamond Pattern (DOS-SDSP), the Diamond-Orthogonal Search Algorithm with Large Diamond Pattern (DOS-LDSP), the Modified Diamond-Orthogonal Search Algorithm with Small Diamond Pattern (MDOS-SDSP), and the Modified Diamond-Orthogonal Search Algorithm with Large Diamond Pattern (MDOS-LDSP). These 6 algorithms are divided into 3 main methods namely Method A, Method B, and Method C depending on their search patterns and strategies. The first method involves the manipulation of the diamond pattern in the process, the second method includes the manipulation of the orthogonal steps, and lastly, the third method is the modified version of the second method to improve the performances of the algorithms. Evaluation is based on the algorithm performances in terms of the search points needed to find the final motion vector, the Peak-Signal to Noise Ratio (PSNR) of the algorithms, and the runtime performance of algorithm simulations. The result shows that the DOS-SDSP algorithm has the lowest search points with only 1.7341, 4.9059 and 4.0230 for each motion’s content respectively; meanwhile all the algorithms acquired similar and close PSNR values for all types of motion contents. As for simulation runtime, the results show that Method B has the least simulation runtime and Method C has the highest simulation runtime compared to others for all video sequences. The finding suggests that an early termination technique should be implemented at the early stage of the process, and mixing the selection of the mode is able to improve the algorithm performances. Therefore, it can be concluded that Method B gives the best performance in terms of search points reduction and simulation runtime while Method C yields the best for PSNR values for all types of motion contents
Motion estimation with chessboard pattern prediction strategy
Due to high correlations among the adjacent blocks, several algorithms utilize movement information of spatially and temporally correlated neighboring blocks to adapt their search patterns to that information. In this paper, this information is used to define a dynamic search pattern. Each frame is divided into two sets, black and white blocks, like a chessboard pattern and a different search pattern, is defined for each set. The advantage of this definition is that the number of spatially neighboring blocks is increased for each current block and it leads to a better prediction for each block. Simulation results show that the proposed algorithm is closer to the Full-Search algorithm in terms of quality metrics such as PSNR than the other state-of-the-art algorithms while at the same time the average number of search points is less.info:eu-repo/semantics/publishedVersio
Coarse-to-Fine Adaptive People Detection for Video Sequences by Maximizing Mutual Information
Applying people detectors to unseen data is challenging since patterns distributions, such
as viewpoints, motion, poses, backgrounds, occlusions and people sizes, may significantly differ
from the ones of the training dataset. In this paper, we propose a coarse-to-fine framework to adapt
frame by frame people detectors during runtime classification, without requiring any additional
manually labeled ground truth apart from the offline training of the detection model. Such adaptation
make use of multiple detectors mutual information, i.e., similarities and dissimilarities of detectors
estimated and agreed by pair-wise correlating their outputs. Globally, the proposed adaptation
discriminates between relevant instants in a video sequence, i.e., identifies the representative frames
for an adaptation of the system. Locally, the proposed adaptation identifies the best configuration
(i.e., detection threshold) of each detector under analysis, maximizing the mutual information to
obtain the detection threshold of each detector. The proposed coarse-to-fine approach does not
require training the detectors for each new scenario and uses standard people detector outputs, i.e.,
bounding boxes. The experimental results demonstrate that the proposed approach outperforms
state-of-the-art detectors whose optimal threshold configurations are previously determined and
fixed from offline training dataThis work has been partially supported by the Spanish government under the project TEC2014-53176-R
(HAVideo
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Research and developments of Dirac video codec
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.In digital video compression, apart from storage, successful transmission of the compressed video
data over the bandwidth limited erroneous channels is another important issue. To enable a video
codec for broadcasting application, it is required to implement the corresponding coding tools (e.g.
error-resilient coding, rate control etc.). They are normally non-normative parts of a video codec and
hence their specifications are not defined in the standard. In Dirac as well, the original codec is
optimized for storage purpose only and so, several non-normative part of the encoding tools are still
required in order to be able to use in other types of application.
Being the "Research and Developments of the Dirac Video Codec" as the research title, phase I of
the project is mainly focused on the error-resilient transmission over a noisy channel. The error-resilient
coding method used here is a simple and low complex coding scheme which provides the
error-resilient transmission of the compressed video bitstream of Dirac video encoder over the packet
erasure wired network. The scheme combines source and channel coding approach where error-resilient
source coding is achieved by data partitioning in the wavelet transformed domain and
channel coding is achieved through the application of either Rate-Compatible Punctured
Convolutional (RCPC) Code or Turbo Code (TC) using un-equal error protection between header plus
MV and data. The scheme is designed mainly for the packet-erasure channel, i.e. targeted for the
Internet broadcasting application.
But, for a bandwidth limited channel, it is still required to limit the amount of bits generated from
the encoder depending on the available bandwidth in addition to the error-resilient coding. So, in the
2nd phase of the project, a rate control algorithm is presented. The algorithm is based upon the Quality
Factor (QF) optimization method where QF of the encoded video is adaptively changing in order to
achieve average bitrate which is constant over each Group of Picture (GOP). A relation between the
bitrate, R and the QF, which is called Rate-QF (R-QF) model is derived in order to estimate the
optimum QF of the current encoding frame for a given target bitrate, R.
In some applications like video conferencing, real-time encoding and decoding with minimum
delay is crucial, but, the ability to do real-time encoding/decoding is largely determined by the
complexity of the encoder/decoder. As we all know that motion estimation process inside the encoder
is the most time consuming stage. So, reducing the complexity of the motion estimation stage will
certainly give one step closer to the real-time application. So, as a partial contribution toward realtime
application, in the final phase of the research, a fast Motion Estimation (ME) strategy is designed
and implemented. It is the combination of modified adaptive search plus semi-hierarchical way of
motion estimation. The same strategy was implemented in both Dirac and H.264 in order to
investigate its performance on different codecs. Together with this fast ME strategy, a method which
is called partial cost function calculation in order to further reduce down the computational load of the
cost function calculation was presented. The calculation is based upon the pre-defined set of patterns
which were chosen in such a way that they have as much maximum coverage as possible over the
whole block.
In summary, this research work has contributed to the error-resilient transmission of compressed
bitstreams of Dirac video encoder over a bandwidth limited error prone channel. In addition to this,
the final phase of the research has partially contributed toward the real-time application of the Dirac
video codec by implementing a fast motion estimation strategy together with partial cost function
calculation idea.BBC R&D and Brunel University
An improved block matching algorithm for motion estimation invideo sequences and application in robotics
Block Matching is one of the most efficient techniques for motion estimation for video sequences. Metaheuristic algorithms have been used effectively for motion estimation. In this paper, we propose two hybrid algorithms: Artificial Bee Colony with Differential Evolution and Harmony Search with Differential Evolution based motion estimation algorithms. Extensive experiments are conducted using four standard video sequences. The video sequences utilized for experimentation have all essential features such as different formats, resolutions and number of frames which are generally required in input video sequences. We compare the performance of the proposed algorithms with other algorithms considering various parameters such as Structural Similarity, Peak Signal to Noise Ratio, Average Number of Search Points etc. The comparative results demonstrate that the proposed algorithms outperformed other algorithms
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
An improved block matching algorithm for motion estimation in video sequences and application in robotics
Block Matching is one of the most efficient techniques for motion estimation for video sequences. Metaheuristic algorithms have been used effectively for motion estimation. In this paper, we propose two hybrid algorithms: Artificial Bee Colony with Differential Evolution and Harmony Search with Differential Evolution based motion estimation algorithms. Extensive experiments are conducted using four standard video sequences. The video sequences utilized for experimentation have all essential features such as different formats, resolutions and number of frames which are generally required in input video sequences. We compare the performance of the proposed algorithms with other algorithms considering various parameters such as Structural Similarity, Peak Signal to Noise Ratio, Average Number of Search Points etc. The comparative results demonstrate that the proposed algorithms outperformed other algorithms
Efficient HEVC-based video adaptation using transcoding
In a video transmission system, it is important to take into account the great diversity of the network/end-user constraints. On the one hand, video content is typically streamed over a network that is characterized by different bandwidth capacities. In many cases, the bandwidth is insufficient to transfer the video at its original quality. On the other hand, a single video is often played by multiple devices like PCs, laptops, and cell phones. Obviously, a single video would not satisfy their different constraints.
These diversities of the network and devices capacity lead to the need for video adaptation techniques, e.g., a reduction of the bit rate or spatial resolution. Video transcoding, which modifies a property of the video without the change of the coding format, has been well-known as an efficient adaptation solution. However, this approach comes along with a high computational complexity, resulting in huge energy consumption in the network and possibly network latency.
This presentation provides several optimization strategies for the transcoding process of HEVC (the latest High Efficiency Video Coding standard) video streams. First, the computational complexity of a bit rate transcoder (transrater) is reduced. We proposed several techniques to speed-up the encoder of a transrater, notably a machine-learning-based approach and a novel coding-mode evaluation strategy have been proposed. Moreover, the motion estimation process of the encoder has been optimized with the use of decision theory and the proposed fast search patterns. Second, the issues and challenges of a spatial transcoder have been solved by using machine-learning algorithms. Thanks to their great performance, the proposed techniques are expected to significantly help HEVC gain popularity in a wide range of modern multimedia applications
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