H264 data partitioned video streaming

Motivated by the increasing demand and challenges of video streaming in this thesis, we investigate methods by which the quality of the video can be improved. We utilise overlay networks that have been created by implemented relay nodes to produce path diversity, and show through analytical and simulation models for which environments path diversity can improve the packet loss probability. We take the simulation and analytical models further by implementing a real overlay network on top of Planetlab, and show that when the network conditions remain constant the video quality received by the client can be improved. In addition, we show that in the environments where path diversity improves the video quality forward error correction can be used to further enhance the quality. We then investigate the effect of IEEE 802.11e Wireless LAN standard with quality of service enabled on the video quality received by a wireless client. We find that assigning all the video to a single class outperforms a cross class assignment scheme proposed by other researchers. The issue of virtual contention at the access point is also examined. We increase the intelligence of our relay nodes and enable them to cache video, in order to maximise the usefulness of these caches. For this purpose, we introduce a measure, called the PSNR profit, and present an optimal caching method for achieving the maximum PSNR profit at the relay nodes where partitioned video contents are stored and provide an enhanced quality for the client. We also show that the optimised cache the degradation in the video quality received by the client becomes more graceful than the non-optimised system when the network experiences packet loss or is congested.

A comparison of denoising methods for X-ray fluoroscopic images

Fluoroscopic images exhibit severe signal-dependent quantum noise, due to the reduced X-ray dose involved in image formation, that is generally modelled as Poisson-distributed. However, image gray-level transformations, commonly applied by fluoroscopic device to enhance contrast, modify the noise statistics and the relationship between image noise variance and expected pixel intensity. Image denoising is essential to improve quality of fluoroscopic images and their clinical information content. Simple average filters are commonly employed in real-time processing, but they tend to blur edges and details. An extensive comparison of advanced denoising algorithms specifically designed for both signal-dependent noise (AAS, BM3Dc, HHM, TLS) and independent additive noise (AV, BM3D, K-SVD) was presented. Simulated test images degraded by various levels of Poisson quantum noise and real clinical fluoroscopic images were considered. Typical gray-level transformations (e.g. white compression) were also applied in order to evaluate their effect on the denoising algorithms. Performances of the algorithms were evaluated in terms of peak-signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), mean square error (MSE), structural similarity index (SSIM) and computational time. On average, the filters designed for signal-dependent noise provided better image restorations than those assuming additive white Gaussian noise (AWGN). Collaborative denoising strategy was found to be the most effective in denoising of both simulated and real data, also in the presence of image gray-level transformations. White compression, by inherently reducing the greater noise variance of brighter pixels, appeared to support denoising algorithms in performing more effectively. © 2012 Elsevier Ltd. All rights reserved.

A quality driven adaptation scheme for DASH streaming

In this work we present a quality driven approach to DASH (Dynamic Adaptive Streaming over HTTP) for segment selection in varying network conditions. Current adaption algorithms focus largely on regulating data rates using network layer parameters by selecting the level of quality on offer that can eliminate buffer underrun without considering picture fidelity. In reality, viewers may accept a level of buffer underrun in order to achieve an improved level of picture fidelity. In this case, the conventional DASH algorithms can cause extreme degradation of the picture fidelity when attempting to eliminate buffer underrun with scarce bandwidth availability. Our work is concerned with a quality-aware rate adaption scheme that maximizes the client's quality of experience in terms of both continuity and fidelity (picture quality). Results show that the scheme proposed can maintain a high level of quality for streaming services, especially at low packet loss rates. It is also shown that by eliminating buffer underrun completely, the PSNR that reflects the picture quality of the video is greatly reduced. Our scheme offers the offset between continuity-based quality and resolution-based quality, which can be used to set threshold values for the level of quality desired by clients with different quality requirements. © 2013 IEEE.

Quality assessment of video transmission in heterogeneous networks as applied to real-time telecare

This work looks into video quality assessment applied to the field of telecare and proposes an alternative metric to the more traditionally used PSNR based on the requirements of such an application. We show that the Pause Intensity metric introduced in [1] is also relevant and applicable to heterogeneous networks with a wireless last hop connected to a wired TCP backbone. We demonstrate through our emulation testbed that the impairments experienced in such a network architecture are dominated by continuity based impairments rather than artifacts, such as motion drift or blockiness. We also look into the implication of using Pause Intensity as a metric in terms of the overall video latency, which is potentially problematic should the video be sent and acted upon in real-time. We conclude that Pause Intensity may be used alongside the video characteristics which have been suggested as a measure of the overall video quality. © 2012 IEEE.

Pause intensity:a no-reference quality assessment metric for video streaming in TCP networks

In this paper a full analytic model for pause intensity (PI), a no-reference metric for video quality assessment, is presented. The model is built upon the video play out buffer behavior at the client side and also encompasses the characteristics of a TCP network. Video streaming via TCP produces impairments in play continuity, which are not typically reflected in current objective metrics such as PSNR and SSIM. Recently the buffer under run frequency/probability has been used to characterize the buffer behavior and as a measurement for performance optimization. But we show, using subjective testing, that under run frequency cannot reflect the viewers' quality of experience for TCP based streaming. We also demonstrate that PI is a comprehensive metric made up of a combination of phenomena observed in the play out buffer. The analytical model in this work is verified with simulations carried out on ns-2, showing that the two results are closely matched. The effectiveness of the PI metric has also been proved by subjective testing on a range of video clips, where PI values exhibit a good correlation with the viewers' opinion scores. © 2012 IEEE.

Exploring the effect of buffer behaviour on perceived video quality

In this work we deal with video streams over TCP networks and propose an alternative measurement to the widely used and accepted peak signal to noise ratio (PSNR) due to the limitations of this metric in the presence of temporal errors. A test-bed was created to simulate buffer under-run in scalable video streams and the pauses produced as a result of the buffer under-run were inserted into the video before being employed as the subject of subjective testing. The pause intensity metric proposed in [1] was compared with the subjective results and it was shown that in spite of reductions in frame rate and resolution, a correlation with pause intensity still exists. Due to these conclusions, the metric may be employed in layer selection in scalable video streams. © 2011 IEEE.