Video Prioritization for Unequal Error Protection

We analyze the effect of packet losses in video sequences and propose a lightweight Unequal Error Protection strategy which, by choosing which packet is discarded, reduces strongly the Mean Square Error of the received sequenc

Video Packet Priority Assignment Based On Spatio-Temporal Perceptual Importance

A novel perceptually motivated two-stage algorithm for assigning priority to video packet data to be transmitted over the internet is proposed. Priority assignment is based on temporal and spatial features that are derived from low-level vision concepts. The motivation for a two-stage design is to be able to handle different application settings. The first stage of the algorithm is computationally very efficient and can be directly used in low-delay applications with limited computational resources. The two-stage method performs exceedingly well across a variety of content and can be used in less restrictive operating settings. The efficacy of the proposed algorithm (both stages) is demonstrated using an intelligent packet drop application where it is compared with cumulative mean squared error (cMSE) based priority assignment and random packet dropping. The proposed prioritization algorithm allows for packet drops that result in significantly lower perceptual annoyance at the receiver relative to the other methods considered

Error concealment-aware encoding for robust video transmission

In this paper an error concealment-aware encoding scheme is proposed to improve the quality of decoded video in broadcast environments prone to transmission errors and data loss. The proposed scheme is based on a scalable coding approach where the best error concealment (EC) methods to be used at the decoder are optimally determined at the encoder and signalled to the decoder through SEI messages. Such optimal EC modes are found by simulating transmission losses followed by a lagrangian optimisation of the signalling rate - EC distortion cost. A generalised saliency-weighted distortion is used and the residue between coded frames and their EC substitutes is encoded using a rate-controlled enhancement layer. When data loss occurs the decoder uses the signalling information is used at the decoder, in case of data loss, to improve the reconstruction quality. The simulation results show that the proposed method achieves consistent quality gains in comparison with other reference methods and previous works. Using only the EC mode signalling, i.e., without any residue transmitted in the enhancement layer, an average PSNR gain up to 2.95 dB is achieved, while using the full EC-aware scheme, i.e., including residue encoded in the enhancement layer, the proposed scheme outperforms other comparable methods, with PSNR gain up to 3.79 dB

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

Visual Saliency in Video Compression and Transmission

This dissertation explores the concept of visual saliency—a measure of propensity for drawing visual attention—and presents various novel methods for utilization of visual saliencyin video compression and transmission. Specifically, a computationally-efficient method for visual saliency estimation in digital images and videos is developed, which approximates one of the most well-known visual saliency models. In the context of video compression, a saliency-aware video coding method is proposed within a region-of-interest (ROI) video coding paradigm. The proposed video coding method attempts to reduce attention-grabbing coding artifacts and keep viewers’ attention in areas where the quality is highest. The method allows visual saliency to increase in high quality parts of the frame, and allows saliency to reduce in non-ROI parts. Using this approach, the proposed method is able to achieve the same subjective quality as competing state-of-the-art methods at a lower bit rate. In the context of video transmission, a novel saliency-cognizant error concealment method is presented for ROI-based video streaming in which regions with higher visual saliency are protected more heavily than low saliency regions. In the proposed error concealment method, a low-saliency prior is added to the error concealment process as a regularization term, which serves two purposes. First, it provides additional side information for the decoder to identify the correct replacement blocks for concealment. Second, in the event that a perfectly matched block cannot be unambiguously identified, the low-saliency prior reduces viewers’ visual attention on the loss-stricken regions, resulting in higher overall subjective quality. During the course of this research, an eye-tracking dataset for several standard video sequences was created and made publicly available. This dataset can be utilized to test saliency models for video and evaluate various perceptually-motivated algorithms for video processing and video quality assessment

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Network Science for IoT

The research work presented in this thesis is based on the concept and defintion of network that can spread in several and different real world contexts. Indeed, we can refer to a network in a telecommunications sense considering a collection of transmitters, receivers, and communication channels that send or are used to send information to one another. However, as a matter of fact, in nature there are other several examples of networks: the human brain is one of them. The relationship between the actors in Hollywood can be studied in terms of network as well, a generic social community can be compared to a network, eco-systems are networks of species. The recent Network Science aims at studying all these systems using a set of common mathematical methods. In the following of the thesis, we will focus on some of well known telecommunications networks issues using standard telecommunications procedures to address them, with relevant reference to video flow transmissions and management of electric vehicles networks. At the same time, different models aiming at reach the same goals in contexts that may differ from a telecommunications setup can be used. In more details, we will evaluate queueing systems, jamming problems, groups recognition in networks, and mobile computing using game theoretic approaches. It is worth noting that this aspect can be also seen in a reverse order. Indeed, we will discuss how standard telecommunications analysis can be used to investigate on problems not directly related to a telecommunications background. In particular, one of our future purposes is to investigate on the brain connectivity that is raising significant interest in the recent scientific society

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition