1 Abstract -The 3D (3Dimensional) video technologies are emerging to provide more immersive media content compared to conventional 2D (2Dimensional) video applications. More often 3D video quality is measured using rigorous and time-consuming subjective evaluation test campaigns. This is due to the fact that 3D video quality can be described as a combination of several perceptual attributes such as overall image quality, perceived depth, presence, naturalness and eye strain, etc. Hence this paper investigates the relationship between subjective quality measures and several objective quality measures like PSNR, SSIM, and VQM for 3D video content. The 3D video content captured using both stereo camera pair (two cameras for left and right views) and colour-and-depth special range cameras are considered in this study. The results show that, VQM quality measures of individual left and right views (rendered left and right views for colour-and-depth sequences) can be effectively used in predicting the overall image quality and statistical measures like PSNR and SSIM of left and right views illustrate good correlations with depth perception of 3D video.

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Interest in 3D video has surged in recent years. However, efforts to improve the quality of compression and transmission schemes are severely hampered by a lack of effective quality evaluation metrics. This is a particularly severe problem for researchers trying to improve the robustness of video transmission to packet loss. Subjective tests for evaluating error robustness present huge requirements in terms of time and resources. To solve this problem, this paper presents a quality metric for 3D video, and evaluates its effectiveness for the measurement of quality in the presence of packet loss. A key feature of the work is the use of depth planes to enable the metric to better model how the Human Visual System (HVS) perceives 3D video. The quality metric results are compared with subjective test results. The correlation between the proposed quality metric and the subjective test results is shown to be stronger than standard quality metrics, such as Video Quality Metric (VQM)

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SCADA (Supervisory Control And Data Acquisition) systems have always been susceptible to cyber-attacks. Different types of cyber-attacks could occur depending on the architecture and configurations used in the SCADA system. To protect cyber infrastructure from above attacks a growing collaborative effort between cyber security professionals and researchers from private and academia has involved in designing variety of intelligent intrusion detection systems. This paper introduces a new European Framework-7 project CockpitCI and roles of intelligent machine learning methods to prevent SCADA systems from cyber-attacks.