Video transmission over noisy wireless channels leads to errors on video, which degrades the visual quality notably and makes error concealment an indispensable job. In the literature, there are several error concealment techniques based on estimating the lost parts of the video from the available data. Utilization of data hiding for this problem, which seems to be an alternative of predicting the lost data, provides a reserve information about the video to the receiver while unchanging the transmitted bit-stream syntax / hence, improves the reconstruction video quality without significant extra channel utilization. A complete error resilient video transmission codec is proposed, utilizing imperceptible embedded information for combined detecting, resynchronization and reconstruction of the errors and lost data. The data, which is imperceptibly embedded into the video itself at the encoder, is extracted from the video at the decoder side to be utilized in error concealment. A spatial domain error recovery technique, which hides edge orientation information of a block, and a resynchronization technique, which embeds bit length of a block into other blocks are combined, as well as some parity information about the hidden data, to conceal channel errors on intra-coded frames of a video sequence. The errors on inter-coded frames are basically recovered by hiding motion vector information along with a checksum into the next frames. The simulation results show that the proposed approach performs superior to conventional approaches for concealing the errors in binary symmetric channels, especially for higher bit rates and error rates.

O presente trabalho apresenta o resultado de um estudo comparando o uso de técnicas de aprendizado de máquina na adaptação dinâmica de vídeo. O sistema utiliza informações de contexto, como as características de um dispositivo, preferências do usuário e condições de rede, como entrada de um mecanismo de decisão responsável por encontrar parâmetros de codificação mais adequados ao contexto à geração de um novo fluxo de vídeo dinamicamente personalizado / This work presents the results of a comparative study about machine learning techniques used in dynamic video adaptation. The system uses contextual information, such as devices? characteristics, user?s preferences and network condition as an input for a decision engine, responsible to find the best encoding parameters to be used during the generation of a new dynamically personalized video stream

Real time applications over IP network became widely used in different fields; social video conference, online educational lectures, industrial, military, and online robotic medical surgery. Online medical surgery over IP network has experienced rapid growth in the last few years primarily due to advances in technology (e.g., increased bandwidth; new cameras, monitors, and coder/decoders (CODECs)) and changes in the medical care environment (e.g., increased outpatient care, remote surgeries). The purpose of this study was to examine and analyze the impact of IP networks parameters; delay, jitter, throughput, and drop packet on the performance of real-time medical surgery videos sent across different IP networks; native IPv6, native IPv4, 6to4 and 6in4 tunneling transition mechanisms and compare the behavior of video packets over IP networks. The impact of each parameter over IP networks is examined by using different video codecs MPEG-1, MPEG-2, and MPEG-4. This study has been carried out with two main parts; theoretical and practical part, the theoretical part of this study focused on the calculations of various delays in IP networks such as transmission, processing, propagation, and queuing delays for video packet, while the practical part includes; examining of video codecs throughput over IP networks by using jperf tool and examining delay, jitter, and packet drops for different packet sizes by using IDT-G tool and how these parameters can affect quality of received video. The obtained theoretical and practical results were presented in different tables and plotted into different graphs to show the performance of real time video over IP networks. These results confirmed that video codecs MPEG-1, MPEG-2, and MPEG-4 were highly impacted by encapsulation and de-capsulation process except MPEG-4 codec, MPEG-4 was the least impacted by IPv4, IPv6, and IP transition mechanisms concerning throughput and wastage bandwidth. It also indicated that using IPv6-to-4 and IPv6-in-4 tunneling mechanisms caused more bandwidth wastage, high delay, jitter, and packet drop than IPv4 and IPv6.

O presente trabalho apresenta o resultado de um estudo comparando o uso de técnicas de aprendizado de máquina na adaptação dinâmica de vídeo. O sistema utiliza informações de contexto, como as características de um dispositivo, preferências do usuário e condições de rede, como entrada de um mecanismo de decisão responsável por encontrar parâmetros de codificação mais adequados ao contexto à geração de um novo fluxo de vídeo dinamicamente personalizado / This work presents the results of a comparative study about machine learning techniques used in dynamic video adaptation. The system uses contextual information, such as devices? characteristics, user?s preferences and network condition as an input for a decision engine, responsible to find the best encoding parameters to be used during the generation of a new dynamically personalized video stream

This thesis is devoted to the study of a compression and transmission framework for video. It exploits the JPEG 2000 standard and the coding with side information principles to enable an efficient interactive browsing of video sequences. During the last decade, we have witnessed an explosion of digital visual information as well as a significant diversification of visualization devices. In terms of viewing experience, many applications now enable users to interact with the content stored on a distant server. Pausing video sequences to observe details by zooming and panning or, at the opposite, browsing low resolutions of high quality HD videos are becoming common tasks. The video distribution framework envisioned in this thesis targets such devices and applications. Based on the conditional replenishment framework, the proposed system combines two complementary coding methods. The first one is JPEG 2000, a scalable and very efficient compression algorithm. The second method is based on the coding with side information paradigm. This technique is relatively novel in a video context, and has been adapted to the particular scalable image representation adopted in this work. Interestingly, it has been improved by integrating an image source model and by exploiting the temporal correlation inherent to the sequence. A particularity of this work is the emphasis on the system scalability as well as on the server complexity. The proposed browsing architecture can scale to handle large volumes of content and serve a possibly very large number of heterogeneous users. This is achieved by defining a scheduler that adapts its decisions to the channel conditions and to user requirements expressed in terms of computational capabilities and spatio-temporal interest. This scheduling is carried out in real-time at low computational cost and in a post-compression way, without re-encoding the sequences.

Les schémas de Codage Vidéo Linéaire (CVL) inspirés de SoftCast ont émergé dans la dernière décennie comme une alternative aux schémas de codage vidéo classiques. Ces schémas de codage source-canal conjoint exploitent des résultats théoriques montrant qu’une transmission (quasi-)analogique est plus performante dans des situations de multicast que des schémas numériques lorsque les rapports signal-à-bruit des canaux (C-SNR) diffèrent d’un récepteur à l’autre. Dans ce contexte, les schémas de CVL permettent d’obtenir une qualité de vidéo décodée proportionnelle au C-SNR du récepteur.Une première contribution de cette thèse concerne l’optimisation de la matrice de précodage de canal pour une transmission de type OFDM de flux générés par un CVL lorsque les contraintes de puissance diffèrent d’un sous-canal à l’autre. Ce type de contrainte apparait en sur des canaux DSL, ou dans des dispositifs de transmission sur courant porteur en ligne (CPL). Cette thèse propose une solution optimale à ce problème de type multi-level water filling et nécessitant la solution d’un problème de type Structured Hermitian Inverse Eigenvalue. Trois algorithmes sous-optimaux de complexité réduite sont également proposés. Des nombreux résultats de simulation montrent que les algorithmes sous-optimaux ont des performances très proches de l’optimum et réduisent significativement le temps de codage. Le calcul de la matrice de précodage dans une situation de multicast est également abordé. Une seconde contribution principale consiste en la réduction de l’impact du bruit impulsif dans les CVL. Le problème de correction du bruit impulsif est formulé comme un problème d’estimation d’un vecteur creux. Un algorithme de type Fast Bayesian Matching Pursuit (FBMP) est adapté au contexte CVL. Cette approche nécessite de réserver des sous-canaux pour la correction du bruit impulsif, entrainant une diminution de la qualité vidéo en l'absence de bruit impulsif. Un modèle phénoménologique (MP) est proposé pour décrire l’erreur résiduelle après correction du bruit impulsif. Ce modèle permet de d’optimiser le nombre de sous-canaux à réserver en fonction des caractéristiques du bruit impulsif. Les résultats de simulation montrent que le schéma proposé améliore considérablement les performances lorsque le flux CVL est transmis sur un canal sujet à du bruit impulsif. / SoftCast based Linear Video Coding (LVC) schemes have been emerged in the last decade as a quasi analog joint-source-channel alternative to classical video coding schemes. Theoretical analyses have shown that analog coding is better than digital coding in a multicast scenario when the channel signal-to-noise ratios (C-SNR) differ among receivers. LVC schemes provide in such context a decoded video quality at different receivers proportional to their C-SNR.This thesis considers first the channel precoding and decoding matrix design problem for LVC schemes under a per-subchannel power constraint. Such constraint is found, e.g., on Power Line Telecommunication (PLT) channels and is similar to per-antenna power constraints in multi-antenna transmission system. An optimal design approach is proposed, involving a multi-level water filling algorithm and the solution of a structured Hermitian Inverse Eigenvalue problem. Three lower-complexity alternative suboptimal algorithms are also proposed. Extensive experiments show that the suboptimal algorithms perform closely to the optimal one and can reduce significantly the complexity. The precoding matrix design in multicast situations also has been considered.A second main contribution consists in an impulse noise mitigation approach for LVC schemes. Impulse noise identification and correction can be formulated as a sparse vector recovery problem. A Fast Bayesian Matching Pursuit (FBMP) algorithm is adapted to LVC schemes. Subchannels provisioning for impulse noise mitigation is necessary, leading to a nominal video quality decrease in absence of impulse noise. A phenomenological model (PM) is proposed to describe the impulse noise correction residual. Using the PM model, an algorithm to evaluate the optimal number of subchannels to provision is proposed. Simulation results show that the proposed algorithms significantly improve the video quality when transmitted over channels prone to impulse noise.