Source coding, a central concept in information theory, is the study of encoding and decoding data. Depending on the topological structure of the sources, i.e. how the sources are connected with encoders, different rate distortion functions are used. In this thesis two different encoding schemes---distributed and decentralized---are discussed and compared with a benchmark (centralized) coding structure. Specifically, all structures for two and three sources are discussed and a special case for the multi-source (more than three sources) is calculated. This work gives a pathway to characterize the generalized multiterminal source coding systems by finding the difference in the rate distortion limits from the optimal centralized coding system. It is shown that in specific cases, some decentralized systems can achieve the Shannon lower bound in a high resolution regime. / Thesis / Master of Science (MSc)

Although fine granularity scalability (FGS) video coding based on matching pursuits and bit-plane coding have been proven to have better coding efficiency than discrete-cosine-transform-based FGS at low bit rates, it might not be the most efficient method in terms of rate-distortion optimization (RDO). We propose a rate-distortion optimization FGS video coding by dividing a frame into blocks to generate block-based embedded bit-streams and reorganize the bit-streams into several quality layers according to the rate-distortion slopes. The comparison between FGS matching pursuit video coding and RDO-FGS matching pursuit video coding is shown in the experimental results.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 105-107). Also available via the Internet.

In this work, we focus on two important goals of the transmission of scalable video over the Internet. The first goal is to provide high quality video to end users and the second one is to properly design networks and predict network performance for video transmission based on the characteristics of existing video traffic. Rate-distortion (R-D) based schemes are often applied to improve and stabilize video quality; however, the lack of R-D modeling of scalable coders limits their applications in scalable streaming. Thus, in the first part of this work, we analyze R-D curves of scalable video coders and propose a novel operational R-D model. We evaluate and demonstrate the accuracy of our R-D function in various scalable coders, such as Fine Granular Scalable (FGS) and Progressive FGS coders. Furthermore, due to the time-constraint nature of Internet streaming, we propose another operational R-D model, which is accurate yet with low computational cost, and apply it to streaming applications for quality control purposes. The Internet is a changing environment; however, most quality control approaches only consider constant bit rate (CBR) channels and no specific studies have been conducted for quality control in variable bit rate (VBR) channels. To fill this void, we examine an asymptotically stable congestion control mechanism and combine it with our R-D model to present smooth visual quality to end users under various network conditions. Our second focus in this work concerns the modeling and analysis of video traffic, which is crucial to protocol design and efficient network utilization for video transmission. Although scalable video traffic is expected to be an important source for the Internet, we find that little work has been done on analyzing or modeling it. In this regard, we develop a frame-level hybrid framework for modeling multi-layer VBR video traffic. In the proposed framework, the base layer is modeled using a combination of wavelet and time-domain methods and the enhancement layer is linearly predicted from the base layer using the cross-layer correlation.

Thesis (M.Sc.). / Written for the Dept. of Physics. Title from title page of PDF (viewed 2008/05/29). Includes bibliographical references.

Aquesta recerca està centrada en l'escalabilitat qualitativa de l'estàndard de compressió d'imatges JPEG2000. L'escalabilitat qualitativa és una característica fonamental que permet el truncament de la tira de bits a diferents punts sense penalitzar la qualitat de la imatge recuperada. L'escalabilitat qualitativa és també fonamental en transmissions d'imatges interactives, ja que permet la transmissió de finestres d'interès a diferents qualitats. El JPEG2000 aconsegueix escalabilitat qualitativa a partir del mètode de control de factor de compressió utilitzat en el procés de compressió, que empotra capes de qualitat a la tira de bits. En alguns escenaris, aquesta arquitectura pot causar dos problemàtiques: per una banda, quan el procés de codificació acaba, el número i distribució de les capes de qualitat és permanent, causant una manca d'escalabilitat qualitativa a tires de bits amb una o poques capes de qualitat. Per altra banda, el mètode de control de factor de compressió construeix capes de qualitat considerant la optimització de la raó distorsió per l'àrea completa de la imatge, i això pot provocar que la distribució de les capes de qualitat per la transmissió de finestres d'interès no sigui adequada. Aquesta tesis introdueix tres mètodes de control de factor de compressió que proveeixen escalabilitat qualitativa per finestres d'interès, o per tota l'àrea de la imatge, encara que la tira de bits contingui una o poques capes de qualitat. El primer mètode està basat en una simple estratègia d'entrellaçat (CPI) que modela la raó distorsió a partir d'una aproximació clàssica. Un anàlisis acurat del CPI motiva el segon mètode, basat en un ordre d'escaneig invers i una concatenació de passades de codificació (ROC). El tercer mètode es beneficia dels models de raó distorsió del CPI i ROC, desenvolupant una novedosa aproximació basada en la caracterització de la raó distorsió dels blocs de codificació dins una subbanda (CoRD). Els resultats experimentals suggereixen que tant el CPI com el ROC són capaços de proporcionar escalabilitat qualitativa a tires de bits, encara que continguin una o poques capes de qualitat, aconseguint un rendiment de codificació pràcticament equivalent a l'obtingut amb l'ús de capes de qualitat. Tot i això, els resultats del CPI no estan ben balancejats per les diferents raons de compressió i el ROC presenta irregularitats segons el corpus d'imatges. CoRD millora els resultats de CPI i ROC i aconsegueix un rendiment ben balancejat. A més, CoRD obté un rendiment de compressió una mica millor que l'aconseguit amb l'ús de capes de qualitat. La complexitat computacional del CPI, ROC i CoRD és, a la pràctica, negligible, fent-los adequats per el seu ús en transmissions interactives d'imatges. / This work is focused on the quality scalability of the JPEG2000 image compression standard. Quality scalability is an important feature that allows the truncation of the code-stream at different bit-rates without penalizing the coding performance. Quality scalability is also fundamental in interactive image transmissions to allow the delivery of Windows of Interest (WOI) at increasing qualities. JPEG2000 achieves quality scalability through the rate control method used in the encoding process, which embeds quality layers to the code-stream. In some scenarios, this architecture might raise two drawbacks: on the one hand, when the coding process finishes, the number and bit-rates of quality layers are fixed, causing a lack of quality scalability to code-streams encoded with a single or few quality layers. On the other hand, the rate control method constructs quality layers considering the rate¬distortion optimization of the complete image, and this might not allocate the quality layers adequately for the delivery of a WOI at increasing qualities. This thesis introduces three rate control methods that supply quality scalability for WOIs, or for the complete image, even if the code-stream contains a single or few quality layers. The first method is based on a simple Coding Passes Interleaving (CPI) that models the rate-distortion through a classical approach. An accurate analysis of CPI motivates the second rate control method, which introduces simple modifications to CPI based on a Reverse subband scanning Order and coding passes Concatenation (ROC). The third method benefits from the rate-distortion models of CPI and ROC, developing an approach based on a novel Characterization of the Rate-Distortion slope (CoRD) that estimates the rate-distortion of the code¬blocks within a subband. Experimental results suggest that CPI and ROC are able to supply quality scalability to code-streams, even if they contain a single or few quality layers, achieving a coding performance almost equivalent to the one obtained with the use of quality layers. However, the results of CPI are unbalanced among bit-rates, and ROC presents an irregular coding performance for some corpus of images. CoRD outperforms CPI and ROC achieving well-balanced and regular results and, in addition, it obtains a slightly better coding performance than the one achieved with the use of quality layers. The computational complexity of CPI, ROC and CoRD is negligible in practice, making them suitable to control interactive image transmissions.

Due to the lossy nature of image/video compression and the expensive bandwidth and computation resources in a multimedia system, one of the key design issues for image and video coding/transcoding is to optimize trade-off among distortion, rate, and/or complexity. This thesis studies the application of rate distortion (RD) optimization approaches to image and video coding/transcoding for exploring the best RD performance of a video codec compatible to the newest video coding standard H.264 and for designing computationally efficient down-sampling algorithms with high visual fidelity in the discrete Cosine transform (DCT) domain. RD optimization for video coding in this thesis considers two objectives, i.e., to achieve the best encoding efficiency in terms of minimizing the actual RD cost and to maintain decoding compatibility with the newest video coding standard H.264. By the actual RD cost, we mean a cost based on the final reconstruction error and the entire coding rate. Specifically, an operational RD method is proposed based on a soft decision quantization (SDQ) mechanism, which has its root in a fundamental RD theoretic study on fixed-slope lossy data compression. Using SDQ instead of hard decision quantization, we establish a general framework in which motion prediction, quantization, and entropy coding in a hybrid video coding scheme such as H.264 are jointly designed to minimize the actual RD cost on a frame basis. The proposed framework is applicable to optimize any hybrid video coding scheme, provided that specific algorithms are designed corresponding to coding syntaxes of a given standard codec, so as to maintain compatibility with the standard. Corresponding to the baseline profile syntaxes and the main profile syntaxes of H.264, respectively, we have proposed three RD algorithms---a graph-based algorithm for SDQ given motion prediction and quantization step sizes, an algorithm for residual coding optimization given motion prediction, and an iterative overall algorithm for jointly optimizing motion prediction, quantization, and entropy coding---with them embedded in the indicated order. Among the three algorithms, the SDQ design is the core, which is developed based on a given entropy coding method. Specifically, two SDQ algorithms have been developed based on the context adaptive variable length coding (CAVLC) in H.264 baseline profile and the context adaptive binary arithmetic coding (CABAC) in H.264 main profile, respectively. Experimental results for the H.264 baseline codec optimization show that for a set of typical testing sequences, the proposed RD method for H.264 baseline coding achieves a better trade-off between rate and distortion, i.e., 12\% rate reduction on average at the same distortion (ranging from 30dB to 38dB by PSNR) when compared with the RD optimization method implemented in H.264 baseline reference codec. Experimental results for optimizing H.264 main profile coding with CABAC show 10\% rate reduction over a main profile reference codec using CABAC, which also suggests 20\% rate reduction over the RD optimization method implemented in H.264 baseline reference codec, leading to our claim of having developed the best codec in terms of RD performance, while maintaining the compatibility with H.264. By investigating trade-off between distortion and complexity, we have also proposed a designing framework for image/video transcoding with spatial resolution reduction, i.e., to down-sample compressed images/video with an arbitrary ratio in the DCT domain. First, we derive a set of DCT-domain down-sampling methods, which can be represented by a linear transform with double-sided matrix multiplication (LTDS) in the DCT domain. Then, for a pre-selected pixel-domain down-sampling method, we formulate an optimization problem for finding an LTDS to approximate the given pixel-domain method to achieve the best trade-off between visual quality and computational complexity. The problem is then solved by modeling an LTDS with a multi-layer perceptron network and using a structural learning with forgetting algorithm for training the network. Finally, by selecting a pixel-domain reference method with the popular Butterworth lowpass filtering and cubic B-spline interpolation, the proposed framework discovers an LTDS with better visual quality and lower computational complexity when compared with state-of-the-art methods in the literature.

Due to the lossy nature of image/video compression and the expensive bandwidth and computation resources in a multimedia system, one of the key design issues for image and video coding/transcoding is to optimize trade-off among distortion, rate, and/or complexity. This thesis studies the application of rate distortion (RD) optimization approaches to image and video coding/transcoding for exploring the best RD performance of a video codec compatible to the newest video coding standard H.264 and for designing computationally efficient down-sampling algorithms with high visual fidelity in the discrete Cosine transform (DCT) domain. RD optimization for video coding in this thesis considers two objectives, i.e., to achieve the best encoding efficiency in terms of minimizing the actual RD cost and to maintain decoding compatibility with the newest video coding standard H.264. By the actual RD cost, we mean a cost based on the final reconstruction error and the entire coding rate. Specifically, an operational RD method is proposed based on a soft decision quantization (SDQ) mechanism, which has its root in a fundamental RD theoretic study on fixed-slope lossy data compression. Using SDQ instead of hard decision quantization, we establish a general framework in which motion prediction, quantization, and entropy coding in a hybrid video coding scheme such as H.264 are jointly designed to minimize the actual RD cost on a frame basis. The proposed framework is applicable to optimize any hybrid video coding scheme, provided that specific algorithms are designed corresponding to coding syntaxes of a given standard codec, so as to maintain compatibility with the standard. Corresponding to the baseline profile syntaxes and the main profile syntaxes of H.264, respectively, we have proposed three RD algorithms---a graph-based algorithm for SDQ given motion prediction and quantization step sizes, an algorithm for residual coding optimization given motion prediction, and an iterative overall algorithm for jointly optimizing motion prediction, quantization, and entropy coding---with them embedded in the indicated order. Among the three algorithms, the SDQ design is the core, which is developed based on a given entropy coding method. Specifically, two SDQ algorithms have been developed based on the context adaptive variable length coding (CAVLC) in H.264 baseline profile and the context adaptive binary arithmetic coding (CABAC) in H.264 main profile, respectively. Experimental results for the H.264 baseline codec optimization show that for a set of typical testing sequences, the proposed RD method for H.264 baseline coding achieves a better trade-off between rate and distortion, i.e., 12\% rate reduction on average at the same distortion (ranging from 30dB to 38dB by PSNR) when compared with the RD optimization method implemented in H.264 baseline reference codec. Experimental results for optimizing H.264 main profile coding with CABAC show 10\% rate reduction over a main profile reference codec using CABAC, which also suggests 20\% rate reduction over the RD optimization method implemented in H.264 baseline reference codec, leading to our claim of having developed the best codec in terms of RD performance, while maintaining the compatibility with H.264. By investigating trade-off between distortion and complexity, we have also proposed a designing framework for image/video transcoding with spatial resolution reduction, i.e., to down-sample compressed images/video with an arbitrary ratio in the DCT domain. First, we derive a set of DCT-domain down-sampling methods, which can be represented by a linear transform with double-sided matrix multiplication (LTDS) in the DCT domain. Then, for a pre-selected pixel-domain down-sampling method, we formulate an optimization problem for finding an LTDS to approximate the given pixel-domain method to achieve the best trade-off between visual quality and computational complexity. The problem is then solved by modeling an LTDS with a multi-layer perceptron network and using a structural learning with forgetting algorithm for training the network. Finally, by selecting a pixel-domain reference method with the popular Butterworth lowpass filtering and cubic B-spline interpolation, the proposed framework discovers an LTDS with better visual quality and lower computational complexity when compared with state-of-the-art methods in the literature.

Zugl.: Erlangen, Nürnberg, University, Diss., 2009.

M. Tech. Electrical Engineering. / This study proposes a distributive joint power and rate adaptation algorithm (JRPA) in ad hoc networks based on coupled interference minimisation. In the proposed method, the influence of coupled interference was controlled by dynamically adjusting network users' transmit power choices. The users are therefore aware of the current link status while determining their data rates. In addition, every maximize utility of other users as it maximizes its utility due to the inevitable cooperation, hence, improving a collective network performance. Solving this network utility maximization problem results in a supermodular game equivalence where users cooperate to maximise both local and global utility, hence the supermodular game theory concept was used to analyse the optimality and convergence of the proposed solution.