The advent of multimedia computing has lead to an increased demand for digital images and videos (sequence of frames). Telemedicine is a major application of digital image processing and is increasingly deployed over different venues. However, there are still many challenges associated with this technology, especially on high quality real-time video compression. Wavelet transform has been becoming important in image compression applications because of its flexibility and efficiency in representing non-stationary signals. Generally, wavelet based compressions do not result in blocky effects and/or artifacts. However, existing wavelet-based compression systems are computationally intensive for real-time transmission of videos. In this thesis, we propose a novel region of interest (ROI) wavelet-based real-time video compression technique that is especially suited for telemedical applications. We shall demonstrate that with currently available and affordable hardware, the proposed ROI-based compression technique can simultaneously meet the stringent constraints imposed by the special requirements of telemedicine on processing time, bandwidth and high quality. The region of interest (e.g. the area of medical examination/surgery) in the decompressed images will have the highest possible image quality throughout the transmission at the expense of the unimportant outside region. Corresponding to the novel ROI-based wavelet transformation, suitable quantization and coding schemes are explored and incorporated into a complete video compression and transmission system. Factors that influence the ROI system performance with respect to computation time, compression ratio and video quality, are investigated with the aim of providing relevant suggestions on how to achieve desired performances. Software and user requirement factors include video frame size, the size of the ROI, the choice of wavelet filter, the scheme and depth of decomposition, and the quantization and coding schemes. Other performance influencing factors relate to the implementation platform, such as the local machine performance and network bandwidth. Comprehensive testing on the various software influencing factors and the CPU speed win be carried out to demonstrate the viability of the ROI system on currently available technology.

No description available.

No description available.

No description available.

The first part of this study is devoted to assessing the suitability of soft proofs as a surrogate for the final print in judging colour-reproduction quality. Two sets of psychophysical experiments were carried out. The first investigated whether the type of medium used to generate a stimulus influenced its appearance, while the second attempted to determine whether judgments made on the basis of hardcopy simulation are transferable to prints. The second part of this study is devoted to improve the accuracy of soft proofing by increasing the amount of information rendered on screen. We present a workflow that extends traditional colorimetry beyond simple colour appearance prediction to a higher dimensional representation, which includes gloss. The intention is to provide a full goniometric simulation of the spatial distribution of light reflected by a print based upon data gathered with simple and inexpensive instruments, glossmeters and spectrophotometers.

No description available.

No description available.

Global illumination models are used to simulate the complex lighting effects found in nature. This thesis investigates the wavelet radiance global illumination model and the extension of the wavelet radiance algorithm to support non-uniform rational B-spline (NURBS) surfaces. NURBS are a powerful mathematical form for representing freeform surfaces - NURBS are used in many rendering systems and therefore it is very useful to extend illumination models to support these surfaces.

The tradition of broadcast video is today complemented with user generated content, as portable devices support video coding. Similarly, computing is becoming ubiquitous, where Internet of Things (IoT) incorporate heterogeneous networks to communicate with personal and/or infrastructure devices. Irrespective, the emphasises is on bandwidth and processor efficiencies, meaning increasing the signalling options in video encoding. Consequently, assessment for pixel differences applies uniform cost to be processor efficient, in contrast the Human Visual System (HVS) has non-uniform sensitivity based upon lighting, edges and textures. Existing perceptual assessments, are natively incompatible and processor demanding, making perceptual video coding (PVC) unsuitable for these environments. This research allows existing perceptual assessment at the native level using low complexity techniques, before producing new pixel-base image quality assessments (IQAs). To manage these IQAs a framework was developed and implemented in the high efficiency video coding (HEVC) encoder. This resulted in bit-redistribution, where greater bits and smaller partitioning were allocated to perceptually significant regions. Using a HEVC optimised processor the timing increase was < +4% and < +6% for video streaming and recording applications respectively, 1/3 of an existing low complexity PVC solution. Future work should be directed towards perceptual quantisation which offers the potential for perceptual coding gain.

This thesis addresses the problem of reconstructing complex real-world dynamic scenes without prior knowledge of the scene structure, dynamic objects or background. Previous approaches to 3D reconstruction of dynamic scenes either require a controlled studio set-up with chroma-key backgrounds or prior knowledge such as static background appearance or segmentation of the dynamic objects. This thesis presents a new approach which enables general dynamic scene reconstruction. This is achieved by initializing the reconstruction with sparse wide-baseline feature matches between views which avoids the requirement for prior knowledge of the background appearance or assumptions that the background is static. To achieve sparse reconstruction of dynamic objects a novel segmentation based feature detector SFD is introduced. SFD is shown to give an order of magnitude increase in the number and reliability of features detected. A coarse-to-fine approach is introduced for reconstruction of dense 3D models of dynamic scenes. This uses joint segmentation and shape refinement to achieve robust reconstruction of dynamic object such as people. The approach is evaluated across a wide-range of indoor and outdoor scenes. The second major contribution of this research is to introduce temporal coherence into the reconstruction process. The dynamic scene is segmented into objects based on the initial sparse 3D feature reconstruction of the scene. Dense reconstruction is then performed for each object. For dynamic objects the reconstruction is propagated over time to provide a prior for the reconstruction at successive frames in the sequence. This is combined with the introduction of a geodesic star convexity constraint in the segmentation refinement to improve the segmentation of complex objects. Evaluation on general dynamic scene demonstrates significant improvement in both segmentation and reconstruction with temporal coherence reducing the ambiguity in the reconstruction of complex shape. The final significant contribution of this research is the introduction of a complete framework for 4D temporally coherent shape reconstruction from one or more camera views. The 4D match tree is introduced as an intermediate representation for robust alignment of partial surface reconstructions across a complete sequence. SFD is used to achieve wide-timeframe matching of partial surface reconstructions between any pair of frames in the sequence. This allows the evaluation of a frame-to-frame shape similarity metric. A 4D match tree is then reconstructed as the minimum spanning tree which represents the shortest path in shape similarity space for alignment across all frames in the sequence. The 4D match tree is applied to achieve robust 4D shape reconstruction of complex dynamic scenes. This is the first approach to demonstrate 4D reconstruction of general real-world dynamic scenes with non-rigid shape from video.