Transforming 3D Cinema Content for an Enhanced 3DTV Experience

3D cinema and 3DTV are at two different levels in the screen size spectrum. When the same stereoscopic-3D content is viewed on a cinema screen and 3DTV screen, it will produce a different 3D impression. As a result, it is difficult to fulfill the requirements of 3DTV with content captured for 3D cinema. Thus, it is important to properly address the issue of 3DTV content creation to avoid possible delays in the deployment of 3DTV. In this paper, we first explore the effects of using the same content for 3D cinema and 3DTV and then analyze the performance of several disparity based transformations for 3D cinema to 3DTV content conversion, by subjective testing. Effectiveness of the transformations is analyzed in terms of both depth quality and visual comfort of 3D experience. We show that by using a simple shift-based disparity transformation technique, it is possible to enhance the 3DTV experience from a common input signal which is originally captured for cinema viewing

3D Cinema to 3DTV Content Adaptation

3D cinema and 3DTV have grown in popularity in recent years. Filmmakers have a significant opportunity in front ofthem given the recent success of 3D films. In this paper we investigate whether this opportunity could be extended to the home in a meaningful way. "3D" perceived from viewing stereoscopic content depends on the viewing geometry. This implies that the stereoscopic-3D content should be captured for a specific viewing geometry in order to provide a satisfactory 3D experience. However, although it would be possible, it is clearly not viable, to produce and transmit multiple streams of the same content for different screen sizes. In this study to solve the above problem, we analyze theperformance of six different disparity-based transformation techniques, which could be used for cinema-to-3DTV content conversion. Subjective tests are performed to evaluate the effectiveness of the algorithms in terms of depth effect, visual comfort and overall 3D quality. The resultant 3DTV experience is also compared to that of cinema. We show that by applying the proper transformation technique on the content originally captured for cinema, it is possible to enhance the 3DTV experience. The selection of the appropriate transformation is highly dependent on the contentcharacteristic

A Taxonomy of Supervised Learning for IDSs in SCADA Environments

International audienceSupervisory Control and Data Acquisition (SCADA) systems play an important role in monitoring industrial processes such as electric power distribution, transport systems, water distribution, and wastewater collection systems. Such systems require a particular attention with regards to security aspects, as they deal with critical infrastructures that are crucial to organizations and countries. Protecting SCADA systems from intrusion is a very challenging task because they do not only inherit traditional IT security threats but they also include additional vulnerabilities related to field components (e.g., cyber-physical attacks). Many of the existing intrusion detection techniques rely on supervised learning that consists of algorithms that are first trained with reference inputs to learn specific information, and then tested on unseen inputs for classification purposes. This article surveys supervised learning from a specific security angle, namely SCADA-based intrusion detection. Based on a systematic review process, existing literature is categorized and evaluated according to SCADA-specific requirements. Additionally, this survey reports on well-known SCADA datasets and testbeds used with machine learning methods. Finally, we present key challenges and our recommendations for using specific supervised methods for SCADA systems