Deep Multi-modality Soft-decoding of Very Low Bit-rate Face Videos

We propose a novel deep multi-modality neural network for restoring very low bit rate videos of talking heads. Such video contents are very common in social media, teleconferencing, distance education, tele-medicine, etc., and often need to be transmitted with limited bandwidth. The proposed CNN method exploits the correlations among three modalities, video, audio and emotion state of the speaker, to remove the video compression artifacts caused by spatial down sampling and quantization. The deep learning approach turns out to be ideally suited for the video restoration task, as the complex non-linear cross-modality correlations are very difficult to model analytically and explicitly. The new method is a video post processor that can significantly boost the perceptual quality of aggressively compressed talking head videos, while being fully compatible with all existing video compression standards.Comment: Accepted by Proceedings of the 28th ACM International Conference on Multimedia(ACM MM),202

High Performance Multiview Video Coding

Following the standardization of the latest video coding standard High Efficiency Video Coding in 2013, in 2014, multiview extension of HEVC (MV-HEVC) was published and brought significantly better compression performance of around 50% for multiview and 3D videos compared to multiple independent single-view HEVC coding. However, the extremely high computational complexity of MV-HEVC demands significant optimization of the encoder. To tackle this problem, this work investigates the possibilities of using modern parallel computing platforms and tools such as single-instruction-multiple-data (SIMD) instructions, multi-core CPU, massively parallel GPU, and computer cluster to significantly enhance the MVC encoder performance. The aforementioned computing tools have very different computing characteristics and misuse of the tools may result in poor performance improvement and sometimes even reduction. To achieve the best possible encoding performance from modern computing tools, different levels of parallelism inside a typical MVC encoder are identified and analyzed. Novel optimization techniques at various levels of abstraction are proposed, non-aggregation massively parallel motion estimation (ME) and disparity estimation (DE) in prediction unit (PU), fractional and bi-directional ME/DE acceleration through SIMD, quantization parameter (QP)-based early termination for coding tree unit (CTU), optimized resource-scheduled wave-front parallel processing for CTU, and workload balanced, cluster-based multiple-view parallel are proposed. The result shows proposed parallel optimization techniques, with insignificant loss to coding efficiency, significantly improves the execution time performance. This , in turn, proves modern parallel computing platforms, with appropriate platform-specific algorithm design, are valuable tools for improving the performance of computationally intensive applications

Adaptive delivery of immersive 3D multi-view video over the Internet

The increase in Internet bandwidth and the developments in 3D video technology have paved the way for the delivery of 3D Multi-View Video (MVV) over the Internet. However, large amounts of data and dynamic network conditions result in frequent network congestion, which may prevent video packets from being delivered on time. As a consequence, the 3D video experience may well be degraded unless content-aware precautionary mechanisms and adaptation methods are deployed. In this work, a novel adaptive MVV streaming method is introduced which addresses the future generation 3D immersive MVV experiences with multi-view displays. When the user experiences network congestion, making it necessary to perform adaptation, the rate-distortion optimum set of views that are pre-determined by the server, are truncated from the delivered MVV streams. In order to maintain high Quality of Experience (QoE) service during the frequent network congestion, the proposed method involves the calculation of low-overhead additional metadata that is delivered to the client. The proposed adaptive 3D MVV streaming solution is tested using the MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH) standard. Both extensive objective and subjective evaluations are presented, showing that the proposed method provides significant quality enhancement under the adverse network conditions

Advances in Spatially Faithful (3D) Telepresence

Benefits of AR technologies have been well proven in collaborative industrial applications, for example in remote maintenance and consultancy. Benefits may also be high in telepresence applications, where virtual and mixed reality (nowadays often referred as extended reality, XR) technologies are used for sharing information or objects over network. Since the 90’s, technical enablers for advanced telepresence solutions have developed considerably. At the same time, the importance of remote technologies has grown immensely due to general disruption of work, demands for reducing travelling and CO2, and the need for preventing pandemics. An advanced 3D telepresence solution benefits from using XR technologies. Particularly interesting are solutions based on HMD or glasses type of near-eye-displays (NED). However, as AR/VR glasses supporting natural occlusions and accommodation are still missing from the market, a good alternative is to use screen displays in new ways, better supporting e.g. virtual meeting geometries and other important cues for 3D perception. In this article, researchers Seppo Valli, Mika Hakkarainen, and Pekka Siltanen from VTT Technical Research Centre of Finland describe the status, challenges, and opportunities in both glasses and screen based 3D telepresence. The writers also specify an affordable screen based solution with improved immersiveness, naturalness, and efficiency, enhanced by applying XR technologies

Error control strategies in H.265|HEVC video transmission

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWith the rapid development in video coding technologies in the last decade, high-resolution video delivery suffers from packet loss due to unreliable transmission channels (time-varying characteristics). The error Resilience approaches at channel coding level are less efficient to implement in real time video transmission as the encoded video samples are in variable code length. Therefore, error resilience in video coding standard plays a vital role to reduce the effect of error propagation and improve the perceived visual quality. The main work in this thesis is to develop an efficient error resilience mechanism for H.265|HEVC video coding standard to reduce the effects of error propagation in error-prone conditions. In this thesis, two error resilience algorithms are proposed. The first one is Adaptive Slice Encoding (ASE) error resilience algorithm. The concept of this algorithm is to extract and protect the most active slices in the coded bitstream based on the adaptive search window. This algorithm can be applied in low delay video transmission with and without using a feedback channel. It is also designed to be compatible with reference coding software manual (HM16) for H.265|HEVC coding standard. The second proposed algorithm is a joint encoder-decoder error resilience called Error resilience based on Supplemental Enhancement Information (ERSEI) algorithm. A feedback message status is used from the decoder to notify the encoder to start encoding clean random-access picture adaptively based on the decoded picture hash message status from the decoder. At the same time, the decoder will be notified to start the error concealment process whilst waiting to receive correct video data. A recovery point message from the decoder feedback channel is used to update the encoder with error messages. In this thesis, extensive experimental work, evaluation, and comparison with state-of-the-art related algorithms have been conducted to evaluate the proposed algorithms. Furthermore, the best trade-off between the coding efficiency of the proposed error resilience algorithms and error resilience performance has been considered at the design stage. The experimental work evaluation includes both encoding conditions, i.e. error-free and error-prone. The results achieved from the experiments show significant improvements, in (Y-PSNR) results and subjective quality of the decoded bitstream, using the proposed algorithm in error-prone conditions with a variety of packet loss rates. Moreover, experimental work is conducted to test the algorithms complexity in terms of required processing execution time at both encoding and decoding stages. Additionally, the video coding standard performance for both H.264|AVC and H.265|HEVC coding standards are evaluated in error-free and error-prone environments. For ASE algorithm and when compared with improved region of interest (IROI) and region of interest (ROI) algorithms, a significant improvement in visual quality was the most obvious finding from the obtained results with PLRs of 2-18 (%). For ERSEI algorithm and when compared with the default HM16 with pixel copy concealment and motion compensated error concealment (MCEC) techniques, the evaluation results indicate clear visual quality enhancement under different packet loss rates PLRs (1,2 6, 8) %.The Ministry of Higher Education and Scientific Research in Ira

Encoder-Driven Inpainting Strategy in Multiview Video Compression

In free viewpoint video systems, where a user has the freedom to select a virtual view from which an observation image of the 3D scene is rendered, the scene is commonly represented by texture and depth images from multiple nearby viewpoints. In such representation, there exists data redundancy across multiple dimensions: a single visible 3D voxel may be represented by pixels in multiple viewpoint images (inter-view redundancy), a pixel patch may recur in a distant spatial region of the same image due to self-similarity (inter-patch redundancy), and pixels in a local spatial region tend to be similar (inter-pixel redundancy). It isimportant to exploit these redundancies for effective multiview video compression. Existing schemes attempt to eliminate them via the traditional video coding paradigm of hybrid signal prediction/residual coding; typically, the encoder codes explicit information to guide the decoder to the location of the most similar block along with the signal differential. In this paper, we argue that, given the inherent redundancy in the representation, the decoder can often independently recover missing data via inpainting without explicit directions from encoder, resulting in lower coding overhead. Specifically, after pixels in a reference view are projected to a target view via depth image-based rendering (DIBR) at the decoder, the remaining holes in the target view are filled via an inpainting process in a block-by-block manner. First, blocks are ordered in terms of difficulty-to-inpaint by the decoder. Then, explicit instructions are only sent for the reconstruction of the most difficult blocks. In particular, the missing pixels are explicitly coded via a graph Fourier transform (GFT) or a sparsification procedure using DCT, which leads to low coding cost. For the blocks that are easy to inpaint, the decoder independently completes missing pixels via template-based inpainting. We implemented our encoder-driven inpainting strategy as an extension of High Efficiency Video Coding (HEVC). Experimental results show that our coding strategy can outperform comparable implementation of HEVC by up to 0.8dB in reconstructed image qualit