28 research outputs found

    Efficient Coding Tree Unit (CTU) Decision Method for Scalable High-Efficiency Video Coding (SHVC) Encoder

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    High-efficiency video coding (HEVC or H.265) is the latest video compression standard developed by the joint collaborative team on video coding (JCT-VC), finalized in 2013. HEVC can achieve an average bit rate decrease of 50% in comparison with H.264/AVC while still maintaining video quality. To upgrade the HEVC used in heterogeneous access networks, the JVT-VC has been approved scalable extension of HEVC (SHVC) in July 2014. The SHVC can achieve the highest coding efficiency but requires a very high computational complexity such that its real-time application is limited. To reduce the encoding complexity of SHVC, in this chapter, we employ the temporal-spatial and inter-layer correlations between base layer (BL) and enhancement layer (EL) to predict the best quadtree of coding tree unit (CTU) for quality SHVC. Due to exist a high correlation between layers, we utilize the coded information from the CTU quadtree in BL, including inter-layer intra/residual prediction and inter-layer motion parameter prediction, to predict the CTU quadtree in EL. Therefore, we develop an efficient CTU decision method by combing temporal-spatial searching order algorithm (TSSOA) in BL and a fast inter-layer searching algorithm (FILSA) in EL to speed up the encoding process of SHVC. The simulation results show that the proposed efficient CTU decision method can achieve an average time improving ratio (TIR) about 52–78% and 47–69% for low delay (LD) and random access (RA) configurations, respectively. It is clear that the proposed method can efficiently reduce the computational complexity of SHVC encoder with negligible loss of coding efficiency with various types of video sequences

    Complexity control of HEVC through quadtree depth estimation

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    Optimisation du codage HEVC par des moyens de pré-analyse et/ou de pré-codage du contenu

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    La compression vidéo HEVC standardisée en 2013 offre des gains de compression supérieurs dépassant les 50% par rapport au standard de compression précédent MPEG4-AVC/H.264. Ces gains de compression se paient par une augmentation très importante de la complexité de codage. Si on ajoute à cela l’augmentation de complexité générée par l’accroissement de résolution et de fréquence images du signal vidéo d’entrée pour passer de la Haute Définition (HD) à l’Ultra Haute Définition (UHD), on comprend vite l’intérêt de techniques de réduction de complexité pour le développement de codeurs économiquement viables. En premier lieu, un effort particulier a été réalisé pour réduirela complexité des images Intra. Nous proposons une méthode d’inférence des modes de codage à partir d’un pré-codage d’un version réduite en HD de la vidéo UHD. Ensuite, nous proposons une méthode de partitionnement rapide basée sur la pré-analyse du contenu. La première méthode offre une réduction de complexité d’un facteur 3 et la deuxième, d’un facteur 6, contre une perte de compression proche de 5%. En second lieu, nous avons traité le codage des images Inter. En mettant en oeuvre une solution d’inférence des modes de codage UHD à partir d’un pré-codage au format HD, la complexité de codage est réduite d’un facteur 3 en considérant les 2 flux produits et d’un facteur 9.2 sur le seul flux UHD, pour une perte en compression proche de 3%. Appliqué à une configuration de codage proche d’un système réellement déployé, l’apport de notre algorithme reste intéressant puisqu’il réduit la complexité de codage du flux UHD d’un facteur proche de 2 pour une perte de compression limitée à 4%. Les stratégies de réduction de complexité mises en oeuvre au cours de cette thèse pour le codage Intra et Inter offrent des perspectives intéressantes pour le développement de codeurs HEVC UHD plus économes en ressources de calculs. Elles sont particulièrement adaptées au domaine de la WebTV/OTT qui prend une part croissante dans la diffusion de la vidéo et pour lequel le signal vidéo est codé à des résolutions multiples pour adresser des réseaux et des terminaux de capacités variées.The High Efficiency Video Coding (HEVC) standard was released in 2013 which reduced network bandwidth by a factor of 2 compared to the prior standard H.264/AVC. These gains are achieved by a very significant increase in the encoding complexity. Especially with the industrial demand to shift in format from High Definition (HD) to Ultra High Definition (UHD), one can understand the relevance of complexity reduction techniques to develop cost-effective encoders. In our first contribution, we attempted new strategies to reduce the encoding complexity of Intra-pictures. We proposed a method with inference rules on the coding modes from the modes obtained with pre-encoding of the UHD video down-sampled in HD. We, then, proposed a fast partitioning method based on a preanalysis of the content. The first method reduced the complexity by a factor of 3x and the second one, by a factor of 6, with a loss of compression efficiency of 5%. As a second contribution, we adressedthe Inter-pictures. By implementing inference rules in the UHD encoder, from a HD pre-encoding pass, the encoding complexity is reduced by a factor of 3x when both HD and UHD encodings are considered, and by 9.2x on just the UHD encoding, with a loss of compression efficiency of 3%. Combined with an encoding configuration imitating a real system, our approach reduces the complexity by a factor of close to 2x with 4% of loss. These strategies built during this thesis offer encouraging prospects for implementation of low complexity HEVC UHD encoders. They are fully adapted to the WebTV/OTT segment that is playing a growing part in the video delivery, in which the video signal is encoded with different resolution to reach heterogeneous devices and network capacities

    HEVC의 소수 단위 움직임 추정을 위한 보간 필터 중복 연산 감소 방법

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    학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 8. 이혁재.High-Efficiency Video Coding (HEVC) [1] is the latest video coding standard established by Joint Collaborative Team on Video Coding (JCT-VC) aiming to achieve twice encoding efficiency with comparatively high video quality compared to its predecessor, the H.264 standard. Motion Estimation (ME) which consists of integer motion estimation (IME) and fractional motion estimation (FME) is the bottleneck of HEVC computation. In the execution of the HM reference software, ME alone accounts for about 50 % of the execution time in which IME contributes to about 20 % and FME does around 30% [2].The FMEs enormous computational complexity can be explained by two following reasons: • A large number of FME refinements processed: In HEVC, a frame is divided into CTU, whose size is usually 64x64 pixels. One 64x64 CTU consists of 85 CUs including one 64x64 CU at depth 0, four 32x32 CUs at depth 1, 16 16x16 CUs at depth 2, and 64 8x8 CUs at depth 3. Each CU can be partitioned into PUs according to a set of 8 allowable partition types. An HEVC encoder processes FME refinement for all possible PUs with usually 4 reference frames before deciding the best configuration for a CTU. As a result, typically in HEVCs reference software, HM, for one CTU, it has to process 2,372 FME refinements, which consumes a lot of computational resources. • A complicated and redundant interpolation process: Conventionally, FME refinement, which consists of interpolation and sum of absolute transformed difference (SATD), is processed for every PU in 4 reference frames. As a result, for a 64x64 CTU, in order to process fractional pixel refinement, FME needs to interpolate 6,232,900 fractional pixels. In addition, In HEVC, fractional pixels which consist half fractional pixels and quarter fractional pixels, are interpolated by 8-tap filters and 7-tap filters instead of 6-tap filters and bilinear filters as previous standards. As a result, interpolation process in FME imposes an extreme computational burden on HEVC encoders. This work proposes two algorithms which tackle each one of the two above reasons. The first algorithm, Advanced Decision of PU Partitions and CU Depths for FME, estimates the cost of IMEs and selects the PU partition types at the CU level and the CU depths at the coding tree unit (CTU) level for FME. Experimental results show that the algorithm effectively reduces the complexity by 67.47% with a BD-BR degrade of 1.08%. The second algorithm, A Reduction of the Interpolation Redundancy for FME, reduces up to 86.46% interpolation computation without any encoding performance decrease. The combination of the two algorithms forms a coherent solution to reduce the complexity of FME. Considering interpolation is a half of the complexity of an FME refinement, then the complexity of FME could be reduced more than 85% with a BD-BR increase of 1.66%Chapter 1. Introduction 1 1. Introduction to Video Coding 1 1.1. Definition of Video Coding 1 1.2. The Need of Video Coding 1 1.3. Basics of Video Coding 2 1.4. Video Coding Standard 2 2. Introduction to HEVC 6 2.1. HEVC Background and Development 6 2.2. Block Partitioning Structure in HEVC 9 Chapter 2. Fractional Motion Estimation in HEVC and Related Works on Complexity Reduction 21 1. Motion Estimation 21 2. Fractional Motion Estimation 22 2.1. Interpolation 22 2.2. Sum of Absolute Transformed Difference Calculation 27 2.3. Fractional Motion Estimation Procedure 28 Chapter 3. Complexity Reduction for FME 31 1. Problem Statement and Previous Studies 31 1.1. Problem Statement 31 1.2. Previous Studies 32 2. Proposed Algorithms 34 2.1. Advanced Decision of PU Partitions and CU Depths for Fractional Motion Estimation in HEVC 34 2.2. Range-based interpolation algorithm 40 Chapter 4. Experiment Results 43 1. Advanced Decision of PU Partitions and CU Depths for Fractional Motion Estimation in HEVC Algorithms 43 1.1. Advanced Decision of PU Partitions 43 1.2. Advanced Decision of CU Partitions 47 1.3. Combination of Advanced PU Partition and CU Depth Decision 47 1.4. Comparison with Other Similar Works 48 2. Range-based Algorithm 49 2.1. Software Implementation 49 2.2. Hardware Implementation of the Algorithm 50 Chapter 5. Conclusion 61 Bibliography 64 Abstract in Korean 66Maste

    Efficient bit rate transcoding for high efficiency video coding

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    High efficiency video coding (HEVC) shows a significant advance in compression efficiency and is considered to be the successor of H.264/AVC. To incorporate the HEVC standard into real-life network applications and a diversity of other applications, efficient bit rate adaptation (transrating) algorithms are required. A current problem of transrating for HEVC is the high computational complexity associated with the encoder part of such a cascaded pixel domain transcoder. This paper focuses on deriving an optimal strategy for reducing the transcoding complexity with a complexity-scalable scheme. We propose different transcoding techniques which are able to reduce the transcoding complexity in both CU and PU optimization levels. At the CU level, CUs can be evaluated in top-to-bottom or bottom-to-top flows, in which the coding information of the input video stream is utilized to reduce the number of evaluations or to early terminate certain evaluations. At the PU level, the PU candidates are adaptively selected based on the probability of PU sizes and the co-located input PU partitioning. Moreover, with the use of different proposed methods, a complexity-scalable transrating scheme can be achieved. Furthermore, the transcoding complexity can be effectively controlled by the machine learning based approach. Simulations show that the proposed techniques provide a superior transcoding performance compared to the state-of-the-art related works. Additionally, the proposed methods can achieve a range of trade-offs between transrating complexity and coding performance. From the proposed schemes, the fastest approach is able to reduce the complexity by 82% while keeping the bitrate loss below 3%
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