Heterogeneous Video Transcoder for H.264/AVC to HEVC

The new video coding standard, High Efficiency Video Coding, was developed to succeed the current standard, H.264/Advance Video Coding. However, there is a lot of legacy content encoded with H.264. So the new efficient method is proposed for transcoding the H.264 encoded video into high efficiency video coding format. In proposed method, two stages are implemented. In training stage, transcoding is done using SSD method and different coding parameters or features are extracted from incoming H.264. In transcoding stage, the best mode of outgoing coding unit partitions are decided by calculating threshold value and optimum weight using extracted features. Then it is evaluated by doing experiments on different videos. DOI: 10.17762/ijritcc2321-8169.150615

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

Efficient HEVC-based video adaptation using transcoding

In a video transmission system, it is important to take into account the great diversity of the network/end-user constraints. On the one hand, video content is typically streamed over a network that is characterized by different bandwidth capacities. In many cases, the bandwidth is insufficient to transfer the video at its original quality. On the other hand, a single video is often played by multiple devices like PCs, laptops, and cell phones. Obviously, a single video would not satisfy their different constraints. These diversities of the network and devices capacity lead to the need for video adaptation techniques, e.g., a reduction of the bit rate or spatial resolution. Video transcoding, which modifies a property of the video without the change of the coding format, has been well-known as an efficient adaptation solution. However, this approach comes along with a high computational complexity, resulting in huge energy consumption in the network and possibly network latency. This presentation provides several optimization strategies for the transcoding process of HEVC (the latest High Efficiency Video Coding standard) video streams. First, the computational complexity of a bit rate transcoder (transrater) is reduced. We proposed several techniques to speed-up the encoder of a transrater, notably a machine-learning-based approach and a novel coding-mode evaluation strategy have been proposed. Moreover, the motion estimation process of the encoder has been optimized with the use of decision theory and the proposed fast search patterns. Second, the issues and challenges of a spatial transcoder have been solved by using machine-learning algorithms. Thanks to their great performance, the proposed techniques are expected to significantly help HEVC gain popularity in a wide range of modern multimedia applications

Transcodage rapide de H.264 à HEVC basé sur la propagation du mouvement et une traversée postfixe des unités de codage arborescent

En 2013, l’ITU-T et l’ISO ont publié conjointement le plus récent standard de compression vidéo, appelé HEVC. Comparé à son prédécesseur, H.264, ce nouveau standard réduit le débit d’environ 50% pour une qualité vidéo similaire. Pour bénéficier de cette plus grande efficacité de codage, et pour assurer l’interopérabilité entre les systèmes, plusieurs séquences vidéos H.264 doivent être transcodées (converties) en séquences HEVC. La manière la plus simple de réaliser cette opération consiste à décoder entièrement la séquence H.264 source, puis à la réencoder entièrement à l’aide d’un encodeur HEVC. Cette approche, appelée transcodage en cascade dans le domaine des pixels (TCDP), produit un codage efficace et offre un maximum de flexibilité, notamment en ce qui a trait à la configuration de la séquence de sortie. Cependant, elle est très complexe en calculs. Pour réduire cette complexité, plusieurs approches réutilisent de l’information de codage (vecteurs de mouvement, modes de codage, données résiduelles, etc.) extraite de la séquence H.264, afin d’accélérer certaines étapes de l’encodage HEVC. La majorité de ces approches préserve l’efficacité de codage, mais obtient cependant des accélérations limitées (habituellement, entre 2 et 4x, selon l’approche). Dans cette thèse, nous proposons une approche de transcodage H.264 à HEVC plus rapide que celles présentées dans la littérature. Notre solution est composée d’un algorithme de propagation du mouvement et d’une méthode pour réduire le nombre de modes HEVC à tester. L’algorithme de propagation de mouvement crée une liste des vecteurs de mouvement candidats au niveau des unités de codage arborescent (CTU) et, par la suite, sélectionne le meilleur candidat au niveau des unités de prédiction. Cette méthode élimine la redondance des calculs en précalculant l’erreur de prédiction de chaque candidat au niveau des CTUs, et réutilise cette information pour différentes tailles de partitionnement. Pour sa part, l’algorithme de réduction des modes est basé sur un parcours postfixe de la CTU traitée. Cet algorithme permet notamment d’arrêter prématurément le traitement d’un mode jugé non prometteur. Par rapport à une approche de transcodage TCDP, nos résultats expérimentaux montrent que la solution proposée est en moyenne 7.81 fois plus rapide, pour une augmentation moyenne du BD-Rate de 2.05%. Nous expériences montrent également que les résultats obtenus sont significativement supérieurs à ceux de l’état de l’art