Can you tell a face from a HEVC bitstream?

Image and video analytics are being increasingly used on a massive scale. Not only is the amount of data growing, but the complexity of the data processing pipelines is also increasing, thereby exacerbating the problem. It is becoming increasingly important to save computational resources wherever possible. We focus on one of the poster problems of visual analytics -- face detection -- and approach the issue of reducing the computation by asking: Is it possible to detect a face without full image reconstruction from the High Efficiency Video Coding (HEVC) bitstream? We demonstrate that this is indeed possible, with accuracy comparable to conventional face detection, by training a Convolutional Neural Network on the output of the HEVC entropy decoder

Streaming and User Behaviour in Omnidirectional Videos

Omnidirectional videos (ODVs) have gone beyond the passive paradigm of traditional video, offering higher degrees of immersion and interaction. The revolutionary novelty of this technology is the possibility for users to interact with the surrounding environment, and to feel a sense of engagement and presence in a virtual space. Users are clearly the main driving force of immersive applications and consequentially the services need to be properly tailored to them. In this context, this chapter highlights the importance of the new role of users in ODV streaming applications, and thus the need for understanding their behaviour while navigating within ODVs. A comprehensive overview of the research efforts aimed at advancing ODV streaming systems is also presented. In particular, the state-of-the-art solutions under examination in this chapter are distinguished in terms of system-centric and user-centric streaming approaches: the former approach comes from a quite straightforward extension of well-established solutions for the 2D video pipeline while the latter one takes the benefit of understanding users’ behaviour and enable more personalised ODV streaming

SLEPX: An Efficient Lightweight Cipher for Visual Protection of Scalable HEVC Extension

This paper proposes a lightweight cipher scheme aimed at the scalable extension of the High Efficiency Video Coding (HEVC) codec, referred to as the Scalable HEVC (SHVC) standard. This stream cipher, Symmetric Cipher for Lightweight Encryption based on Permutation and EXlusive OR (SLEPX), applies Selective Encryption (SE) over suitable coding syntax elements in the SHVC layers. This is achieved minimal computational complexity and delay. The algorithm also conserves most SHVC functionalities, i.e. preservation of bit-length, decoder format-compliance, and error resilience. For comparative analysis, results were taken and compared with other state-of-art ciphers i.e. Exclusive-OR (XOR) and the Advanced Encryption Standard (AES). The performance of SLEPX is also compared with existing video SE solutions to confirm the efficiency of the adopted scheme. The experimental results demonstrate that SLEPX is as secure as AES in terms of visual protection, while computationally efficient comparable with a basic XOR cipher. Visual quality assessment, security analysis and extensive cryptanalysis (based on numerical values of selected binstrings) also showed the effectiveness of SLEPX’s visual protection scheme for SHVC compared to previously-employed cryptographic technique

Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise

End-to-end 3D video communication over heterogeneous networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Three-dimensional technology, more commonly referred to as 3D technology, has revolutionised many fields including entertainment, medicine, and communications to name a few. In addition to 3D films, games, and sports channels, 3D perception has made tele-medicine a reality. By the year 2015, 30% of the all HD panels at home will be 3D enabled, predicted by consumer electronics manufacturers. Stereoscopic cameras, a comparatively mature technology compared to other 3D systems, are now being used by ordinary citizens to produce 3D content and share at a click of a button just like they do with the 2D counterparts via sites like YouTube. But technical challenges still exist, including with autostereoscopic multiview displays. 3D content requires many complex considerations--including how to represent it, and deciphering what is the best compression format--when considering transmission or storage, because of its increased amount of data. Any decision must be taken in the light of the available bandwidth or storage capacity, quality and user expectations. Free viewpoint navigation also remains partly unsolved. The most pressing issue getting in the way of widespread uptake of consumer 3D systems is the ability to deliver 3D content to heterogeneous consumer displays over the heterogeneous networks. Optimising 3D video communication solutions must consider the entire pipeline, starting with optimisation at the video source to the end display and transmission optimisation. Multi-view offers the most compelling solution for 3D videos with motion parallax and freedom from wearing headgear for 3D video perception. Optimising multi-view video for delivery and display could increase the demand for true 3D in the consumer market. This thesis focuses on an end-to-end quality optimisation in 3D video communication/transmission, offering solutions for optimisation at the compression, transmission, and decoder levels.Brunel University - Isambard Research Scholarshi

5G-QoE:QoE Modelling for Ultra-HD Video Streaming in 5G Networks

Traffic on future fifth-generation (5G) mobile networks is predicted to be dominated by challenging video applications such as mobile broadcasting, remote surgery and augmented reality, demanding real-time, and ultra-high quality delivery. Two of the main expectations of 5G networks are that they will be able to handle ultra-high-definition (UHD) video streaming and that they will deliver services that meet the requirements of the end user's perceived quality by adopting quality of experience (QoE) aware network management approaches. This paper proposes a 5G-QoE framework to address the QoE modeling for UHD video flows in 5G networks. Particularly, it focuses on providing a QoE prediction model that is both sufficiently accurate and of low enough complexity to be employed as a continuous real-time indicator of the 'health' of video application flows at the scale required in future 5G networks. The model has been developed and implemented as part of the EU 5G PPP SELFNET autonomic management framework, where it provides a primary indicator of the likely perceptual quality of UHD video application flows traversing a realistic multi-tenanted 5G mobile edge network testbed. The proposed 5G-QoE framework has been implemented in the 5G testbed, and the high accuracy of QoE prediction has been validated through comparing the predicted QoE values with not only subjective testing results but also empirical measurements in the testbed. As such, 5G-QoE would enable a holistic video flow self-optimisation system employing the cutting-edge Scalable H.265 video encoding to transmit UHD video applications in a QoE-aware manner