3D multiple description coding for error resilience over wireless networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Mobile communications has gained a growing interest from both customers and service providers alike in the last 1-2 decades. Visual information is used in many application domains such as remote health care, video –on demand, broadcasting, video surveillance etc. In order to enhance the visual effects of digital video content, the depth perception needs to be provided with the actual visual content. 3D video has earned a significant interest from the research community in recent years, due to the tremendous impact it leaves on viewers and its enhancement of the user’s quality of experience (QoE). In the near future, 3D video is likely to be used in most video applications, as it offers a greater sense of immersion and perceptual experience. When 3D video is compressed and transmitted over error prone channels, the associated packet loss leads to visual quality degradation. When a picture is lost or corrupted so severely that the concealment result is not acceptable, the receiver typically pauses video playback and waits for the next INTRA picture to resume decoding. Error propagation caused by employing predictive coding may degrade the video quality severely. There are several ways used to mitigate the effects of such transmission errors. One widely used technique in International Video Coding Standards is error resilience. The motivation behind this research work is that, existing schemes for 2D colour video compression such as MPEG, JPEG and H.263 cannot be applied to 3D video content. 3D video signals contain depth as well as colour information and are bandwidth demanding, as they require the transmission of multiple high-bandwidth 3D video streams. On the other hand, the capacity of wireless channels is limited and wireless links are prone to various types of errors caused by noise, interference, fading, handoff, error burst and network congestion. Given the maximum bit rate budget to represent the 3D scene, optimal bit-rate allocation between texture and depth information rendering distortion/losses should be minimised. To mitigate the effect of these errors on the perceptual 3D video quality, error resilience video coding needs to be investigated further to offer better quality of experience (QoE) to end users. This research work aims at enhancing the error resilience capability of compressed 3D video, when transmitted over mobile channels, using Multiple Description Coding (MDC) in order to improve better user’s quality of experience (QoE). Furthermore, this thesis examines the sensitivity of the human visual system (HVS) when employed to view 3D video scenes. The approach used in this study is to use subjective testing in order to rate people’s perception of 3D video under error free and error prone conditions through the use of a carefully designed bespoke questionnaire.Petroleum Technology Development Fund (PTDF

Image data compression based on a multiresolution signal model

Image data compression is an important topic within the general field of image processing. It has practical applications varying from medical imagery to video telephones, and provides significant implications for image modelling theory. In this thesis a new class of linear signal models, linear interpolative multiresolution models, is presented and applied to the data compression of a range of natural images. The key property of these models is that whilst they are non- causal in the two spatial dimensions they are causal in a third dimension, the scale dimension. This leads to computationally efficient predictors which form the basis of the data compression algorithms. Models of varying complexity are presented, ranging from a simple stationary form to one which models visually important features such as lines and edges in terms of scale and orientation. In addition to theoretical results such as related rate distortion functions, the results of applying the compression algorithms to a variety of images are presented. These results compare favourably, particularly at high compression ratios, with many of the techniques described in the literature, both in terms of mean squared quantisation noise and more meaningfully, in terms of perceived visual quality. In particular the use of local orientation over various scales within the consistent spatial interpolative framework of the model significantly reduces perceptually important distortions such as the blocking artefacts often seen with high compression coders. A new algorithm for fast computation of the orientation information required by the adaptive coder is presented which results in an overall computational complexity for the coder which is broadly comparable to that of the simpler non-adaptive coder. This thesis is concluded with a discussion of some of the important issues raised by the work