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

Towards visualization and searching :a dual-purpose video coding approach

In modern video applications, the role of the decoded video is much more than filling a screen for visualization. To offer powerful video-enabled applications, it is increasingly critical not only to visualize the decoded video but also to provide efficient searching capabilities for similar content. Video surveillance and personal communication applications are critical examples of these dual visualization and searching requirements. However, current video coding solutions are strongly biased towards the visualization needs. In this context, the goal of this work is to propose a dual-purpose video coding solution targeting both visualization and searching needs by adopting a hybrid coding framework where the usual pixel-based coding approach is combined with a novel feature-based coding approach. In this novel dual-purpose video coding solution, some frames are coded using a set of keypoint matches, which not only allow decoding for visualization, but also provide the decoder valuable feature-related information, extracted at the encoder from the original frames, instrumental for efficient searching. The proposed solution is based on a flexible joint Lagrangian optimization framework where pixel-based and feature-based processing are combined to find the most appropriate trade-off between the visualization and searching performances. Extensive experimental results for the assessment of the proposed dual-purpose video coding solution under meaningful test conditions are presented. The results show the flexibility of the proposed coding solution to achieve different optimization trade-offs, notably competitive performance regarding the state-of-the-art HEVC standard both in terms of visualization and searching performance.Em modernas aplicações de vídeo, o papel do vídeo decodificado é muito mais que simplesmente preencher uma tela para visualização. Para oferecer aplicações mais poderosas por meio de sinais de vídeo,é cada vez mais crítico não apenas considerar a qualidade do conteúdo objetivando sua visualização, mas também possibilitar meios de realizar busca por conteúdos semelhantes. Requisitos de visualização e de busca são considerados, por exemplo, em modernas aplicações de vídeo vigilância e comunicações pessoais. No entanto, as atuais soluções de codificação de vídeo são fortemente voltadas aos requisitos de visualização. Nesse contexto, o objetivo deste trabalho é propor uma solução de codificação de vídeo de propósito duplo, objetivando tanto requisitos de visualização quanto de busca. Para isso, é proposto um arcabouço de codificação em que a abordagem usual de codificação de pixels é combinada com uma nova abordagem de codificação baseada em features visuais. Nessa solução, alguns quadros são codificados usando um conjunto de pares de keypoints casados, possibilitando não apenas visualização, mas também provendo ao decodificador valiosas informações de features visuais, extraídas no codificador a partir do conteúdo original, que são instrumentais em aplicações de busca. A solução proposta emprega um esquema flexível de otimização Lagrangiana onde o processamento baseado em pixel é combinado com o processamento baseado em features visuais objetivando encontrar um compromisso adequado entre os desempenhos de visualização e de busca. Os resultados experimentais mostram a flexibilidade da solução proposta em alcançar diferentes compromissos de otimização, nomeadamente desempenho competitivo em relação ao padrão HEVC tanto em termos de visualização quanto de busca

Algorithms and Hardware Co-Design of HEVC Intra Encoders

Digital video is becoming extremely important nowadays and its importance has greatly increased in the last two decades. Due to the rapid development of information and communication technologies, the demand for Ultra-High Definition (UHD) video applications is becoming stronger. However, the most prevalent video compression standard H.264/AVC released in 2003 is inefficient when it comes to UHD videos. The increasing desire for superior compression efficiency to H.264/AVC leads to the standardization of High Efficiency Video Coding (HEVC). Compared with the H.264/AVC standard, HEVC offers a double compression ratio at the same level of video quality or substantial improvement of video quality at the same video bitrate. Yet, HE-VC/H.265 possesses superior compression efficiency, its complexity is several times more than H.264/AVC, impeding its high throughput implementation. Currently, most of the researchers have focused merely on algorithm level adaptations of HEVC/H.265 standard to reduce computational intensity without considering the hardware feasibility. What’s more, the exploration of efficient hardware architecture design is not exhaustive. Only a few research works have been conducted to explore efficient hardware architectures of HEVC/H.265 standard. In this dissertation, we investigate efficient algorithm adaptations and hardware architecture design of HEVC intra encoders. We also explore the deep learning approach in mode prediction. From the algorithm point of view, we propose three efficient hardware-oriented algorithm adaptations, including mode reduction, fast coding unit (CU) cost estimation, and group-based CABAC (context-adaptive binary arithmetic coding) rate estimation. Mode reduction aims to reduce mode candidates of each prediction unit (PU) in the rate-distortion optimization (RDO) process, which is both computation-intensive and time-consuming. Fast CU cost estimation is applied to reduce the complexity in rate-distortion (RD) calculation of each CU. Group-based CABAC rate estimation is proposed to parallelize syntax elements processing to greatly improve rate estimation throughput. From the hardware design perspective, a fully parallel hardware architecture of HEVC intra encoder is developed to sustain UHD video compression at 4K@30fps. The fully parallel architecture introduces four prediction engines (PE) and each PE performs the full cycle of mode prediction, transform, quantization, inverse quantization, inverse transform, reconstruction, rate-distortion estimation independently. PU blocks with different PU sizes will be processed by the different prediction engines (PE) simultaneously. Also, an efficient hardware implementation of a group-based CABAC rate estimator is incorporated into the proposed HEVC intra encoder for accurate and high-throughput rate estimation. To take advantage of the deep learning approach, we also propose a fully connected layer based neural network (FCLNN) mode preselection scheme to reduce the number of RDO modes of luma prediction blocks. All angular prediction modes are classified into 7 prediction groups. Each group contains 3-5 prediction modes that exhibit a similar prediction angle. A rough angle detection algorithm is designed to determine the prediction direction of the current block, then a small scale FCLNN is exploited to refine the mode prediction

Adaptive Content Frame Skipping for Wyner–Ziv-Based Light Field Image Compression

Light field (LF) imaging introduces attractive possibilities for digital imaging, such as digital focusing, post-capture changing of the focal plane or view point, and scene depth estimation, by capturing both spatial and angular information of incident light rays. However, LF image compression is still a great challenge, not only due to light field imagery requiring a large amount of storage space and a large transmission bandwidth, but also due to the complexity requirements of various applications. In this paper, we propose a novel LF adaptive content frame skipping compression solution by following a Wyner–Ziv (WZ) coding approach. In the proposed coding approach, the LF image is firstly converted into a four-dimensional LF (4D-LF) data format. To achieve good compression performance, we select an efficient scanning mechanism to generate a 4D-LF pseudo-sequence by analyzing the content of the LF image with different scanning methods. In addition, to further explore the high frame correlation of the 4D-LF pseudo-sequence, we introduce an adaptive frame skipping algorithm followed by decision tree techniques based on the LF characteristics, e.g., the depth of field and angular information. The experimental results show that the proposed WZ-LF coding solution achieves outstanding rate distortion (RD) performance while having less computational complexity. Notably, a bit rate saving of 53% is achieved compared to the standard high-efficiency video coding (HEVC) Intra codec.</jats:p

Lossless Compression of Medical Image Sequences Using a Resolution Independent Predictor and Block Adaptive Encoding

The proposed block-based lossless coding technique presented in this paper targets at compression of volumetric medical images of 8-bit and 16-bit depth. The novelty of the proposed technique lies in its ability of threshold selection for prediction and optimal block size for encoding. A resolution independent gradient edge detector is used along with the block adaptive arithmetic encoding algorithm with extensive experimental tests to find a universal threshold value and optimal block size independent of image resolution and modality. Performance of the proposed technique is demonstrated and compared with benchmark lossless compression algorithms. BPP values obtained from the proposed algorithm show that it is capable of effective reduction of inter-pixel and coding redundancy. In terms of coding efficiency, the proposed technique for volumetric medical images outperforms CALIC and JPEG-LS by 0.70 % and 4.62 %, respectively

Receiver-Driven Video Adaptation

In the span of a single generation, video technology has made an incredible impact on daily life. Modern use cases for video are wildly diverse, including teleconferencing, live streaming, virtual reality, home entertainment, social networking, surveillance, body cameras, cloud gaming, and autonomous driving. As these applications continue to grow more sophisticated and heterogeneous, a single representation of video data can no longer satisfy all receivers. Instead, the initial encoding must be adapted to each receiver's unique needs. Existing adaptation strategies are fundamentally flawed, however, because they discard the video's initial representation and force the content to be re-encoded from scratch. This process is computationally expensive, does not scale well with the number of videos produced, and throws away important information embedded in the initial encoding. Therefore, a compelling need exists for the development of new strategies that can adapt video content without fully re-encoding it. To better support the unique needs of smart receivers, diverse displays, and advanced applications, general-use video systems should produce and offer receivers a more flexible compressed representation that supports top-down adaptation strategies from an original, compressed-domain ground truth. This dissertation proposes an alternate model for video adaptation that addresses these challenges. The key idea is to treat the initial compressed representation of a video as the ground truth, and allow receivers to drive adaptation by dynamically selecting which subsets of the captured data to receive. In support of this model, three strategies for top-down, receiver-driven adaptation are proposed. First, a novel, content-agnostic entropy coding technique is implemented in which symbols are selectively dropped from an input abstract symbol stream based on their estimated probability distributions to hit a target bit rate. Receivers are able to guide the symbol dropping process by supplying the encoder with an appropriate rate controller algorithm that fits their application needs and available bandwidths. Next, a domain-specific adaptation strategy is implemented for H.265/HEVC coded video in which the prediction data from the original source is reused directly in the adapted stream, but the residual data is recomputed as directed by the receiver. By tracking the changes made to the residual, the encoder can compensate for decoder drift to achieve near-optimal rate-distortion performance. Finally, a fully receiver-driven strategy is proposed in which the syntax elements of a pre-coded video are cataloged and exposed directly to clients through an HTTP API. Instead of requesting the entire stream at once, clients identify the exact syntax elements they wish to receive using a carefully designed query language. Although an implementation of this concept is not provided, an initial analysis shows that such a system could save bandwidth and computation when used by certain targeted applications.Doctor of Philosoph

Error resilience and concealment techniques for high-efficiency video coding

This thesis investigates the problem of robust coding and error concealment in High Efficiency Video Coding (HEVC). After a review of the current state of the art, a simulation study about error robustness, revealed that the HEVC has weak protection against network losses with significant impact on video quality degradation. Based on this evidence, the first contribution of this work is a new method to reduce the temporal dependencies between motion vectors, by improving the decoded video quality without compromising the compression efficiency. The second contribution of this thesis is a two-stage approach for reducing the mismatch of temporal predictions in case of video streams received with errors or lost data. At the encoding stage, the reference pictures are dynamically distributed based on a constrained Lagrangian rate-distortion optimization to reduce the number of predictions from a single reference. At the streaming stage, a prioritization algorithm, based on spatial dependencies, selects a reduced set of motion vectors to be transmitted, as side information, to reduce mismatched motion predictions at the decoder. The problem of error concealment-aware video coding is also investigated to enhance the overall error robustness. A new approach based on scalable coding and optimally error concealment selection is proposed, where the optimal error concealment modes are found by simulating transmission losses, followed by a saliency-weighted optimisation. Moreover, recovery residual information is encoded using a rate-controlled enhancement layer. Both are transmitted to the decoder to be used in case of data loss. Finally, an adaptive error resilience scheme is proposed to dynamically predict the video stream that achieves the highest decoded quality for a particular loss case. A neural network selects among the various video streams, encoded with different levels of compression efficiency and error protection, based on information from the video signal, the coded stream and the transmission network. Overall, the new robust video coding methods investigated in this thesis yield consistent quality gains in comparison with other existing methods and also the ones implemented in the HEVC reference software. Furthermore, the trade-off between coding efficiency and error robustness is also better in the proposed methods