9,625 research outputs found
Dynamically variable step search motion estimation algorithm and a dynamically reconfigurable hardware for its implementation
Motion Estimation (ME) is the most computationally intensive part of video compression and video enhancement systems. For the recently available High Definition (HD) video formats, the computational complexity of De full search (FS) ME algorithm is prohibitively high, whereas the PSNR obtained by fast search ME algorithms is low. Therefore, ill this paper, we present Dynamically Variable Step Search (DVSS) ME algorithm for Processing high definition video formats and a dynamically reconfigurable hardware efficiently implementing DVSS algorithm. The architecture for efficiently implementing DVSS algorithm. The simulation results showed that DVSS algorithm performs very close to FS algorithm by searching much fewer search locations than FS algorithm and it outperforms successful past search ME algorithms by searching more search locations than these algorithms. The proposed hardware is implemented in VHDL and is capable, of processing high definition video formats in real time. Therefore, it can be used in consumer electronics products for video compression, frame rate up-conversion and de-interlacing(1)
Optimization of the motion estimation for parallel embedded systems in the context of new video standards
15 pagesInternational audienceThe effciency of video compression methods mainly depends on the motion compensation stage, and the design of effcient motion estimation techniques is still an important issue. An highly accurate motion estimation can significantly reduce the bit-rate, but involves a high computational complexity. This is particularly true for new generations of video compression standards, MPEG AVC and HEVC, which involves techniques such as different reference frames, sub-pixel estimation, variable block sizes. In this context, the design of fast motion estimation solutions is necessary, and can concerned two linked aspects: a high quality algorithm and its effcient implementation. This paper summarizes our main contributions in this domain. In particular, we first present the HME (Hierarchical Motion Estimation) technique. It is based on a multi-level refinement process where the motion estimation vectors are first estimated on a sub-sampled image. The multi-levels decomposition provides robust predictions and is particularly suited for variable block sizes motion estimations. The HME method has been integrated in a AVC encoder, and we propose a parallel implementation of this technique, with the motion estimation at pixel level performed by a DSP processor, and the sub-pixel refinement realized in an FPGA. The second technique that we present is called HDS for Hierarchical Diamond Search. It combines the multi-level refinement of HME, with a fast search at pixel-accuracy inspired by the EPZS method. This paper also presents its parallel implementation onto a multi-DSP platform and the its use in the HEVC context
Parallel H.264/AVC Fast Rate-Distortion Optimized Motion Estimation using Graphics Processing Unit and Dedicated Hardware
Heterogeneous systems on a single chip composed of CPU, Graphical Processing Unit (GPU), and Field Programmable Gate Array (FPGA) are expected to emerge in near future. In this context, the System on Chip (SoC) can be dynamically adapted to employ different architectures for execution of data-intensive applications. Motion estimation is one such task that can be accelerated using FPGA and GPU for high performance H.264/AVC encoder implementation. In most of works on parallel implementation of motion estimation, the bit rate cost of motion vectors is generally ignored. On the contrary, this paper presents a fast rate-distortion optimized parallel motion estimation algorithm implemented on GPU using OpenCL and FPGA/ASIC using VHDL. The predicted motion vectors are estimated from temporally preceding motion vectors and used for evaluating the bit rate cost of the motion vectors simultaneously. The experimental results show that the proposed scheme achieves significant speedup on GPU and FPGA, and has comparable ratedistortion performance with respect to sequential fast motion estimation algorithm
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
Clouds Motion Estimation from Ground-Based Sky Camera and Satellite Images
Estimation of cloud motion is a challenging task due to the non-linear phenomena of cloud formation and deformation. Satellite images processing is a popular tool used to study the characteristics of clouds which constitute major factors in forecasting the meteorological parameters. Due to the low resolution of satellite images, researchers have turned towards analyzing the high-resolution images captured by ground-based sky cameras. The first objective of this chapter is to demonstrate the different techniques used to estimate clouds motion and to compare them with respect to the accuracy and the computational time. The second aim is to propose a fast and efficient block matching technique based on combining the two types of images. The first idea of our approach is to analyze the low-resolution satellite images to detect the direction of motion. Then, the direction is used to orient the search process to estimate the optimal motion vectors from the high-resolution ground-based sky images. The second idea of our method is to use the entropy technique to find the optimal block sizes. The third idea is to imply an adaptive cost function to perform the matching process. The comparative study demonstrates the high performance of the proposed method with regards to the robustness, the accuracy and the computation time
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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
Cost and Coding Efficient Motion Estimation Design Considerations for High Efficiency Video Coding (HEVC) Standard
This paper focuses on motion estimation engine design in future high-efficiency video coding (HEVC) encoders. First, a methodology is explained to analyze hardware implementation cost in terms of hardware area, memory size and memory bandwidth for various possible motion estimation engine designs. For 11 different configurations, hardware cost as well as the coding efficiency are quantified and are compared through a graphical analysis to make design decisions. It has been shown that using smaller block sizes (e.g. 4 × 4) imposes significantly larger hardware requirements at the expense of modest improvements in coding efficiency. Secondly, based on the analysis on various configurations, one configuration is chosen and algorithm improvements are presented to further reduce hardware implementation cost of the selected configuration. Overall, the proposed changes provide 56 × on-chip bandwidth, 151 × off-chip bandwidth, 4.3 × core area and 4.5 × on-chip memory area savings when compared to the hardware implementation of the HM-3.0 design.Texas Instruments Incorporate
Hardware-aware motion estimation search algorithm development for high-efficiency video coding (HEVC) standard
This work presents a hardware-aware search algorithm for HEVC motion estimation. Implications of several decisions in search algorithm are considered with respect to their hardware implementation costs (in terms of area and bandwidth). Proposed algorithm provides 3X logic area in integer motion estimation, 16% on-chip reference buffer area and 47X maximum off-chip bandwidth savings when compared to HM-3.0 fast search algorithm.Texas Instruments Incorporate
MPEG-4 Software Video Encoding
A Thesis submitted in fulfillment of the requirements of the degree of doctor of Philosophy in the University of LondonThis thesis presents a software model that allows a parallel decomposition of the
MPEG-4 video encoder onto shared memory architectures, in order to reduce its
total video encoding time.
Since a video sequence consists of video objects each of which is likely to have
different encoding requirements, the model incorporates a scheduler which
(a) always selects the most appropriate video object for encoding and,
(b) employs a mechanism for dynamically allocating video objects allocation onto
the system processors, based on video object size information.
Further spatial video object parallelism is exploited by applying the single program
multiple data (SPMD) paradigm within the different modules of the MPEG-4
video encoder. Due to the fact that not all macroblocks have the same processing
requirements, the model also introduces a data partition scheme that generates tiles
with identical processing requirements. Since, macroblock data dependencies
preclude data parallelism at the shape encoder the model also introduces a new
mechanism that allows parallelism using a circular pipeline macroblock technique
The encoding time depends partly on an encoder’s computational complexity. This
thesis also addresses the problem of the motion estimation, as its complexity has a
significant impact on the encoder’s complexity. In particular, two fast motion
estimation algorithms have been developed for the model which reduce the
computational complexity significantly. The thesis includes experimental results on a four processor shared memory
platform, Origin200
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