Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-prediction

The exorbitant increase in the computational complexity of modern video coding standards, such as High Efficiency Video Coding (HEVC), is a compelling challenge for resource-constrained consumer electronic devices. For instance, the brute force evaluation of all possible combinations of available coding modes and quadtree-based coding structure in HEVC to determine the optimum set of coding parameters for a given content demand a substantial amount of computational and energy resources. Thus, the resource requirements for real time operation of HEVC has become a contributing factor towards the Quality of Experience (QoE) of the end users of emerging multimedia and future internet applications. In this context, this paper proposes a content-adaptive Coding Unit (CU) size selection algorithm for HEVC intra-prediction. The proposed algorithm builds content-specific weighted Support Vector Machine (SVM) models in real time during the encoding process, to provide an early estimate of CU size for a given content, avoiding the brute force evaluation of all possible coding mode combinations in HEVC. The experimental results demonstrate an average encoding time reduction of 52.38%, with an average Bjøntegaard Delta Bit Rate (BDBR) increase of 1.19% compared to the HM16.1 reference encoder. Furthermore, the perceptual visual quality assessments conducted through Video Quality Metric (VQM) show minimal visual quality impact on the reconstructed videos of the proposed algorithm compared to state-of-the-art approaches

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

Bayesian adaptive algorithm for fast coding unit decision in the High Efficiency Video Coding (HEVC) standard

The latest High Efficiency Video Coding standard (HEVC) provides a set of new coding tools to achieve a significantly higher coding efficiency than previous standards. In this standard, the pixels are first grouped into Coding Units (CU), then Prediction Units (PU), and finally Transform Units (TU). All these coding levels are organized into a quadtree-shaped arrangement that allows highly flexible data representation; however, they involve a very high computational complexity. In this paper, we propose an effective early CU depth decision algorithm to reduce the encoder complexity. Our proposal is based on a hierarchical approach, in which a hypothesis test is designed to make a decision at every CU depth, where the algorithm either produces an early termination or decides to evaluate the subsequent depth level. Moreover, the proposed method is able to adaptively estimate the parameters that define each hypothesis test, so that it adapts its behavior to the variable contents of the video sequences. The proposed method has been extensively tested, and the experimental results show that our proposal outperforms several state-of-the-art methods, achieving a significant reduction of the computational complexity (36.5% and 38.2% average reductions in coding time for two different encoder configurations) in exchange for very slight losses in coding performance (1.7% and 0.8% average bit rate increments).This work has been partially supported by the National Grant TEC2014-53390-P of the Spanish Ministry of Economy and Competitiveness

Algorithms and methods for video transcoding.

Video transcoding is the process of dynamic video adaptation. Dynamic video adaptation can be defined as the process of converting video from one format to another, changing the bit rate, frame rate or resolution of the encoded video, which is mainly necessitated by the end user requirements. H.264 has been the predominantly used video compression standard for the last 15 years. HEVC (High Efficiency Video Coding) is the latest video compression standard finalised in 2013, which is an improvement over H.264 video compression standard. HEVC performs significantly better than H.264 in terms of the Rate-Distortion performance. As H.264 has been widely used in the last decade, a large amount of video content exists in H.264 format. There is a need to convert H.264 video content to HEVC format to achieve better Rate-Distortion performance and to support legacy video formats on newer devices. However, the computational complexity of HEVC encoder is 2-10 times higher than that of H.264 encoder. This makes it necessary to develop low complexity video transcoding algorithms to transcode from H.264 to HEVC format. This research work proposes low complexity algorithms for H.264 to HEVC video transcoding. The proposed algorithms reduce the computational complexity of H.264 to HEVC video transcoding significantly, with negligible loss in Rate-Distortion performance. This work proposes three different video transcoding algorithms. The MV-based mode merge algorithm uses the block mode and MV variances to estimate the split/non-split decision as part of the HEVC block prediction process. The conditional probability-based mode mapping algorithm models HEVC blocks of sizes 16×16 and lower as a function of H.264 block modes, H.264 and HEVC Quantisation Parameters (QP). The motion-compensated MB residual-based mode mapping algorithm makes the split/non-split decision based on content-adaptive classification models. With a combination of the proposed set of algorithms, the computational complexity of the HEVC encoder is reduced by around 60%, with negligible loss in Rate-Distortion performance, outperforming existing state-of-art algorithms by 20-25% in terms of computational complexity. The proposed algorithms can be used in computation-constrained video transcoding applications, to support video format conversion in smart devices, migration of large-scale H.264 video content from host servers to HEVC, cloud computing-based transcoding applications, and also to support high quality videos over bandwidth-constrained networks

A Motion Estimation based Algorithm for Encoding Time Reduction in HEVC

High Efficiency Video Coding (HEVC) is a video compression standard that offers 50% more efficiency at the expense of high encoding time contrasted with the H.264 Advanced Video Coding (AVC) standard. The encoding time must be reduced to satisfy the needs of real-time applications. This paper has proposed the Multi- Level Resolution Vertical Subsampling (MLRVS) algorithm to reduce the encoding time. The vertical subsampling minimizes the number of Sum of Absolute Difference (SAD) computations during the motion estimation process. The complexity reduction algorithm is also used for fast coding the coefficients of the quantised block using a flag decision. Two distinct search patterns are suggested: New Cross Diamond Diamond (NCDD) and New Cross Diamond Hexagonal (NCDH) search patterns, which reduce the time needed to locate the motion vectors. In this paper, the MLRVS algorithm with NCDD and MLRVS algorithm with NCDH search patterns are simulated separately and analyzed. The results show that the encoding time of the encoder is decreased by 55% with MLRVS algorithm using NCDD search pattern and 56% with MLRVS using NCDH search pattern compared to HM16.5 with Test Zone (TZ) search algorithm. These results are achieved with a slight increase in bit rate and negligible deterioration in output video quality

Low Complexity Mode Decision for 3D-HEVC

High efficiency video coding- (HEVC-) based 3D video coding (3D-HEVC) developed by joint collaborative team on 3D video coding (JCT-3V) for multiview video and depth map is an extension of HEVC standard. In the test model of 3D-HEVC, variable coding unit (CU) size decision and disparity estimation (DE) are introduced to achieve the highest coding efficiency with the cost of very high computational complexity. In this paper, a fast mode decision algorithm based on variable size CU and DE is proposed to reduce 3D-HEVC computational complexity. The basic idea of the method is to utilize the correlations between depth map and motion activity in prediction mode where variable size CU and DE are needed, and only in these regions variable size CU and DE are enabled. Experimental results show that the proposed algorithm can save about 43% average computational complexity of 3D-HEVC while maintaining almost the same rate-distortion (RD) performance