Compression vidéo basée sur l'exploitation d'un décodeur intelligent

This Ph.D. thesis studies the novel concept of Smart Decoder (SDec) where the decoder is given the ability to simulate the encoder and is able to conduct the R-D competition similarly as in the encoder. The proposed technique aims to reduce the signaling of competing coding modes and parameters. The general SDec coding scheme and several practical applications are proposed, followed by a long-term approach exploiting machine learning concept in video coding. The SDec coding scheme exploits a complex decoder able to reproduce the choice of the encoder based on causal references, eliminating thus the need to signal coding modes and associated parameters. Several practical applications of the general outline of the SDec scheme are tested, using different coding modes during the competition on the reference blocs. Despite the choice for the SDec reference block being still simple and limited, interesting gains are observed. The long-term research presents an innovative method that further makes use of the processing capacity of the decoder. Machine learning techniques are exploited in video coding with the purpose of reducing the signaling overhead. Practical applications are given, using a classifier based on support vector machine to predict coding modes of a block. The block classification uses causal descriptors which consist of different types of histograms. Significant bit rate savings are obtained, which confirms the potential of the approach.Cette thèse de doctorat étudie le nouveau concept de décodeur intelligent (SDec) dans lequel le décodeur est doté de la possibilité de simuler l’encodeur et est capable de mener la compétition R-D de la même manière qu’au niveau de l’encodeur. Cette technique vise à réduire la signalisation des modes et des paramètres de codage en compétition. Le schéma général de codage SDec ainsi que plusieurs applications pratiques sont proposées, suivis d’une approche en amont qui exploite l’apprentissage automatique pour le codage vidéo. Le schéma de codage SDec exploite un décodeur complexe capable de reproduire le choix de l’encodeur calculé sur des blocs de référence causaux, éliminant ainsi la nécessité de signaler les modes de codage et les paramètres associés. Plusieurs applications pratiques du schéma SDec sont testées, en utilisant différents modes de codage lors de la compétition sur les blocs de référence. Malgré un choix encore simple et limité des blocs de référence, les gains intéressants sont observés. La recherche en amont présente une méthode innovante qui permet d’exploiter davantage la capacité de traitement d’un décodeur. Les techniques d’apprentissage automatique sont exploitées pour but de réduire la signalisation. Les applications pratiques sont données, utilisant un classificateur basé sur les machines à vecteurs de support pour prédire les modes de codage d’un bloc. La classification des blocs utilise des descripteurs causaux qui sont formés à partir de différents types d’histogrammes. Des gains significatifs en débit sont obtenus, confirmant ainsi le potentiel de l’approche

Non-MPM Mode Coding for Intra Prediction in Video Coding

The High Efficiency Video Coding standard introduced thirty-five intra prediction modes. It employed a method based on three most probable modes (MPM) to improve intra mode coding. This method significantly improved the performance by extracting three MPMs out of the thirty-five intra modes. The Joint Video Exploration Team (JVET) defines sixty-seven intra prediction modes for a possible future video coding standard. In the latest JVET development, six MPMs are chosen, and the remaining sixty-one modes are divided into sixteen “selected” and forty-five “non-selected” modes. These non-MPM modes are coded using fixed length coding. This research focusses on finding more efficient ways to code these intra prediction modes, including MPM modes and non-MPM modes. A method is proposed to select and order the sixty-one non-MPM modes based on probability statistics. The modes that fall into selected category are coded using shorter codes and non-selected modes are coded using larger codes, which is in line with the principle of entropy coding. Experimental results prove performance improvement when compared to JEM7.0 software as a reference

Algorithms and Hardware Co-Design of HEVC Intra Encoders

Digital video is becoming extremely important nowadays and its importance has greatly increased in the last two decades. Due to the rapid development of information and communication technologies, the demand for Ultra-High Definition (UHD) video applications is becoming stronger. However, the most prevalent video compression standard H.264/AVC released in 2003 is inefficient when it comes to UHD videos. The increasing desire for superior compression efficiency to H.264/AVC leads to the standardization of High Efficiency Video Coding (HEVC). Compared with the H.264/AVC standard, HEVC offers a double compression ratio at the same level of video quality or substantial improvement of video quality at the same video bitrate. Yet, HE-VC/H.265 possesses superior compression efficiency, its complexity is several times more than H.264/AVC, impeding its high throughput implementation. Currently, most of the researchers have focused merely on algorithm level adaptations of HEVC/H.265 standard to reduce computational intensity without considering the hardware feasibility. What’s more, the exploration of efficient hardware architecture design is not exhaustive. Only a few research works have been conducted to explore efficient hardware architectures of HEVC/H.265 standard. In this dissertation, we investigate efficient algorithm adaptations and hardware architecture design of HEVC intra encoders. We also explore the deep learning approach in mode prediction. From the algorithm point of view, we propose three efficient hardware-oriented algorithm adaptations, including mode reduction, fast coding unit (CU) cost estimation, and group-based CABAC (context-adaptive binary arithmetic coding) rate estimation. Mode reduction aims to reduce mode candidates of each prediction unit (PU) in the rate-distortion optimization (RDO) process, which is both computation-intensive and time-consuming. Fast CU cost estimation is applied to reduce the complexity in rate-distortion (RD) calculation of each CU. Group-based CABAC rate estimation is proposed to parallelize syntax elements processing to greatly improve rate estimation throughput. From the hardware design perspective, a fully parallel hardware architecture of HEVC intra encoder is developed to sustain UHD video compression at 4K@30fps. The fully parallel architecture introduces four prediction engines (PE) and each PE performs the full cycle of mode prediction, transform, quantization, inverse quantization, inverse transform, reconstruction, rate-distortion estimation independently. PU blocks with different PU sizes will be processed by the different prediction engines (PE) simultaneously. Also, an efficient hardware implementation of a group-based CABAC rate estimator is incorporated into the proposed HEVC intra encoder for accurate and high-throughput rate estimation. To take advantage of the deep learning approach, we also propose a fully connected layer based neural network (FCLNN) mode preselection scheme to reduce the number of RDO modes of luma prediction blocks. All angular prediction modes are classified into 7 prediction groups. Each group contains 3-5 prediction modes that exhibit a similar prediction angle. A rough angle detection algorithm is designed to determine the prediction direction of the current block, then a small scale FCLNN is exploited to refine the mode prediction

Efficient Motion Estimation and Mode Decision Algorithms for Advanced Video Coding

H.264/AVC video compression standard achieved significant improvements in coding efficiency, but the computational complexity of the H.264/AVC encoder is drastically high. The main complexity of encoder comes from variable block size motion estimation (ME) and rate-distortion optimized (RDO) mode decision methods. This dissertation proposes three different methods to reduce computation of motion estimation. Firstly, the computation of each distortion measure is reduced by proposing a novel two step edge based partial distortion search (TS-EPDS) algorithm. In this algorithm, the entire macroblock is divided into different sub-blocks and the calculation order of partial distortion is determined based on the edge strength of the sub-blocks. Secondly, we have developed an early termination algorithm that features an adaptive threshold based on the statistical characteristics of rate-distortion (RD) cost regarding current block and previously processed blocks and modes. Thirdly, this dissertation presents a novel adaptive search area selection method by utilizing the information of the previously computed motion vector differences (MVDs). In H.264/AVC intra coding, DC mode is used to predict regions with no unified direction and the predicted pixel values are same and thus smooth varying regions are not well de-correlated. This dissertation proposes an improved DC prediction (IDCP) mode based on the distance between the predicted and reference pixels. On the other hand, using the nine prediction modes in intra 4x4 and 8x8 block units needs a lot of overhead bits. In order to reduce the number of overhead bits, an intra mode bit rate reduction method is suggested. This dissertation also proposes an enhanced algorithm to estimate the most probable mode (MPM) of each block. The MPM is derived from the prediction mode direction of neighboring blocks which have different weights according to their positions. This dissertation also suggests a fast enhanced cost function for mode decision of intra encoder. The enhanced cost function uses sum of absolute Hadamard-transformed differences (SATD) and mean absolute deviation of the residual block to estimate distortion part of the cost function. A threshold based large coefficients count is also used for estimating the bit-rate part

Análise do HEVC escalável : desempenho e controlo de débito

Mestrado em Engenharia Eletrónica e TelecomunicaçõesEsta dissertação apresenta um estudo da norma de codificação de vídeo de alta eficiência (HEVC) e a sua extensão para vídeo escalável, SHVC. A norma de vídeo SHVC proporciona um melhor desempenho quando codifica várias camadas em simultâneo do que quando se usa o codificador HEVC numa configuração simulcast. Ambos os codificadores de referência, tanto para a camada base como para a camada superior usam o mesmo modelo de controlo de débito, modelo R-λ, que foi otimizado para o HEVC. Nenhuma otimização de alocação de débito entre camadas foi até ao momento proposto para o modelo de testes (SHM 8) para a escalabilidade do HEVC (SHVC). Derivamos um novo modelo R-λ apropriado para a camada superior e para o caso de escalabilidade espacial, que conduziu a um ganho de BD-débito de 1,81% e de BD-PSNR de 0,025 em relação ao modelo de débito-distorção existente no SHM do SHVC. Todavia, mostrou-se também nesta dissertação que o proposto modelo de R-λ não deve ser usado na camada inferior (camada base) no SHVC e por conseguinte no HEVC.This dissertation provides a study of the High Efficiency Video Coding standard (HEVC) and its scalable extension, SHVC. The SHVC provides a better performance when encoding several layers simultaneously than using an HEVC encoder in a simulcast configuration. Both reference encoders, in the base layer and in the enhancement layer use the same rate control model, R-λ model, which was optimized for HEVC. No optimal bitrate partitioning amongst layers is proposed in scalable HEVC (SHVC) test model (SHM 8). We derived a new R-λ model for the enhancement layer and for the spatial case which led to a DB-rate gain of 1.81% and DB-PSNR gain of 0.025 in relation to the rate-distortion model of SHM-SHVC. Nevertheless, we also show in this dissertation that the proposed model of R-λ should not be used neither in the base layer nor in HEVC

Towards visualization and searching :a dual-purpose video coding approach

In modern video applications, the role of the decoded video is much more than filling a screen for visualization. To offer powerful video-enabled applications, it is increasingly critical not only to visualize the decoded video but also to provide efficient searching capabilities for similar content. Video surveillance and personal communication applications are critical examples of these dual visualization and searching requirements. However, current video coding solutions are strongly biased towards the visualization needs. In this context, the goal of this work is to propose a dual-purpose video coding solution targeting both visualization and searching needs by adopting a hybrid coding framework where the usual pixel-based coding approach is combined with a novel feature-based coding approach. In this novel dual-purpose video coding solution, some frames are coded using a set of keypoint matches, which not only allow decoding for visualization, but also provide the decoder valuable feature-related information, extracted at the encoder from the original frames, instrumental for efficient searching. The proposed solution is based on a flexible joint Lagrangian optimization framework where pixel-based and feature-based processing are combined to find the most appropriate trade-off between the visualization and searching performances. Extensive experimental results for the assessment of the proposed dual-purpose video coding solution under meaningful test conditions are presented. The results show the flexibility of the proposed coding solution to achieve different optimization trade-offs, notably competitive performance regarding the state-of-the-art HEVC standard both in terms of visualization and searching performance.Em modernas aplicações de vídeo, o papel do vídeo decodificado é muito mais que simplesmente preencher uma tela para visualização. Para oferecer aplicações mais poderosas por meio de sinais de vídeo,é cada vez mais crítico não apenas considerar a qualidade do conteúdo objetivando sua visualização, mas também possibilitar meios de realizar busca por conteúdos semelhantes. Requisitos de visualização e de busca são considerados, por exemplo, em modernas aplicações de vídeo vigilância e comunicações pessoais. No entanto, as atuais soluções de codificação de vídeo são fortemente voltadas aos requisitos de visualização. Nesse contexto, o objetivo deste trabalho é propor uma solução de codificação de vídeo de propósito duplo, objetivando tanto requisitos de visualização quanto de busca. Para isso, é proposto um arcabouço de codificação em que a abordagem usual de codificação de pixels é combinada com uma nova abordagem de codificação baseada em features visuais. Nessa solução, alguns quadros são codificados usando um conjunto de pares de keypoints casados, possibilitando não apenas visualização, mas também provendo ao decodificador valiosas informações de features visuais, extraídas no codificador a partir do conteúdo original, que são instrumentais em aplicações de busca. A solução proposta emprega um esquema flexível de otimização Lagrangiana onde o processamento baseado em pixel é combinado com o processamento baseado em features visuais objetivando encontrar um compromisso adequado entre os desempenhos de visualização e de busca. Os resultados experimentais mostram a flexibilidade da solução proposta em alcançar diferentes compromissos de otimização, nomeadamente desempenho competitivo em relação ao padrão HEVC tanto em termos de visualização quanto de busca

MAP Joint Source-Channel Arithmetic Decoding for Compressed Video

In order to have robust video transmission over error prone telecommunication channels several mechanisms are introduced. These mechanisms try to detect, correct or conceal the errors in the received video stream. In this thesis, the performance of the video codec is improved in terms of error rates without increasing overhead in terms of data bit rate. This is done by exploiting the residual syntactic/semantic redundancy inside compressed video along with optimizing the configuration of the state-of-the art entropy coding, i.e., binary arithmetic coding, and optimizing the quantization of the channel output. The thesis is divided into four phases. In the first phase, a breadth-first suboptimal sequential maximum a posteriori (MAP) decoder is employed for joint source-channel arithmetic decoding of H.264 symbols. The proposed decoder uses not only the intentional redundancy inserted via a forbidden symbol (FS) but also exploits residual redundancy by a syntax checker. In contrast to previous methods this is done as each channel bit is decoded. Simulations using intra prediction modes show improvements in error rates, e.g., syntax element error rate reduction by an order of magnitude for channel SNR of 7.33dB. The cost of this improvement is more computational complexity spent on the syntax checking. In the second phase, the configuration of the FS in the symbol set is studied. The delay probability function, i.e., the probability of the number of bits required to detect an error, is calculated for various FS configurations. The probability of missed error detection is calculated as a figure of merit for optimizing the FS configuration. The simulation results show the effectiveness of the proposed figure of merit, and support the FS configuration in which the FS lies entirely between the other information carrying symbols to be the best. In the third phase, a new method for estimating the a priori probability of particular syntax elements is proposed. This estimation is based on the interdependency among the syntax elements that were previously decoded. This estimation is categorized as either reliable or unreliable. The decoder uses this prior information when they are reliable, otherwise the MAP decoder considers that the syntax elements are equiprobable and in turn uses maximum likelihood (ML) decoding. The reliability detection is carried out using a threshold on the local entropy of syntax elements in the neighboring macroblocks. In the last phase, a new measure to assess performance of the channel quantizer is proposed. This measure is based on the statistics of the rank of true candidate among the sorted list of candidates in the MAP decoder. Simulation results shows that a quantizer designed based on the proposed measure is superior to the quantizers designed based on maximum mutual information and minimum mean square error

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure