Double Bind: Splitting identity and the body as an object is a research project consisting of studio-based practice presented mainly in video installation format. This work looks at hysterical symptoms as a performance of a body’s split identity. The project draws on the Lacanian theory of Mirror Stage which proposes that the self experienced by the subject, and the image of that self (represented in a mirror-like reflection, or an image) are different to each other, and the development of self-awareness as misrecognition of one’s self. As a conspicuous example of split body, Chapter One describes how the hysterical body, in clinical and artistic representation, is dissociated into multiple selves. In Chapter Two, I discuss some examples of contemporary performance artists who use themselves as subjects, but whose bodies become objects that do not portray the self. In the final chapter I explain how, in my video work, I objectify my own body and how I assess whether this is a mode of self-portraiture. / During the course of this research, I studied a wide range of medical resources and psychoanalytical literature, much of which employed visual illustration and documentation. For example, I have drawn inspiration from Jean-Martin Charcot’s photographic documents of female hysterics whom he treated as patients at the French hospital of La Salpêtrière in the late 19th century; in particular the figure of his most famous patient, known as Augustine. My research also involved studio-based investigation, such as experimentations with the performance of my own body in video format, and the contextual study of artistic and critical texts relating to contemporary media art. / The aim of this research is to demonstrate the ways in which my video performances split the body, creating an Other within one body that can be compared with the hysterical body of a patient, like Augustine, performing for her doctor. In this condition, I perform as the subject and the object of the gaze at the same time. My self-portrait is split in this way: it creates a body double, which I misrecognise as myself. But in doing so, I am both the director and the performer of the image. This is the double bind that my video work puts me into.

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.

Double Bind: Splitting identity and the body as an object is a research project consisting of studio-based practice presented mainly in video installation format. This work looks at hysterical symptoms as a performance of a body’s split identity. The project draws on the Lacanian theory of Mirror Stage which proposes that the self experienced by the subject, and the image of that self (represented in a mirror-like reflection, or an image) are different to each other, and the development of self-awareness as misrecognition of one’s self. As a conspicuous example of split body, Chapter One describes how the hysterical body, in clinical and artistic representation, is dissociated into multiple selves. In Chapter Two, I discuss some examples of contemporary performance artists who use themselves as subjects, but whose bodies become objects that do not portray the self. In the final chapter I explain how, in my video work, I objectify my own body and how I assess whether this is a mode of self-portraiture. / During the course of this research, I studied a wide range of medical resources and psychoanalytical literature, much of which employed visual illustration and documentation. For example, I have drawn inspiration from Jean-Martin Charcot’s photographic documents of female hysterics whom he treated as patients at the French hospital of La Salpêtrière in the late 19th century; in particular the figure of his most famous patient, known as Augustine. My research also involved studio-based investigation, such as experimentations with the performance of my own body in video format, and the contextual study of artistic and critical texts relating to contemporary media art. / The aim of this research is to demonstrate the ways in which my video performances split the body, creating an Other within one body that can be compared with the hysterical body of a patient, like Augustine, performing for her doctor. In this condition, I perform as the subject and the object of the gaze at the same time. My self-portrait is split in this way: it creates a body double, which I misrecognise as myself. But in doing so, I am both the director and the performer of the image. This is the double bind that my video work puts me into.

In this dissertation, first, we have proposed and implemented a new perceptually tuned wavelet based, rate scalable, and color image encoding/decoding system based on the human perceptual model. It is based on state-of-the-art research on embedded wavelet image compression technique, Contrast Sensitivity Function (CSF) for Human Visual System (HVS) and extends this scheme to handle optimal bit allocation among multiple bands, such as Y, Cb, and Cr. Our experimental image codec shows very exciting results in compression performance and visual quality comparing to the new wavelet based international still image compression standard - JPEG 2000. On the other hand, our codec also shows significant better speed performance and comparable visual quality in comparison to the best codec available in rate scalable color image compression - CSPIHT that is based on Set Partition In Hierarchical Tree (SPIHT) and Karhunen-Loeve Transform (KLT). Secondly, a novel wavelet based interframe compression scheme has been developed and put into practice. It is based on the Flexible Block Wavelet Transform (FBWT) that we have developed. FBWT based interframe compression is very efficient in both compression and speed performance. The compression performance of our video codec is compared with H263+. At the same bit rate, our encoder, being comparable to the H263+ scheme, with a slightly lower (Peak Signal Noise Ratio (PSNR) value, produces a more visually pleasing result. This implementation also preserves scalability of wavelet embedded coding technique. Thirdly, the scheme to handle optimal bit allocation among color bands for still imagery has been modified and extended to accommodate the spatial-temporal sensitivity of the HVS model. The bit allocation among color bands based on Kelly's spatio-temporal CSF model is designed to achieve the perceptual optimum for human eyes. A perceptually tuned, wavelet based, rate scalable video encoding/decoding system has been designed and implemented based on this new bit allocation scheme. Finally to present the potential applications of our rate scalable video codec, a prototype system for rate scalable video streaming over the Internet has been designed and implemented to deal with the bandwidth unpredictability of the Internet.

L'apparition de nouvelles technologies comme l'informatique en nuages (cloud computing) offre de nouvelles opportunités de traitement de l'information. Par exemple, il est désormais facile de stocker ses photos ou vidéos personnelles sur des serveurs distants. Il est également possible de partager ces contenus à travers ces mêmes serveurs, ou encore via les réseaux sociaux ou les plateformes de téléchargement. Cependant, ces données personnelles sont bien souvent accessibles par le fournisseur de service, essentiellement pour des raisons pratiques : par exemple adapter une vidéo pour qu'elle s'affiche au bon format quel que soit l'appareil utilisé pour la visionner, permettre le partage de ses contenus avec d’autres personnes, etc. Cela soulève cependant un problème de confidentialité de ces données personnelles, et de confiance dans le fournisseur du service. La cryptographie classique apporte des solutions à ce problème, mais soulève malheureusement celui de la maniabilité des données : il devient par exemple impossible d'adapter un contenu vidéo au bon format d'affichage puisque le fournisseur ne peut plus « voir » la vidéo. Une solution alternative réside toutefois dans le chiffrement homomorphe. Cet outil un peu magique de la cryptographie avancée apporte la même sécurité que les algorithmes de cryptographie classique, mais permet de plus de manipuler les données tout en conservant leur forme chiffrée. Il offre ainsi une nouvelle perspective pour les fournisseurs puisque ceux-ci peuvent continuer à traiter l'information sans être capable de la voir, et donc sans atteinte à la vie privée de leurs utilisateurs, se conformant ainsi au nouveau Règlement Général sur la Protection des Données (RGPD). Bien que le chiffrement homomorphe soit très souvent considéré comme insuffisamment mature, du fait de sa complexité algorithmique, cette thèse cherche à montrer son caractère prometteur, en s'intéressant à son usage pour le traitement d'images et de vidéos chiffrées à la source. Nous regardons ainsi les différents algorithmes qui constituent un encodeur d'image/vidéo (JPEG/H264 et HEVC) et les transformons en des circuits qui sont manipulables par des systèmes de chiffrement homomorphes. Nous proposons ainsi dans cette thèse le tout premier pipeline de compression d'images de type JPEG ("homomorphic-JPEG") sur des pixels qui sont chiffrés de bout-en-bout. Pour optimiser la gestion des données ainsi protégées, nous proposons également de nouveaux outils applicables à tous les schémas de chiffrement homomorphe sur les réseaux idéaux. Notre approche permet de maximiser le nombre de slots dans un chiffré et introduit de nouvelles fonctions pour manipuler ces différents slots de manière indépendante les uns des autres. Ces travaux de thèse ont abouti à la publication de deux articles dans des conférences internationales ainsi qu’à la soumission d'un article supplémentaire. / The emergence of new technologies like cloud computing brings new opportunities in information processing. For example it is easy today to send our personal pictures or videos to a remote server (Google Drive, OneDrive …). We can also share this content among the same servers or via social networks and streaming services. However, this personal data is often also available to the service provider, mainly for practical reasons e.g. to configure a video to have the right format regardless of the displayer (smartphone or computer), to share our data with other people, etc. This raises issues of privacy and trust into the service provider. Classical cryptography brings some answers to this kind of issues, yet leaving the problem of handling the encrypted data: e.g., it becomes impossible to reconfigure a video because the provider can no longer “see” it. An alternative solution is “homomorphic encryption”. It is a powerful tool of advanced cryptography which provides the same security as classical cryptography algorithms, but it still allows us to manipulate ciphertexts such their underlying plaintexts are modified. Consequently, it offers a new perspective to service providers since they can continue to process their clients’information without knowing what it contains. This allows them to provide privacy-preserving services and comply with the new General Data Protection Regulation (GDPR). Although it is considered that homomorphic encryption does not have enough maturity due to its large algorithmic complexity, in this thesis, we are trying to show its potential by using it in the context of image and video processing over the encrypted data. In this context, we look at the different algorithms in an image/video encoder (JPEG/H264 and HEVC) and transform them to circuits which can be manipulated by homomorphic encryption schemes. Our main contribution is to propose the first pipeline for an image compression of type JPEG (homomorphic-JPEG) running on end-to-end encrypted pixels. To optimize the management of the encrypted data, we also propose new tools applicable to existing homomorphic encryption schemes over the ring version of lattices. Our approach allows us to maximize the number of slots in some ciphertext and we introduce new functions allowing to handle these slots independently in the encrypted domain. This thesis work also lead to two publications to international conferences as well as the submission of an additional article.

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