2,773 research outputs found
Scalable video/image transmission using rate compatible PUM turbo codes
The robust delivery of video over emerging wireless networks poses many challenges due to the heterogeneity of access networks, the variations in streaming devices, and the expected variations in network conditions caused by interference and coexistence. The proposed approach exploits the joint optimization of a wavelet-based scalable video/image coding framework and a forward error correction method based on PUM turbo codes. The scheme minimizes the reconstructed image/video distortion at the decoder subject to a constraint on the overall transmission bitrate budget. The minimization is achieved by exploiting the rate optimization technique and the statistics of the transmission channel
Motion Scalability for Video Coding with Flexible Spatio-Temporal Decompositions
PhDThe research presented in this thesis aims to extend the scalability range of the
wavelet-based video coding systems in order to achieve fully scalable coding with a
wide range of available decoding points. Since the temporal redundancy regularly
comprises the main portion of the global video sequence redundancy, the techniques
that can be generally termed motion decorrelation techniques have a central role in
the overall compression performance. For this reason the scalable motion modelling
and coding are of utmost importance, and specifically, in this thesis possible
solutions are identified and analysed.
The main contributions of the presented research are grouped into two
interrelated and complementary topics. Firstly a flexible motion model with rateoptimised
estimation technique is introduced. The proposed motion model is based
on tree structures and allows high adaptability needed for layered motion coding. The
flexible structure for motion compensation allows for optimisation at different stages
of the adaptive spatio-temporal decomposition, which is crucial for scalable coding
that targets decoding on different resolutions. By utilising an adaptive choice of
wavelet filterbank, the model enables high compression based on efficient mode
selection. Secondly, solutions for scalable motion modelling and coding are
developed. These solutions are based on precision limiting of motion vectors and
creation of a layered motion structure that describes hierarchically coded motion.
The solution based on precision limiting relies on layered bit-plane coding of motion
vector values. The second solution builds on recently established techniques that
impose scalability on a motion structure. The new approach is based on two major
improvements: the evaluation of distortion in temporal Subbands and motion search
in temporal subbands that finds the optimal motion vectors for layered motion
structure.
Exhaustive tests on the rate-distortion performance in demanding scalable video
coding scenarios show benefits of application of both developed flexible motion
model and various solutions for scalable motion coding
Improved quality block-based low bit rate video coding.
The aim of this research is to develop algorithms for enhancing the subjective quality and coding efficiency of standard block-based video coders. In the past few years, numerous video coding standards based on motion-compensated block-transform structure have been established where block-based motion estimation is used for reducing the correlation between consecutive images and block transform is used for coding the resulting motion-compensated residual images. Due to the use of predictive differential coding and variable length coding techniques, the output data rate exhibits extreme fluctuations. A rate control algorithm is devised for achieving a stable output data rate. This rate control algorithm, which is essentially a bit-rate estimation algorithm, is then employed in a bit-allocation algorithm for improving the visual quality of the coded images, based on some prior knowledge of the images. Block-based hybrid coders achieve high compression ratio mainly due to the employment of a motion estimation and compensation stage in the coding process. The conventional bit-allocation strategy for these coders simply assigns the bits required by the motion vectors and the rest to the residual image. However, at very low bit-rates, this bit-allocation strategy is inadequate as the motion vector bits takes up a considerable portion of the total bit-rate. A rate-constrained selection algorithm is presented where an analysis-by-synthesis approach is used for choosing the best motion vectors in term of resulting bit rate and image quality. This selection algorithm is then implemented for mode selection. A simple algorithm based on the above-mentioned bit-rate estimation algorithm is developed for the latter to reduce the computational complexity. For very low bit-rate applications, it is well-known that block-based coders suffer from blocking artifacts. A coding mode is presented for reducing these annoying artifacts by coding a down-sampled version of the residual image with a smaller quantisation step size. Its applications for adaptive source/channel coding and for coding fast changing sequences are examined
Offline and Online Optical Flow Enhancement for Deep Video Compression
Video compression relies heavily on exploiting the temporal redundancy
between video frames, which is usually achieved by estimating and using the
motion information. The motion information is represented as optical flows in
most of the existing deep video compression networks. Indeed, these networks
often adopt pre-trained optical flow estimation networks for motion estimation.
The optical flows, however, may be less suitable for video compression due to
the following two factors. First, the optical flow estimation networks were
trained to perform inter-frame prediction as accurately as possible, but the
optical flows themselves may cost too many bits to encode. Second, the optical
flow estimation networks were trained on synthetic data, and may not generalize
well enough to real-world videos. We address the twofold limitations by
enhancing the optical flows in two stages: offline and online. In the offline
stage, we fine-tune a trained optical flow estimation network with the motion
information provided by a traditional (non-deep) video compression scheme, e.g.
H.266/VVC, as we believe the motion information of H.266/VVC achieves a better
rate-distortion trade-off. In the online stage, we further optimize the latent
features of the optical flows with a gradient descent-based algorithm for the
video to be compressed, so as to enhance the adaptivity of the optical flows.
We conduct experiments on a state-of-the-art deep video compression scheme,
DCVC. Experimental results demonstrate that the proposed offline and online
enhancement together achieves on average 12.8% bitrate saving on the tested
videos, without increasing the model or computational complexity of the decoder
side.Comment: 9 pages, 6 figure
REGION-BASED ADAPTIVE DISTRIBUTED VIDEO CODING CODEC
The recently developed Distributed Video Coding (DVC) is typically suitable for the
applications where the conventional video coding is not feasible because of its
inherent high-complexity encoding. Examples include video surveillance usmg
wireless/wired video sensor network and applications using mobile cameras etc. With
DVC, the complexity is shifted from the encoder to the decoder.
The practical application of DVC is referred to as Wyner-Ziv video coding (WZ)
where an estimate of the original frame called "side information" is generated using
motion compensation at the decoder. The compression is achieved by sending only
that extra information that is needed to correct this estimation. An error-correcting
code is used with the assumption that the estimate is a noisy version of the original
frame and the rate needed is certain amount of the parity bits. The side information is
assumed to have become available at the decoder through a virtual channel. Due to
the limitation of compensation method, the predicted frame, or the side information, is
expected to have varying degrees of success. These limitations stem from locationspecific
non-stationary estimation noise. In order to avoid these, the conventional
video coders, like MPEG, make use of frame partitioning to allocate optimum coder
for each partition and hence achieve better rate-distortion performance. The same,
however, has not been used in DVC as it increases the encoder complexity.
This work proposes partitioning the considered frame into many coding units
(region) where each unit is encoded differently. This partitioning is, however, done at
the decoder while generating the side-information and the region map is sent over to
encoder at very little rate penalty. The partitioning allows allocation of appropriate
DVC coding parameters (virtual channel, rate, and quantizer) to each region. The
resulting regions map is compressed by employing quadtree algorithm and
communicated to the encoder via the feedback channel. The rate control in DVC is
performed by channel coding techniques (turbo codes, LDPC, etc.). The performance
of the channel code depends heavily on the accuracy of virtual channel model that models estimation error for each region. In this work, a turbo code has been used and
an adaptive WZ DVC is designed both in transform domain and in pixel domain. The
transform domain WZ video coding (TDWZ) has distinct superior performance as
compared to the normal Pixel Domain Wyner-Ziv (PDWZ), since it exploits the
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spatial redundancy during the encoding. The performance evaluations show that the
proposed system is superior to the existing distributed video coding solutions.
Although the, proposed system requires extra bits representing the "regions map" to be
transmitted, fuut still the rate gain is noticeable and it outperforms the state-of-the-art
frame based DVC by 0.6-1.9 dB.
The feedback channel (FC) has the role to adapt the bit rate to the changing
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statistics between the side infonmation and the frame to be encoded. In the
unidirectional scenario, the encoder must perform the rate control. To correctly
estimate the rate, the encoder must calculate typical side information. However, the
rate cannot be exactly calculated at the encoder, instead it can only be estimated. This
work also prbposes a feedback-free region-based adaptive DVC solution in pixel
domain based on machine learning approach to estimate the side information.
Although the performance evaluations show rate-penalty but it is acceptable
considering the simplicity of the proposed algorithm.
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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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