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

Temporal Video Transcoding in Mobile Systems

La tesi analizza il problema della transcodifica temporale per la trasmissione del video in tempo reale su reti mobili. Viene proposta un’architettura di transcodifica temporale e un nuovo algoritmo di ricalcolo dei vettori di moto per il transcoder temporale H.264. Per fronteggiare il problema della riduzione costante della banda del canale wireless nelle reti infrastrutturate, vengono proposte diverse politiche di frame skipping basate sul dimensionamento del buffer del transcoder per garantire una comunicazione in tempo reale. Il moto di un frame e il numero di frames consecutivi scartati vengono inoltre considerati per migliorare la qualità del video transcodificato. E’ stato inoltre proposto e studiato un sistema di trasmissione video per reti veicolari con protocollo IEEE 802.11, basato su transcodifica temporale. Questo sistema permette di scartare quei frames il cui tempo di trasmissione supera un massimo ritardo ammisssibile al di sopra del quale tali frames non verrebbero comunque visualizzati. Il sistema proposto permette un notevole risparmio di banda e migliora la qualità del video evitando che molti frames consecutivi vengano scartati a causa della congestione

Optimization of the motion estimation for parallel embedded systems in the context of new video standards

15 pagesInternational audienceThe effciency of video compression methods mainly depends on the motion compensation stage, and the design of effcient motion estimation techniques is still an important issue. An highly accurate motion estimation can significantly reduce the bit-rate, but involves a high computational complexity. This is particularly true for new generations of video compression standards, MPEG AVC and HEVC, which involves techniques such as different reference frames, sub-pixel estimation, variable block sizes. In this context, the design of fast motion estimation solutions is necessary, and can concerned two linked aspects: a high quality algorithm and its effcient implementation. This paper summarizes our main contributions in this domain. In particular, we first present the HME (Hierarchical Motion Estimation) technique. It is based on a multi-level refinement process where the motion estimation vectors are first estimated on a sub-sampled image. The multi-levels decomposition provides robust predictions and is particularly suited for variable block sizes motion estimations. The HME method has been integrated in a AVC encoder, and we propose a parallel implementation of this technique, with the motion estimation at pixel level performed by a DSP processor, and the sub-pixel refinement realized in an FPGA. The second technique that we present is called HDS for Hierarchical Diamond Search. It combines the multi-level refinement of HME, with a fast search at pixel-accuracy inspired by the EPZS method. This paper also presents its parallel implementation onto a multi-DSP platform and the its use in the HEVC context

Automatic extraction and tracking of face sequences in MPEG video

Ph.DDOCTOR OF PHILOSOPH

Advanced heterogeneous video transcoding

PhDVideo transcoding is an essential tool to promote inter-operability between different video communication systems. This thesis presents two novel video transcoders, both operating on bitstreams of the cur- rent H.264/AVC standard. The first transcoder converts H.264/AVC bitstreams to a Wavelet Scalable Video Codec (W-SVC), while the second targets the emerging High Efficiency Video Coding (HEVC). Scalable Video Coding (SVC) enables low complexity adaptation of compressed video, providing an efficient solution for content delivery through heterogeneous networks. The transcoder proposed here aims at exploiting the advantages offered by SVC technology when dealing with conventional coders and legacy video, efficiently reusing information found in the H.264/AVC bitstream to achieve a high rate-distortion performance at a low complexity cost. Its main features include new mode mapping algorithms that exploit the W-SVC larger macroblock sizes, and a new state-of-the-art motion vector composition algorithm that is able to tackle different coding configurations in the H.264/AVC bitstream, including IPP or IBBP with multiple reference frames. The emerging video coding standard, HEVC, is currently approaching the final stage of development prior to standardization. This thesis proposes and evaluates several transcoding algorithms for the HEVC codec. In particular, a transcoder based on a new method that is capable of complexity scalability, trading off rate-distortion performance for complexity reduction, is proposed. Furthermore, other transcoding solutions are explored, based on a novel content-based modeling approach, in which the transcoder adapts its parameters based on the contents of the sequence being encoded. Finally, the application of this research is not constrained to these transcoders, as many of the techniques developed aim to contribute to advance the research on this field, and have the potential to be incorporated in different video transcoding architectures

Adaptive video delivery using semantics

The diffusion of network appliances such as cellular phones, personal digital assistants and hand-held computers has created the need to personalize the way media content is delivered to the end user. Moreover, recent devices, such as digital radio receivers with graphics displays, and new applications, such as intelligent visual surveillance, require novel forms of video analysis for content adaptation and summarization. To cope with these challenges, we propose an automatic method for the extraction of semantics from video, and we present a framework that exploits these semantics in order to provide adaptive video delivery. First, an algorithm that relies on motion information to extract multiple semantic video objects is proposed. The algorithm operates in two stages. In the first stage, a statistical change detector produces the segmentation of moving objects from the background. This process is robust with regard to camera noise and does not need manual tuning along a sequence or for different sequences. In the second stage, feedbacks between an object partition and a region partition are used to track individual objects along the frames. These interactions allow us to cope with multiple, deformable objects, occlusions, splitting, appearance and disappearance of objects, and complex motion. Subsequently, semantics are used to prioritize visual data in order to improve the performance of adaptive video delivery. The idea behind this approach is to organize the content so that a particular network or device does not inhibit the main content message. Specifically, we propose two new video adaptation strategies. The first strategy combines semantic analysis with a traditional frame-based video encoder. Background simplifications resulting from this approach do not penalize overall quality at low bitrates. The second strategy uses metadata to efficiently encode the main content message. The metadata-based representation of object's shape and motion suffices to convey the meaning and action of a scene when the objects are familiar. The impact of different video adaptation strategies is then quantified with subjective experiments. We ask a panel of human observers to rate the quality of adapted video sequences on a normalized scale. From these results, we further derive an objective quality metric, the semantic peak signal-to-noise ratio (SPSNR), that accounts for different image areas and for their relevance to the observer in order to reflect the focus of attention of the human visual system. At last, we determine the adaptation strategy that provides maximum value for the end user by maximizing the SPSNR for given client resources at the time of delivery. By combining semantic video analysis and adaptive delivery, the solution presented in this dissertation permits the distribution of video in complex media environments and supports a large variety of content-based applications

Scalable and network aware video coding for advanced communications over heterogeneous networks

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityThis work addresses the issues concerned with the provision of scalable video services over heterogeneous networks particularly with regards to dynamic adaptation and user’s acceptable quality of service. In order to provide and sustain an adaptive and network friendly multimedia communication service, a suite of techniques that achieved automatic scalability and adaptation are developed. These techniques are evaluated objectively and subjectively to assess the Quality of Service (QoS) provided to diverse users with variable constraints and dynamic resources. The research ensured the consideration of various levels of user acceptable QoS The techniques are further evaluated with view to establish their performance against state of the art scalable and non-scalable techniques. To further improve the adaptability of the designed techniques, several experiments and real time simulations are conducted with the aim of determining the optimum performance with various coding parameters and scenarios. The coding parameters and scenarios are evaluated and analyzed to determine their performance using various types of video content and formats. Several algorithms are developed to provide a dynamic adaptation of coding tools and parameters to specific video content type, format and bandwidth of transmission. Due to the nature of heterogeneous networks where channel conditions, terminals, users capabilities and preferences etc are unpredictably changing, hence limiting the adaptability of a specific technique adopted, a Dynamic Scalability Decision Making Algorithm (SADMA) is developed. The algorithm autonomously selects one of the designed scalability techniques basing its decision on the monitored and reported channel conditions. Experiments were conducted using a purpose-built heterogeneous network simulator and the network-aware selection of the scalability techniques is based on real time simulation results. A technique with a minimum delay, low bit-rate, low frame rate and low quality is adopted as a reactive measure to a predicted bad channel condition. If the use of the techniques is not favoured due to deteriorating channel conditions reported, a reduced layered stream or base layer is used. If the network status does not allow the use of the base layer, then the stream uses parameter identifiers with high efficiency to improve the scalability and adaptation of the video service. To further improve the flexibility and efficiency of the algorithm, a dynamic de-blocking filter and lambda value selection are analyzed and introduced in the algorithm. Various methods, interfaces and algorithms are defined for transcoding from one technique to another and extracting sub-streams when the network conditions do not allow for the transmission of the entire bit-stream