Investigating low-bitrate, low-complexity H.264 region of interest techniques in error-prone environments

The H.264/AVC video coding standard leverages advanced compression methods to provide a significant increase in performance over previous CODECs in terms of picture quality, bitrate, and flexibility. The specification itself provides several profiles and levels that allow customization through the use of various advanced features. In addition to these features, several new video coding techniques have been developed since the standard\u27s inception. One such technique known as Region of Interest (RoI) coding has been in existence since before H.264\u27s formalization, and several means of implementing RoI coding in H.264 have been proposed. Region of Interest coding operates under the assumption that one or more regions of a sequence have higher priority than the rest of the video. One goal of RoI coding is to provide a decrease in bitrate without significant loss of perceptual quality, and this is particularly applicable to low complexity environments, if the proper implementation is used. Furthermore, RoI coding may allow for enhanced error resilience in the selected regions if desired, making RoI suitable for both low-bitrate and error-prone scenarios. The goal of this thesis project was to examine H.264 Region of Interest coding as it applies to such scenarios. A modified version of the H.264 JM Reference Software was created in which all non-Baseline profile features were removed. Six low-complexity RoI coding techniques, three targeting rate control and three targeting error resilience, were selected for implementation. Error and distortion modeling tools were created to enhance the quality of experimental data. Results were gathered by varying a range of coding parameters including frame size, target bitrate, and macroblock error rates. Methods were then examined based on their rate-distortion curves, ability to achieve target bitrates accurately, and per-region distortions where applicable

Color space adaptation for video coding

Processament d'imatges abans de ser codificades pel codificador HEVC amb la finalitat d'augmentar la qualitat i la fidelitat.[ANGLÈS] Project on the objective and subjective improvements by pre-processing images to be encoded into a video.[CASTELLÀ] Proyecto sobre la repercusión en la mejora de calidad objetiva y subjetiva del pre-procesado de imágenes a codificar con vídeo.[CATALÀ] Projecte sobre la repercussió en la millora de la qualitat objectiva i subjectiva del pre-processament d'imatges a codificar amb vídeo

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

Toward Resilience and Data Reduction in Exascale Scientific Computing

Because of the ever-increasing execution scale, reliability and data management are becoming more and more important for scientific applications. On the one hand, exascale systems are anticipated to be more susceptible to soft errors ,e.g. silent data corruptions, due to the reduction in the size of transistors and the increase of the number of components. These errors will lead to corrupted results without warning, making the output of the computation untrustable. On the other hand, large volumes of highly variable data are produced by scientific computing with high velocity on exascale systems or advanced instruments, and the I/O time on storing these data is prohibitive due to the I/O bottleneck in parallel file systems. In this work, we leverage algorithm-based fault tolerance (ABFT) and error-bound lossy compression to tackle the two problems, in order to support efficient scientific computing on exascale systems.We propose an efficient fault tolerant scheme to tolerant soft errors in Fast Fourier Transform (FFT), one of the most important computation kernels widely used in scientific computing. Traditional redundancy approaches will at least double the execution time or resources, limiting the usage in practice because of the large overhead. Previous works on offline ABFT algorithms for FFT mitigate this problem by providing resilient FFT with lower overhead, but these algorithms fail to make progress in vulnerable environments with high error rates because they can only detect and correct errors after the whole computation finishes. We propose an online ABFT scheme for large-scale FFT inspired by the divide-and-conquer nature of the FFT computation. We devise fault tolerant schemes for both computational and memory errors in FFT, with both serial and parallel optimizations. Experimental results demonstrate that the proposed approach provides more timely error detection and recovery as well as better fault coverage with less overhead, compared to the offline ABFT algorithm.To alleviate the I/O bottleneck in the parallel file systems, we work on a prediction-based error-bounded lossy compressor to significantly reduce the size of scientific datasets while retaining the accuracy of the decompressed data, with adaptive prediction algorithms and compression models. We first propose a regression-based predictor for better prediction accuracy than traditional approaches under large error bounds, followed by an adaptive algorithm that dynamically selects between the traditional Lorenzo predictor and the proposed regression-based predictor, leading to very high compression ratios with little visual distortion. We further unify the prediction-based model and transform-baed model by using transform-based compressors as a predictor, with novel optimizations toward efficient coefficient encoding for both the two models. The proposed adaptive multi-algorithm design provides better compression ratios given the same distortion, significantly reducing storage requirements and I/O time.We further adapt the compression algorithms and compressors to different requirements and/or objectives in realistic scenarios. We leverage a logarithmic transform to precondition the data, which turns a relative-error-bound compression problem into an absolute-error-bound compression problem. This transform aligns two different error requirements while improving the compression quality, efficiently reducing the workload for compressor design. We also correlate the compression algorithm with system information to achieve better I/O performance compared to traditional single compressor deployment. These studies further improve the efficiency of lossy compression from the perspective of efficient I/O in the context of scientific simulation, making scientific applications running on exascale systems more efficient