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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
A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding
Fractal compression is the lossy compression technique in the field of gray/color image and video compression. It gives high compression ratio, better image quality with fast decoding time but improvement in encoding time is a challenge. This review paper/article presents the analysis of most significant existing approaches in the field of fractal based gray/color images and video compression, different block matching motion estimation approaches for finding out the motion vectors in a frame based on inter-frame coding and intra-frame coding i.e. individual frame coding and automata theory based coding approaches to represent an image/sequence of images. Though different review papers exist related to fractal coding, this paper is different in many sense. One can develop the new shape pattern for motion estimation and modify the existing block matching motion estimation with automata coding to explore the fractal compression technique with specific focus on reducing the encoding time and achieving better image/video reconstruction quality. This paper is useful for the beginners in the domain of video compression
A Novel Adaptive Search Range Algorithm for Motion Estimation Based on H.264
Motion estimation (ME) is very vital to video compression. Due to the adoption of the high precision of motion vector (MV) in H.264 encoder, the computational cost increases rapidly, and ME takes about 60% of the whole encoding time. In order to accommodate the new variable block size motion estimation strategy adopted in H.264, this paper proposes a novel adaptive search range(ASR) algorithm as a optimized part based on UMHexagonS. Not only we utilize the median_MVP and interframe information in our ASR algorithm but also a penalty function is included. Experimental results indicate that our proposed method reduces the computational complexity in a certain degree and enhances encoding efficiency but has few changes in the reconstructed image quality and bit rate
Statistical framework for video decoding complexity modeling and prediction
Video decoding complexity modeling and prediction is an increasingly important issue for efficient resource utilization in a variety of applications, including task scheduling, receiver-driven complexity shaping, and adaptive dynamic voltage scaling. In this paper we present a novel view of this problem based on a statistical framework perspective. We explore the statistical structure (clustering) of the execution time required by each video decoder module (entropy decoding, motion compensation, etc.) in conjunction with complexity features that are easily extractable at encoding time (representing the properties of each module's input source data). For this purpose, we employ Gaussian mixture models (GMMs) and an expectation-maximization algorithm to estimate the joint execution-time - feature probability density function (PDF). A training set of typical video sequences is used for this purpose in an offline estimation process. The obtained GMM representation is used in conjunction with the complexity features of new video sequences to predict the execution time required for the decoding of these sequences. Several prediction approaches are discussed and compared. The potential mismatch between the training set and new video content is addressed by adaptive online joint-PDF re-estimation. An experimental comparison is performed to evaluate the different approaches and compare the proposed prediction scheme with related resource prediction schemes from the literature. The usefulness of the proposed complexity-prediction approaches is demonstrated in an application of rate-distortion-complexity optimized decoding
Segmentation-based mesh design for motion estimation
Dans la plupart des codec vidéo standard, l'estimation des mouvements entre deux images se fait généralement par l'algorithme de concordance des blocs ou encore BMA pour « Block Matching Algorithm ». BMA permet de représenter l'évolution du contenu des images en décomposant normalement une image par blocs 2D en mouvement translationnel. Cette technique de prédiction conduit habituellement à de sévères distorsions de 1'artefact de bloc lorsque Ie mouvement est important. De plus, la décomposition systématique en blocs réguliers ne dent pas compte nullement du contenu de l'image. Certains paramètres associes aux blocs, mais inutiles, doivent être transmis; ce qui résulte d'une augmentation de débit de transmission. Pour paillier a ces défauts de BMA, on considère les deux objectifs importants dans Ie codage vidéo, qui sont de recevoir une bonne qualité d'une part et de réduire la transmission a très bas débit d'autre part. Dans Ie but de combiner les deux exigences quasi contradictoires, il est nécessaire d'utiliser une technique de compensation de mouvement qui donne, comme transformation, de bonnes caractéristiques subjectives et requiert uniquement, pour la transmission, l'information de mouvement. Ce mémoire propose une technique de compensation de mouvement en concevant des mailles 2D triangulaires a partir d'une segmentation de l'image. La décomposition des mailles est construite a partir des nœuds repartis irrégulièrement Ie long des contours dans l'image. La décomposition résultant est ainsi basée sur Ie contenu de l'image. De plus, étant donné la même méthode de sélection des nœuds appliquée à l'encodage et au décodage, la seule information requise est leurs vecteurs de mouvement et un très bas débit de transmission peut ainsi être réalise. Notre approche, comparée avec BMA, améliore à la fois la qualité subjective et objective avec beaucoup moins d'informations de mouvement. Dans la premier chapitre, une introduction au projet sera présentée. Dans Ie deuxième chapitre, on analysera quelques techniques de compression dans les codec standard et, surtout, la populaire BMA et ses défauts. Dans Ie troisième chapitre, notre algorithme propose et appelé la conception active des mailles a base de segmentation, sera discute en détail. Ensuite, les estimation et compensation de mouvement seront décrites dans Ie chapitre 4. Finalement, au chapitre 5, les résultats de simulation et la conclusion seront présentés.Abstract: In most video compression standards today, the generally accepted method for temporal prediction is motion compensation using block matching algorithm (BMA). BMA represents the scene content evolution with 2-D rigid translational moving blocks. This kind of predictive scheme usually leads to distortions such as block artefacts especially when the motion is important. The two most important aims in video coding are to receive a good quality on one hand and a low bit-rate on the other. This thesis proposes a motion compensation scheme using segmentation-based 2-D triangular mesh design method. The mesh is constructed by irregularly spread nodal points selected along image contour. Based on this, the generated mesh is, to a great extent, image content based. Moreover, the nodes are selected with the same method on the encoder and decoder sides, so that the only information that has to be transmitted are their motion vectors, and thus very low bit-rate can be achieved. Compared with BMA, our approach could improve subjective and objective quality with much less motion information."--Résumé abrégé par UM
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