Streaming video over the Internet requires mechanisms that limit the streams' bandwidth consumption within its fair share. TCP streaming guarantees this and provides lossless streaming as a side-effect. Adaptation by packet drop does not occur in the network, and excessive startup latency and stalling must be prevented by adapting the bandwidth consumption of the video itself. However, when the adaptation is performed during an ongoing session, it may influence the perceived quality of the entire video and result in improved or reduced visual quality of experience. We have investigated visual artifacts that are caused by adaptive layer switching -we call them flicker effects -and present our results for handheld devices in this paper.We considered three types of flicker, namely noise, blur and motion flicker. The perceptual impact of flicker is explored through subjective assessments. We vary both the intensity of quality changes (amplitude) and the number of quality changes per second (frequency). Users' ability to detect and their acceptance of variations in the amplitudes and frequencies of the quality changes are explored across four content types. Our results indicate that multiple factors influence the acceptance of different quality variations. Amplitude plays the dominant role in delivering satisfactory video quality, while frequency can also be adjusted to relieve the annoyance of flicker artifacts.

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Smart cameras are extensively used for multi-view capture and 3D rendering applications. To achieve high quality, such applications are required to estimate accurate position and orientation of the cameras (called as camera calibration-pose estimation). Traditional techniques that use checkerboard or special markers, are impractical in larger spaces. Hence, feature-based calibration (auto-calibration), is necessary. Such calibration methods are carried out based on features extracted and matched between stereo pairs or multiple cameras.Well known feature extraction methods such as SIFT (Scale Invariant Feature Transform), SURF (Speeded-Up Robust Features) and ORB (Oriented FAST and Rotated BRIEF) have been used for auto-calibration. The accuracy of autocalibration is sensitive to the accuracy of features extracted and matched between a stereo pair or multiple cameras. In practical imaging systems, we encounter several issues such as blur, lens distortion and thermal noise that affect the accuracy of feature detectors.In our study, we investigate the behaviour of SIFT, SURF and ORB through simulations of practical issues and evaluate their performance targeting 3D reconstruction (based on epipolar geometry of a stereo pair). Our experiments are carried out on two real-world stereo image datasets of various resolutions. Our experimental results show significant performance differences between feature extractors' performance in terms of accuracy, execution time and robustness to blur, lens distortion and thermal noise of various levels. Eventually, our study identifies suitable operating ranges that helps other researchers and developers of practical imaging solutions.

Expressing and analysing data dependency in multimedia streams is promising, since content-aware policies at a transport level would benefit from such services. In this paper we present a format-independent dependency model aimed at specifying, validating and reasoning about structural dependency in multimedia streams. Based on this model, we developed a universal dependency description language and a dependency validation service to serve as an infrastructure for content-aware transport layers. Driven by application knowledge, this special form of a cross-layer design enables lower layers to reason about the impact of data loss and drops during transmission while being unaware of the real data format.We outline, how this infrastructure can be used to build content-aware error protection policies and explain how applications need to specify dependency and prepare media streams in order to gain benefits from those policies. While costs and benefits of a dependency model are only quantifiable in conjunction with special policies, we report on the general worst-case costs of our model here.

Scalable video is an attractive option for adapting the bandwidth consumption of streaming video to the available bandwidth. Fine-grained scalability can adapt most closely to the available bandwidth, but this comes at the cost of a higher overhead compared to more coarsegrained videos. In the context of VoD streaming, we have therefore explored whether a similar adaptation to the available bandwidth can be achieved by performing layer switching in coarse-grained scalable videos. In this approach, enhancement layers of a video stream are switched on and off to achieve any desired longer term bandwidth. We have performed three user studies, two using mobile devices and one using an HDTV display, to evaluate the idea. In several cases, the far-from-obvious conclusion is that layer switching is a viable way of achieving bit-rate savings and fine-grained bit-rate adaptation even for rather short times between layer switches, but it does, however, depend on scaling dimensions, content and display device.