A variedade de dispositivos eletrônicos capazes de gravar e reproduzir vídeos digitais vem crescendo rapidamente, aumentando com isso a disponibilidade deste tipo de informação nas mais diferentes plataformas. Com isso, se torna cada vez mais importante o desenvolvimento de formas eficientes de armazenamento, transmissão, e acesso a estes dados. Nesse contexto, a codificação de vídeos tem um papel fundamental ao compactar informação, otimizando o uso de recursos aplicados no armazenamento e na transmissão de vídeos digitais. Não obstante, tarefas que envolvem a análise de vídeos, manipulação e busca baseada em conteúdo também se tornam cada vez mais relevantes, formando uma base para diversas aplicações que exploram a riqueza da informação contida em vídeos digitais. Muitas vezes a solução destes problemas passa pela segmentação de vídeos, que consiste da divisão de um vídeo em regiões que apresentam homogeneidade segundo determinadas características, como por exemplo cor, textura, movimento ou algum aspecto semântico. Nesta tese é proposto um novo método para segmentação de vídeos em objetos constituintes com base na coerência de movimento de regiões. O método de segmentação proposto inicialmente identifica as correspondências entre pontos esparsamente amostrados ao longo de diferentes quadros do vídeo. Logo após, agrupa conjuntos de pontos que apresentam trajetórias semelhantes. Finalmente, uma classificação pixel a pixel é obtida a partir destes grupos de pontos amostrados. O método proposto não assume nenhum modelo de câmera ou de movimento global para a cena e/ou objetos, e possibilita que múltiplos objetos sejam identificados, sem que o número de objetos seja conhecido a priori. Para validar o método de segmentação proposto, foi desenvolvida uma abordagem para a codificação de vídeos baseada em objetos. Segundo esta abordagem, o movimento de um objeto é representado através de transformações afins, enquanto a textura e a forma dos objetos são codificadas simultaneamente, de modo progressivo. O método de codificação de vídeos desenvolvido fornece funcionalidades tais como a transmissão progressiva e a escalabilidade a nível de objeto. Resultados experimentais dos métodos de segmentação e codificação de vídeos desenvolvidos são apresentados, e comparados a outros métodos da literatura. Vídeos codificados segundo o método proposto são comparados em termos de PSNR a vídeos codificados pelo software de referência JM H.264/AVC, versão 16.0, mostrando a que distância o método proposto está do estado da arte em termos de eficiência de codificação, ao mesmo tempo que provê funcionalidades da codificação baseada em objetos. O método de segmentação proposto no presente trabalho resultou em duas publicações, uma nos anais do SIBGRAPI de 2007 e outra no períodico IEEE Transactions on Image Processing. / The variety of electronic devices for digital video recording and playback is growing rapidly, thus increasing the availability of such information in many different platforms. So, the development of efficient ways of storing, transmitting and accessing such data becomes increasingly important. In this context, video coding plays a key role in compressing data, optimizing resource usage for storing and transmitting digital video. Nevertheless, tasks involving video analysis, manipulation and content-based search also become increasingly relevant, forming a basis for several applications that exploit the abundance of information in digital video. Often the solution to these problems makes use of video segmentation, which consists of dividing a video into homogeneous regions according to certain characteristics such as color, texture, motion or some semantic aspect. In this thesis, a new method for segmentation of videos in their constituent objects based on motion coherence of regions is proposed. The proposed segmentation method initially identifies the correspondences of sparsely sampled points along different video frames. Then, it performs clustering of point sets that have similar trajectories. Finally, a pixelwise classification is obtained from these sampled point sets. The proposed method does not assume any camera model or global motion model to the scene and/or objects. Still, it allows the identification of multiple objects, without knowing the number of objects a priori. In order to validate the proposed segmentation method, an object-based video coding approach was developed. According to this approach, the motion of an object is represented by affine transformations, while object texture and shape are simultaneously coded, in a progressive way. The developed video coding method yields functionalities such as progressive transmission and object scalability. Experimental results obtained by the proposed segmentation and coding methods are presented, and compared to other methods from the literature. Videos coded by the proposed method are compared in terms of PSNR to videos coded by the reference software JM H.264/AVC, version 16.0, showing the distance of the proposed method from the sate of the art in terms of coding efficiency, while providing functionalities of object-based video coding. The segmentation method proposed in this work resulted in two publications, one in the proceedings of SIBGRAPI 2007 and another in the journal IEEE Transactions on Image Processing.

Abstract not available

<p>This thesis explores the possibilities of avoiding the issues generally associated with compression of noisy imagery, through the usage of vector quantization. By utilizing the learning aspects of vector quantization, image processing operations such as noise reduction could be implemented in a straightforward way. Several techniques are presented and evaluated. A direct comparison shows that for noisy imagery, vector quantization, in spite of it's simplicity, has clear advantages over MPEG-4 encoding.</p>

<p>In this thesis a H.264 baseline high definition encoder is implemented on CELL processor. The target video sequence is YUV420 1080p at 30 frames per second in our encoder. To meet real-time requirements, a system architecture which reduces DMA requests is designed for large memory accessing. Several key computing kernels: Intra frame encoding, motion estimation searching and entropy coding are designed and ported to CELL processor units. A main challenge is to find a good tradeoff between DMA latency and processing time. The limited 256K bytes on-chip memory of SPE has to be organized efficiently in SIMD way. CAVLC is performed in non-real-time on the PPE.</p><p> </p><p>The experimental results show that our encoder is able to encode I frame in high quality and encode common 1080p video sequences in real-time. With the using of five SPEs and 63KB executable code size, 20.72M cycles are needed to encode one P frame partitions for one SPE. The average PSNR of P frames increases a maximum of 1.52%. In the case of fast speed video sequence, 64x64 search range gets better frame qualities than 16x16 search range and increases only less than two times computing cycles of 16x16. Our results also demonstrate that more potential power of the CELL processor can be utilized in multimedia computing.</p><p> </p><p>The H.264 main profile will be implemented in future phases of this encoder project. Since the platform we use is IBM Full-System Simulator, DMA performance in a real CELL processor is an interesting issue. Real-time entropy coding is another challenge to CELL.</p>

En aquest treball s'estudia el problema de la compressió de video utilitzant funcionalitats basades en el contingut en el marc teòric dels sistemes de codificació de seqüències de video basats en regions. Es tracten bàsicament dos problemes: El primer està relacionat amb com es pot aconseguir una codificació òptima en sistemes de codificació de video basats en regions. En concret, es mostra com es pot utilitzar un metodologia de 'rate-distortion' en aquest tipus de problemes. El segon problema que es tracta és com introduir funcionalitats basades en el contingut en un d'aquests sistemes de codificació de video.La teoria de 'rate-distortion' defineix l'optimalitat en la codificació com la representació d'un senyal que, per una taxa de bits donada, resulta en una distorsió mínima al reconstruir el senyal. En el cas de sistemes de codificació basats en regions, això implica obtenir una partició òptima i al mateix temps, un repartiment òptim dels bits entre les diferents regions d'aquesta partició. Aquest problema es formalitza per sistemes de codificació no escalables i es proposa un algorisme per solucionar-lo. Aquest algorisme s'aplica a un sistema de codificació concret anomenat SESAME. En el SESAME, cada quadre de la seqüència de video es segmenta en un conjunt de regions que es codifiquen de forma independent. La segmentació es fa seguint criteris d'homogeneitat espaial i temporal. Per eliminar la redundància temporal, s'utilitza un sistema predictiu basat en la informació de moviment tant per la partició com per la textura. El sistema permet seguir l'evolució temporal de cada regió per tota la seqüència. Els resultats de la codificació són òptims (o quasi-òptims) pel marc donat en un sentit de 'rate-distortion'. El procés de codificació inclou trobar una partició òptima i també trobar la tècnica de codificació i nivell de qualitat més adient per cada regió. Més endavant s'investiga el problema de codificació de video en sistemes amb escalabilitat i que suporten funcionalitats basades en el contingut. El problema es generalitza incloent en l'esquema de codificació les dependències espaials i temporals entre els diferents quadres o entre les diferents capes d'escalabilitat. En aquest cas, la solució requereix trobar la partició òptima i les tècniques de codificació de textura òptimes tant per la capa base com per la capa de millora. A causa de les dependències que hi ha entre aquestes capes, la partició i el conjunt de tècniques de codificació per la capa de millora dependran de les decisions preses en la capa base. Donat que aquest tipus de solucions generalment són molt costoses computacionalment, també es proposa una solució que no té en compte aquestes dependències.Els algorismes obtinguts s'apliquen per extendre SESAME. El sistema de codificació extès, anomenat XSESAME suporta diferents tipus d'escalabilitat (PSNR, espaial i temporal) així com funcionalitats basades en el contingut i la possibilitat de seguiment d'objectes a través de la seqüència de video. El sistema de codificació permet utilitzar dos modes diferents pel que fa a la selecció de les regions de la partició de la capa de millora: El primer mode (supervisat) està pensat per utilitzar funcionalitats basades en el contingut. El segon mode (no supervisat) no suporta funcionalitats basades en el contingut i el seu objectiu és simplement obtenir una codificació òptima a la capa de millora.Un altre tema que s'ha investigat és la integració d'un mètode de seguiment d'objectes en el sistema de codificació. En el cas general, el seguiment d'objectes en seqüències de video és un problema molt complex. Si a més aquest seguiment es vol integrar en un sistema de codificació apareixen problemes addicionals degut a que els requisits necessaris per obtenir eficiència en la codificació poden entrar en conflicte amb els requisits per una bona precisió en el seguiment d'objectes. Aquesta aparent incompatibilitat es soluciona utilitzant un enfocament basat en una doble partició de cada quadre de la seqüència. La partició que s'utilitza per la codificació es resegmenta utilitzant criteris purament espaials. Al projectar aquesta segona partició permet una millor adaptació dels contorns de l'objecte a seguir. L'excés de regions que implicaria aquesta re-segmentació s'elimina amb una etapa de fusió de regions realitzada a posteriori. / En este trabajo se estudia el problema de la compresión de vídeo utilizando funcionalidades basadas en el contenido en el marco teórico de los sistemas de codificación de secuencias de vídeo basados en regiones. Se tratan básicamente dos problemas: El primero está relacionado con la obtención de una codificación óptima en sistemas de codificación de vídeo basados en regiones. En concreto, se muestra como se puede utilizar un metodología de 'rate-distortion' para este tipo de problemas. El segundo problema tratado es como introducir funcionalidades basadas en el contenido en uno de estos sistemas de codificación de vídeo.La teoría de 'rate-distortion' define la optimalidad en la codificación como la representación de una señal que, para un tasa de bits dada, resulta en una distorsión mínima al reconstruir la señal. En el caso de sistemas de codificación basados en regiones, esto implica obtener una partición óptima y al mismo tiempo, un reparto óptimo de los bits entre las diferentes regiones de esta partición. Este problema se formaliza para sistemas de codificación no escalables y se propone un algoritmo para solucionar este problema. Este algoritmo se aplica a un sistema de codificación concreto llamado SESAME. En SESAME, cada cuadro de la secuencia de vídeo se segmenta en un conjunto de regiones que se codifican de forma independiente. La segmentación se hace siguiendo criterios de homogeneidad espacial y temporal. Para eliminar la redundancia temporal, se utiliza un sistema predictivo basado en la información de movimiento tanto para la partición como para la textura. El sistema permite seguir la evolución temporal de cada región a lo largo de la secuencia. Los resultados de la codificación son óptimos (o casi-óptimos) para el marco dado en un sentido de 'rate-distortion'. El proceso de codificación incluye encontrar una partición óptima y también encontrar la técnica de codificación y nivel de calidad más adecuados para cada región.Más adelante se investiga el problema de la codificación de vídeo en sistemas con escalabilidad y que suporten funcionalidades basadas en el contenido. El problema se generaliza incluyendo en el esquema de codificación las dependencias espaciales y temporales entre los diferentes cuadros o entre las diferentes capas de escalabilidad. En este caso, la solución requiere encontrar la partición óptima y las técnicas de codificación de textura óptimas tanto para la capa base como para la capa de mejora. A causa de les dependencias que hay entre estas capas, la partición y el conjunto de técnicas de codificación para la capa de mejora dependerán de las decisiones tomadas en la capa base. Dado que este tipo de soluciones generalmente son muy costosas computacionalmente, también se propone una solución que no tiene en cuenta estas dependencias.Los algoritmos obtenido se usan en la extensión de SESAME. El sistema de codificación extendido, llamado XSESAME soporta diferentes tipos de escalabilidad (PSNR, espacial y temporal) así como funcionalidades basadas en el contenido y la posibilidad de seguimiento de objetos a través de la secuencia de vídeo. El sistema de codificación permite utilizar dos modos diferentes por lo que hace referencia a la selección de les regiones de la partición de la capa de mejora: El primer modo (supervisado) está pensado para utilizar funcionalidades basadas en el contenido. El segundo modo (no supervisado) no soporta funcionalidades basadas en el contenido y su objetivo es simplemente obtener una codificación óptima en la capa de mejora.Otro tema investigado es la integración de un método de seguimiento de objetos en el sistema de codificación.En el caso general, el seguimiento de objetos en secuencias de vídeo es un problema muy complejo. Si este seguimiento se quiere integrar en un sistema de codificación aparecen problemas adicionales debido a que los requisitos necesarios para obtener eficiencia en la codificación pueden entrar en conflicto con los requisitos para obtener una buena precisión en el seguimiento de objetos. Esta aparente incompatibilidad se soluciona usando un enfoque basado en una doble partición de cada cuadro de la secuencia. La partición que se usa para codificar se resegmenta usando criterios puramente espaciales. Proyectando esta segunda partición se obtiene una mejor adaptación de los contornos al objeto a seguir. El exceso de regiones que implicaría esta resegmentación se elimina con una etapa de fusión de regiones realizada a posteriori. / This work addresses the problem of video compression with content-based functionalities in the framework of segmentation-based video coding systems. Two major problems are considered. The first one is related with coding optimality in segmentation-based coding systems. Regarding this subject, the feasibility of a rate-distortion approach for a complete region-based coding system is shown. The second one is how to address content-based functionalities in the coding system proposed as a solution of the first problem. Optimality, as defined in the framework of rate-distortion theory, deals with obtaining a representation of the video sequence that leads to a minimum distortion of the coded signal for a given bit budget. In the case of segmentation-based coding systems this means to obtain an 'optimal' partition together with the best coding technique for each region of this partition so that the result is optimal in an operational rate-distortion sense. The problem is formalized for independent, non-scalable coding.An algorithm to solve this problem is provided as well.This algorithms is applied to a specific segmentation-based coding system, the so called SESAME. In SESAME, each frame is segmented into a set of regions, that are coded independently. Segmentation involves both spatial and motion homogeneity criteria. To exploit temporal redundancy, a prediction for both the partition and the texture of the current frame is created by using motion information. The time evolution of each region is defined along the sequence (time tracking). The results are optimal (or near-optimal) for the given framework in a rate-distortion sense. The definition of the coding strategy involves a global optimization of the partition as well as of the coding technique/quality level for each region. Later, the investigation is also extended to the problem of video coding optimization in the framework of a scalable video coding system that can address content-based functionalities. The focus is set in the various types of content-based scalability and object tracking. The generality of the problem has also been extended by including the spatial and temporal dependencies between frames and scalability layers into the optimization schema. In this case the solution implies finding the optimal partition and set of quantizers for both the base and the enhancement layers. Due to the coding dependencies of the enhancement layer with respect to the base layer, the partition and the set of quantizers of the enhancement layer depend on the decisions made on the base layer. Also, a solution for the independent optimization problem (i.e. without tacking into account dependencies between different frames of scalability layers) has been proposed to reduce the computational complexity. These solutions are used to extend the SESAME coding system. The extended coding system, named XSESAME, supports different types of scalability (PSNR, Spatial and temporal) as well as content-based functionalities, such as content-based scalability and object tracking. Two different operating modes for region selection in the enhancement layer have been presented: One (supervised) aimed at providing content-based functionalities at the enhancement layer and the other (unsupervised) aimed at coding efficiency, without content-based functionalities. Integration of object tracking into the segmentation-based coding system is also investigated.In the general case, tracking is a very complex problem. If this capability has to be integrated into a coding system, additional problems arise due to conflicting requirements between coding efficiency and tracking accuracy. This is solved by using a double partition approach, where pure spatial criteria are used to re-segment the partition used for coding. The projection of the re-segmented partition results in more precise adaptation to object contours. A merging step is performed a posteriori to eliminate the excess of regions originated by the re-segmentation.

This thesis documents the design of logical superposition coded (SPC) modulation for implementation in wireless video multicast systems, to tackle the issues caused by multi-user channel diversity, one of the legacy problems due to the nature of wireless video multicasting. The framework generates a logical SPC modulated signal by mapping successively refinable information bits into a single signal constellation with modifications in the MAC-layer software. The transmitted logical SPC signals not only manipulatively mimic SPC signals generated by the superposition of multiple modulated signals in the conventional hardware-based SPC modulation, but also yield comparable performance gains when provided with the knowledge of information bits dependencies and receiver channel distributions. At the receiving end, the proposed approach only requires simple modifications in the MAC layer software, which demonstrates full decoding compatibility with the conventional multi-stage signal-interference cancellation (SIC) approach involving additional hardware devices. Generalized formulations for symbol error rate (SER) are derived for performance evaluations and comparisons with the conventional hardware-based approach.

Compressive sensing (CS) is a novel sampling methodology representing a paradigm shift from conventional data acquisition schemes. The theory of compressive sensing ensures that under suitable conditions compressible signals or images can be reconstructed from far fewer samples or measurements than what are required by the Nyquist rate. So far in the literature, most works on CS concentrate on one-dimensional or two-dimensional data. However, besides involving far more data, three-dimensional (3D) data processing does have particularities that require the development of new techniques in order to make successful transitions from theoretical feasibilities to practical capacities. This thesis studies several issues arising from the applications of the CS methodology to some 3D image processing tasks. Two specific applications are hyperspectral imaging and video compression where 3D images are either directly unmixed or recovered as a whole from CS samples. The main issues include CS decoding models, preprocessing techniques and reconstruction algorithms, as well as CS encoding matrices in the case of video compression. Our investigation involves three major parts. (1) Total variation (TV) regularization plays a central role in the decoding models studied in this thesis. To solve such models, we propose an efficient scheme to implement the classic augmented Lagrangian multiplier method and study its convergence properties. The resulting Matlab package TVAL3 is used to solve several models. Computational results show that, thanks to its low per-iteration complexity, the proposed algorithm is capable of handling realistic 3D image processing tasks. (2) Hyperspectral image processing typically demands heavy computational resources due to an enormous amount of data involved. We investigate low-complexity procedures to unmix, sometimes blindly, CS compressed hyperspectral data to directly obtain material signatures and their abundance fractions, bypassing the high-complexity task of reconstructing the image cube itself. (3) To overcome the "cliff effect" suffered by current video coding schemes, we explore a compressive video sampling framework to improve scalability with respect to channel capacities. We propose and study a novel multi-resolution CS encoding matrix, and a decoding model with a TV-DCT regularization function. Extensive numerical results are presented, obtained from experiments that use not only synthetic data, but also real data measured by hardware. The results establish feasibility and robustness, to various extent, of the proposed 3D data processing schemes, models and algorithms. There still remain many challenges to be further resolved in each area, but hopefully the progress made in this thesis will represent a useful first step towards meeting these challenges in the future.

This thesis explores the possibilities of avoiding the issues generally associated with compression of noisy imagery, through the usage of vector quantization. By utilizing the learning aspects of vector quantization, image processing operations such as noise reduction could be implemented in a straightforward way. Several techniques are presented and evaluated. A direct comparison shows that for noisy imagery, vector quantization, in spite of it's simplicity, has clear advantages over MPEG-4 encoding.

This thesis documents the design of logical superposition coded (SPC) modulation for implementation in wireless video multicast systems, to tackle the issues caused by multi-user channel diversity, one of the legacy problems due to the nature of wireless video multicasting. The framework generates a logical SPC modulated signal by mapping successively refinable information bits into a single signal constellation with modifications in the MAC-layer software. The transmitted logical SPC signals not only manipulatively mimic SPC signals generated by the superposition of multiple modulated signals in the conventional hardware-based SPC modulation, but also yield comparable performance gains when provided with the knowledge of information bits dependencies and receiver channel distributions. At the receiving end, the proposed approach only requires simple modifications in the MAC layer software, which demonstrates full decoding compatibility with the conventional multi-stage signal-interference cancellation (SIC) approach involving additional hardware devices. Generalized formulations for symbol error rate (SER) are derived for performance evaluations and comparisons with the conventional hardware-based approach.

Current interframe video compression standards such as the MPEG4 and H.264, require a high-complexity encoder for predictive coding to exploit the similarities among successive video frames. This requirement is acceptable for cases where the video sequence to be transmitted is encoded once and decoded many times. However, some emerging applications such as video-based sensor networks, power-aware surveillance and mobile video communication systems require computational complexity to be shifted from encoder to decoder. Distributed Video Coding (DVC) is a new coding paradigm, based on two information-theoretic results, Slepian-Wolf and Wyner-Ziv, which allows exploiting source statistics at the decoder only. This architecture, therefore, enables very simple encoders to be used in video coding. Wyner-Ziv video coding is a particular case of DVC which deals with lossy source coding where side information is available at the decoder only. In this thesis, we implemented a DVC codec based on the DISCOVER (DIStributed COding for Video sERvices) project and carried out a detailed analysis of each block. Several algorithms have been implemented for each block and results are compared in terms of rate-distortion. The implemented architecture is aimed to be used as a testbed for future studies.