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

High-Level Synthesis Based VLSI Architectures for Video Coding

High Efficiency Video Coding (HEVC) is state-of-the-art video coding standard. Emerging applications like free-viewpoint video, 360degree video, augmented reality, 3D movies etc. require standardized extensions of HEVC. The standardized extensions of HEVC include HEVC Scalable Video Coding (SHVC), HEVC Multiview Video Coding (MV-HEVC), MV-HEVC+ Depth (3D-HEVC) and HEVC Screen Content Coding. 3D-HEVC is used for applications like view synthesis generation, free-viewpoint video. Coding and transmission of depth maps in 3D-HEVC is used for the virtual view synthesis by the algorithms like Depth Image Based Rendering (DIBR). As first step, we performed the profiling of the 3D-HEVC standard. Computational intensive parts of the standard are identified for the efficient hardware implementation. One of the computational intensive part of the 3D-HEVC, HEVC and H.264/AVC is the Interpolation Filtering used for Fractional Motion Estimation (FME). The hardware implementation of the interpolation filtering is carried out using High-Level Synthesis (HLS) tools. Xilinx Vivado Design Suite is used for the HLS implementation of the interpolation filters of HEVC and H.264/AVC. The complexity of the digital systems is greatly increased. High-Level Synthesis is the methodology which offers great benefits such as late architectural or functional changes without time consuming in rewriting of RTL-code, algorithms can be tested and evaluated early in the design cycle and development of accurate models against which the final hardware can be verified

Methods for Light Field Display Profiling and Scalable Super-Multiview Video Coding

Light field 3D displays reproduce the light field of real or synthetic scenes, as observed by multiple viewers, without the necessity of wearing 3D glasses. Reproducing light fields is a technically challenging task in terms of optical setup, content creation, distributed rendering, among others; however, the impressive visual quality of hologramlike scenes, in full color, with real-time frame rates, and over a very wide field of view justifies the complexity involved. Seeing objects popping far out from the screen plane without glasses impresses even those viewers who have experienced other 3D displays before.Content for these displays can either be synthetic or real. The creation of synthetic (rendered) content is relatively well understood and used in practice. Depending on the technique used, rendering has its own complexities, quite similar to the complexity of rendering techniques for 2D displays. While rendering can be used in many use-cases, the holy grail of all 3D display technologies is to become the future 3DTVs, ending up in each living room and showing realistic 3D content without glasses. Capturing, transmitting, and rendering live scenes as light fields is extremely challenging, and it is necessary if we are about to experience light field 3D television showing real people and natural scenes, or realistic 3D video conferencing with real eye-contact.In order to provide the required realism, light field displays aim to provide a wide field of view (up to 180°), while reproducing up to ~80 MPixels nowadays. Building gigapixel light field displays is realistic in the next few years. Likewise, capturing live light fields involves using many synchronized cameras that cover the same display wide field of view and provide the same high pixel count. Therefore, light field capture and content creation has to be well optimized with respect to the targeted display technologies. Two major challenges in this process are addressed in this dissertation.The first challenge is how to characterize the display in terms of its capabilities to create light fields, that is how to profile the display in question. In clearer terms this boils down to finding the equivalent spatial resolution, which is similar to the screen resolution of 2D displays, and angular resolution, which describes the smallest angle, the color of which the display can control individually. Light field is formalized as 4D approximation of the plenoptic function in terms of geometrical optics through spatiallylocalized and angularly-directed light rays in the so-called ray space. Plenoptic Sampling Theory provides the required conditions to sample and reconstruct light fields. Subsequently, light field displays can be characterized in the Fourier domain by the effective display bandwidth they support. In the thesis, a methodology for displayspecific light field analysis is proposed. It regards the display as a signal processing channel and analyses it as such in spectral domain. As a result, one is able to derive the display throughput (i.e. the display bandwidth) and, subsequently, the optimal camera configuration to efficiently capture and filter light fields before displaying them.While the geometrical topology of optical light sources in projection-based light field displays can be used to theoretically derive display bandwidth, and its spatial and angular resolution, in many cases this topology is not available to the user. Furthermore, there are many implementation details which cause the display to deviate from its theoretical model. In such cases, profiling light field displays in terms of spatial and angular resolution has to be done by measurements. Measurement methods that involve the display showing specific test patterns, which are then captured by a single static or moving camera, are proposed in the thesis. Determining the effective spatial and angular resolution of a light field display is then based on an automated analysis of the captured images, as they are reproduced by the display, in the frequency domain. The analysis reveals the empirical limits of the display in terms of pass-band both in the spatial and angular dimension. Furthermore, the spatial resolution measurements are validated by subjective tests confirming that the results are in line with the smallest features human observers can perceive on the same display. The resolution values obtained can be used to design the optimal capture setup for the display in question.The second challenge is related with the massive number of views and pixels captured that have to be transmitted to the display. It clearly requires effective and efficient compression techniques to fit in the bandwidth available, as an uncompressed representation of such a super-multiview video could easily consume ~20 gigabits per second with today’s displays. Due to the high number of light rays to be captured, transmitted and rendered, distributed systems are necessary for both capturing and rendering the light field. During the first attempts to implement real-time light field capturing, transmission and rendering using a brute force approach, limitations became apparent. Still, due to the best possible image quality achievable with dense multi-camera light field capturing and light ray interpolation, this approach was chosen as the basis of further work, despite the massive amount of bandwidth needed. Decompression of all camera images in all rendering nodes, however, is prohibitively time consuming and is not scalable. After analyzing the light field interpolation process and the data-access patterns typical in a distributed light field rendering system, an approach to reduce the amount of data required in the rendering nodes has been proposed. This approach, on the other hand, requires rectangular parts (typically vertical bars in case of a Horizontal Parallax Only light field display) of the captured images to be available in the rendering nodes, which might be exploited to reduce the time spent with decompression of video streams. However, partial decoding is not readily supported by common image / video codecs. In the thesis, approaches aimed at achieving partial decoding are proposed for H.264, HEVC, JPEG and JPEG2000 and the results are compared.The results of the thesis on display profiling facilitate the design of optimal camera setups for capturing scenes to be reproduced on 3D light field displays. The developed super-multiview content encoding also facilitates light field rendering in real-time. This makes live light field transmission and real-time teleconferencing possible in a scalable way, using any number of cameras, and at the spatial and angular resolution the display actually needs for achieving a compelling visual experience

Novel entropy coding and its application of the compression of 3D image and video signals

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe broadcast industry is moving future Digital Television towards Super high resolution TV (4k or 8k) and/or 3D TV. This ultimately will increase the demand on data rate and subsequently the demand for highly efficient codecs. One of the techniques that researchers found it one of the promising technologies in the industry in the next few years is 3D Integral Image and Video due to its simplicity and mimics the reality, independently on viewer aid, one of the challenges of the 3D Integral technology is to improve the compression algorithms to adequate the high resolution and exploit the advantages of the characteristics of this technology. The research scope of this thesis includes designing a novel coding for the 3D Integral image and video compression. Firstly to address the compression of 3D Integral imaging the research proposes novel entropy coding which will be implemented first on 2D traditional images content in order to compare it with the other traditional common standards then will be applied on 3D Integra image and video. This approach seeks to achieve high performance represented by high image quality and low bit rate in association with low computational complexity. Secondly, new algorithm will be proposed in an attempt to improve and develop the transform techniques performance, initially by using a new adaptive 3D-DCT algorithm then by proposing a new hybrid 3D DWT-DCT algorithm via exploiting the advantages of each technique and get rid of the artifact that each technique of them suffers from. Finally, the proposed entropy coding will be further implemented to the 3D integral video in association with another proposed algorithm that based on calculating the motion vector on the average viewpoint for each frame. This approach seeks to minimize the complexity and reduce the speed without affecting the Human Visual System (HVS) performance. Number of block matching techniques will be used to investigate the best block matching technique that is adequate for the new proposed 3D integral video algorithm

Electrical and Computer Engineering Annual Report 2016

Faculty Directory Faculty Highlights Faculty Fellow Program Multidisciplinary Research Fills Critical Needs Better, Faster Technology Metamaterials: Searching for the Perfect Lens The Nontraditional Power of Demand Dispatch Space, Solar Power\u27s Next Frontier Kit Cischke, Award-Winning Senior Lecturer Faculty Publications ECE Academy Class of 2016 Staff Profile: Michele Kamppinen For the Love of Teaching: Jenn Winikus Graduate Student Highlights Undergraduate Student Highlights External Advisory Committee Contracts and Grants Department Statistics AAES National Engineering Awardhttps://digitalcommons.mtu.edu/ece-annualreports/1002/thumbnail.jp

Network streaming and compression for mixed reality tele-immersion

Bulterman, D.C.A. [Promotor]Cesar, P.S. [Copromotor

Viewpoint-Free Photography for Virtual Reality

Viewpoint-free photography, i.e., interactively controlling the viewpoint of a photograph after capture, is a standing challenge. In this thesis, we investigate algorithms to enable viewpoint-free photography for virtual reality (VR) from casual capture, i.e., from footage easily captured with consumer cameras. We build on an extensive body of work in image-based rendering (IBR). Given images of an object or scene, IBR methods aim to predict the appearance of an image taken from a novel perspective. Most IBR methods focus on full or near-interpolation, where the output viewpoints either lie directly between captured images, or nearby. These methods are not suitable for VR, where the user has significant range of motion and can look in all directions. Thus, it is essential to create viewpoint-free photos with a wide field-of-view and sufficient positional freedom to cover the range of motion a user might experience in VR. We focus on two VR experiences: 1) Seated VR experiences, where the user can lean in different directions. This simplifies the problem, as the scene is only observed from a small range of viewpoints. Thus, we focus on easy capture, showing how to turn panorama-style capture into 3D photos, a simple representation for viewpoint-free photos, and also how to speed up processing so users can see the final result on-site. 2) Room-scale VR experiences, where the user can explore vastly different perspectives. This is challenging: More input footage is needed, maintaining real-time display rates becomes difficult, view-dependent appearance and object backsides need to be modelled, all while preventing noticeable mistakes. We address these challenges by: (1) creating refined geometry for each input photograph, (2) using a fast tiled rendering algorithm to achieve real-time display rates, and (3) using a convolutional neural network to hide visual mistakes during compositing. Overall, we provide evidence that viewpoint-free photography is feasible from casual capture. We thoroughly compare with the state-of-the-art, showing that our methods achieve both a numerical improvement and a clear increase in visual quality for both seated and room-scale VR experiences