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

New approaches and a subjective database for video quality assessment

Video quality assessment plays an important role in multimedia systems that process digital images/videos such as video codec, video streaming server. The use of video quality assessment algorithm helps optimize system parameters, increase quality of service, and satisfy customers' demands. Traditional method that recruits human subjects to judge video quality often comes with the expense of time, money, and effort while objective method, which uses computer and built-in algorithms to judge video quality, offers a more affordable way. This dissertation report provides an efficient approach to develop objective video quality assessment algorithm.Algorithms in video quality assessment aim to predict quality of videos in a manner that agrees with subjective ratings of quality judged by human subjects. From that, two important factors are required for the research of video quality assessment. The first factor is an algorithm that is able to predict video quality. Our approach to develop such an algorithm bases on the analyses of spatial and spatiotemporal slices in two separate stages. The first stage estimates perceived quality degradation due to spatial distortion; this stage operates by adaptively applying our previous image quality assessment algorithm on a frame basis with an extension to account for temporal masking. The second stage estimates perceived quality degradation due to joint spatial and temporal distortion; this stage operates by measuring the dissimilarity between the two-dimensional spatiotemporal slices created by taking time-based slices of the original and distorted videos. The combination of these two estimates serves as an overall estimate of perceived quality degradation.The second important factor in the research of video quality assessment is a video-quality database with collected subjective ratings used to validate the algorithms' performance. We create our own video-quality database that consists of more videos ($216$ videos) and more distortion types (six) comparing to the currently available video-quality databases. The experiment to collect subjective ratings of quality is conducted by 40 different subjects following the SAMVIQ methodology.Acknowledge that in many applications, the original video is not available; we develop another video quality assessment algorithm that can predict quality of a processed video without information of the original video. This algorithm, specifically designed for videos compressed by Motion JPEG2000 compression standard, consists of two analyses of quality degradation in the edge/near-edge regions and the non-edge regions of the videos. The algorithm shows promise in the first step of developing a general no-reference algorithm for video quality assessment

Named Data Networking in Vehicular Ad hoc Networks: State-of-the-Art and Challenges

International audienceInformation-Centric Networking (ICN) has been proposed as one of the future Internet architectures. It is poised to address the challenges faced by today's Internet that include, but not limited to, scalability, addressing, security, and privacy. Furthermore, it also aims at meeting the requirements for new emerging Internet applications. To realize ICN, Named Data Networking (NDN) is one of the recent implementations of ICN that provides a suitable communication approach due to its clean slate design and simple communication model. There are a plethora of applications realized through ICN in different domains where data is the focal point of communication. One such domain is Intelligent Transportation System (ITS) realized through Vehicular Ad hoc NETwork (VANET) where vehicles exchange information and content with each other and with the infrastructure. To date, excellent research results have been yielded in the VANET domain aiming at safe, reliable, and infotainment-rich driving experience. However, due to the dynamic topologies, host-centric model, and ephemeral nature of vehicular communication, various challenges are faced by VANET that hinder the realization of successful vehicular networks and adversely affect the data dissemination, content delivery, and user experiences. To fill these gaps, NDN has been extensively used as underlying communication paradigm for VANET. Inspired by the extensive research results in NDN-based VANET, in this paper, we provide a detailed and systematic review of NDN-driven VANET. More precisely, we investigate the role of NDN in VANET and discuss the feasibility of NDN architecture in VANET environment. Subsequently, we cover in detail, NDN-based naming, routing and forwarding, caching, mobility, and security mechanism for VANET. Furthermore, we discuss the existing standards, solutions, and simulation tools used in NDN-based VANET. Finally, we also identify open challenges and issues faced by NDN-driven VANET and highlight future research directions that should be addressed by the research community