Efficient Coding of Transform Coefficient Levels in Hybrid Video Coding

All video coding standards of practical importance, such as Advanced Video Coding (AVC), its successor High Efficiency Video Coding (HEVC), and the state-of-the-art Versatile Video Coding (VVC), follow the basic principle of block-based hybrid video coding. In such an architecture, the video pictures are partitioned into blocks. Each block is first predicted by either intra-picture or motion-compensated prediction, and the resulting prediction errors, referred to as residuals, are compressed using transform coding. This thesis deals with the entropy coding of quantization indices for transform coefficients, also referred to as transform coefficient levels, as well as the entropy coding of directly quantized residual samples. The entropy coding of quantization indices is referred to as level coding in this thesis. The presented developments focus on both improving the coding efficiency and reducing the complexity of the level coding for HEVC and VVC. These goals were achieved by modifying the context modeling and the binarization of the level coding. The first development presented in this thesis is a transform coefficient level coding for variable transform block sizes, which was introduced in HEVC. It exploits the fact that non-zero levels are typically concentrated in certain parts of the transform block by partitioning blocks larger than \square{4} samples into \square{4} sub-blocks. Each \square{4} sub-block is then coded similarly to the level coding specified in AVC for \square{4} transform blocks. This sub-block processing improves coding efficiency and has the advantage that the number of required context models is independent of the set of supported transform block sizes. The maximum number of context-coded bins for a transform coefficient level is one indicator for the complexity of the entropy coding. An adaptive binarization of absolute transform coefficient levels using Rice codes is presented that reduces the maximum number of context-coded bins from 15 (as used in AVC) to three for HEVC. Based on the developed selection of an appropriate Rice code for each scanning position, this adaptive binarization achieves virtually the same coding efficiency as the binarization specified in AVC for bit-rate operation points typically used in consumer applications. The coding efficiency is improved for high bit-rate operation points, which are used in more advanced and professional applications. In order to further improve the coding efficiency for HEVC and VVC, the statistical dependencies among the transform coefficient levels of a transform block are exploited by a template-based context modeling developed in this thesis. Instead of selecting the context model for a current scanning position primarily based on its location inside a transform block, already coded neighboring locations inside a local template are utilized. To further increase the coding efficiency achieved by the template-based context modeling, the different coding phases of the initially developed level coding are merged into a single coding phase. As a consequence, the template-based context modeling can utilize the absolute levels of the neighboring frequency locations, which provides better conditional probability estimates and further improves coding efficiency. This template-based context modeling with a single coding phase is also suitable for trellis-coded quantization (TCQ), since TCQ is state-driven and derives the next state from the current state and the parity of the current level. TCQ introduces different context model sets for coding the significance flag depending on the current state. Based on statistical analyses, an extension of the state-dependent context modeling of TCQ is presented, which further improves the coding efficiency in VVC. After that, a method to reduce the complexity of the level coding at the decoder is presented. This method separates the level coding into a coding phase exclusively consisting of context-coded bins and another one consisting of bypass-coded bins only. For retaining the state-dependent context selection, which significantly contributes to the coding efficiency of TCQ, a dedicated parity flag is introduced and coded with context models in the first coding phase. An adaptive approach is then presented that further reduces the worst-case complexity, effectively lowering the maximum number of context-coded bins per transform coefficient to 1.75 without negatively affecting the coding efficiency. In the last development presented in this thesis, a dedicated level coding for transform skip blocks, which often occur in screen content applications, is introduced for VVC. This dedicated level coding better exploits the statistical properties of directly quantized residual samples for screen content. Various modifications to the level coding improve the coding efficiency for this type of content. Examples for these modifications are a binarization with additional context-coded flags and the coding of the sign information with adaptive context models

Efficient H.264 intra Frame CODEC with Best prediction matrix mode algorithm

The continuous growth of smart communities and everincreasingdemand of sending or storing videos, have led toconsumption of huge amount of data. The video compressiontechniques are solving this emerging challenge. However, H.264standard can be considered most notable, and it has proven to meetproblematic requirements. The authors present (BPMM) as a novelefficient Intra prediction scheme. We can say that the creation of ourproposed technique was in a phased manner; it\u27s emerged as aproposal and achieved impressive results in the performanceparameters as compression ratios, bit rates, and PSNR. Then in thesecond stage, we solved the challenges of overcoming the obstacle ofencoding bits overhead. In this research, we try to address the finalphase of the (BPMM) codec and to introduce our approach in a globalmanner through realization of decoding mechanism. For evaluation ofour scheme, we utilized VHDL as a platform. Final results haveproven our success to pass bottleneck of this phase, since the decodedvideos have the same PSNR that our encoder tells us, whilepreserving steady compression ratio treating the overhead. We aspireour BPMM algorithm will be adopted as reference design of H.264 inthe ITU

Motion estimation and CABAC VLSI co-processors for real-time high-quality H.264/AVC video coding

Real-time and high-quality video coding is gaining a wide interest in the research and industrial community for different applications. H.264/AVC, a recent standard for high performance video coding, can be successfully exploited in several scenarios including digital video broadcasting, high-definition TV and DVD-based systems, which require to sustain up to tens of Mbits/s. To that purpose this paper proposes optimized architectures for H.264/AVC most critical tasks, Motion estimation and context adaptive binary arithmetic coding. Post synthesis results on sub-micron CMOS standard-cells technologies show that the proposed architectures can actually process in real-time 720 × 480 video sequences at 30 frames/s and grant more than 50 Mbits/s. The achieved circuit complexity and power consumption budgets are suitable for their integration in complex VLSI multimedia systems based either on AHB bus centric on-chip communication system or on novel Network-on-Chip (NoC) infrastructures for MPSoC (Multi-Processor System on Chip

Efficient H.264 intra Frame CODEC with Best prediction matrix mode algorithm

The continuous growth of smart communities and everincreasingdemand of sending or storing videos, have led toconsumption of huge amount of data. The video compressiontechniques are solving this emerging challenge. However, H.264standard can be considered most notable, and it has proven to meetproblematic requirements. The authors present (BPMM) as a novelefficient Intra prediction scheme. We can say that the creation of ourproposed technique was in a phased manner; it's emerged as aproposal and achieved impressive results in the performanceparameters as compression ratios, bit rates, and PSNR. Then in thesecond stage, we solved the challenges of overcoming the obstacle ofencoding bits overhead. In this research, we try to address the finalphase of the (BPMM) codec and to introduce our approach in a globalmanner through realization of decoding mechanism. For evaluation ofour scheme, we utilized VHDL as a platform. Final results haveproven our success to pass bottleneck of this phase, since the decodedvideos have the same PSNR that our encoder tells us, whilepreserving steady compression ratio treating the overhead. We aspireour BPMM algorithm will be adopted as reference design of H.264 inthe ITU

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