OBJECTIVES: Report the development of the patient-centered myAURA application and suite of methods designed to aid epilepsy patients, caregivers, and clinicians in making decisions about self-management and care. MATERIALS AND METHODS: myAURA rests on an unprecedented collection of epilepsy-relevant heterogeneous data resources, such as biomedical databases, social media, and electronic health records (EHRs). We use a patient-centered biomedical dictionary to link the collected data in a multilayer knowledge graph (KG) computed with a generalizable, open-source methodology. RESULTS: Our approach is based on a novel network sparsification method that uses the metric backbone of weighted graphs to discover important edges for inference, recommendation, and visualization. We demonstrate by studying drug-drug interaction from EHRs, extracting epilepsy-focused digital cohorts from social media, and generating a multilayer KG visualization. We also present our patient-centered design and pilot-testing of myAURA, including its user interface. DISCUSSION: The ability to search and explore myAURA's heterogeneous data sources in a single, sparsified, multilayer KG is highly useful for a range of epilepsy studies and stakeholder support. CONCLUSION: Our stakeholder-driven, scalable approach to integrating traditional and nontraditional data sources enables both clinical discovery and data-powered patient self-management in epilepsy and can be generalized to other chronic conditions.

The vast majority of today's internet video services are consumed over-the-top (OTT) via reliable streaming (HTTP via TCP), where the primary noticeable delivery-related impairments are startup delay and stalling. In this paper we introduce an objective model called the delivery quality score (DQS) model, to predict user's QoE in the presence of such impairments. We describe a large subjective study that we carried out to tune and validate this model. Our experiments demonstrate that the DQS model correlates highly with the subjective data and that it outperforms other emerging models.

The translational motion model can't effectively model complex motion such as scaling, shearing and rotation. That is why more complex motion models like the affine model have been proposed. However, affine motion vectors (AMV)s are more complicated to encode than the translational motion vectors. In this paper we propose several context-coder solutions based on our novel context type limited exhaustive search simulation. As a result the average compression gains of 7.9%, 5.9%, and 9.6%, for mobile, cost guard, and modified mobile video sequences were respectively realized, with peek compression improvements of 13%. In addition, our simulation is described and successfully compared with J. Vaisey and Jin Tong (2002).

Translational motion vectors (MV)s and macro block (MB) frame partitioning are the predominant means of motion estimation (ME) and motion compensation (MC). However, the translational motion model does not describe sufficiently complex motion such as rotation, zoom or shearing. To remedy this one can start computing more advanced motion parameters and/or partition the frame differently. However these approaches are either very computationally expensive and/or have limited search ranges. Thus, in this paper we propose a novel post processing stage which can be easily incorporated into most of the current coders. This stage generates the predictor for each inter MB, based on an affine motion model using translational motion vectors. Our approach has very low computational complexity, however average PSNR gains of up to 0.6 dB were realized for video sequences with complex motion.

Affine motion compensated prediction (AMCP) is an advanced tool which may be incorporated into future video compression standards. There are numerous coders already using AMCP . However the increased number of motion vector components is a disadvantage and quantizing these components can have significant consequences on the difference macro blocks (DMBs). This paper examines the quantization of affine motion vector (AMV) coefficients, by deriving a quadratic relationship between DMB energy and AMV quantization step size. Mathematical derivations and simulations are provided, including two literature comparisons demonstrating the benefits of this work. In the first comparison, the quantization of orthogonalized AMVs in is compared with quantization guided by the novel quadratic model. In the second comparison, Nokia's MVC coder is modified to use the quadratic model to generate quantization step sizes for various granularities; sequence, frame, and quarter-frame, demonstrating up to 8.7% bit rate reductions. Model driven AMV quantization step size choices are shown to be very close to and even outperform limited exhaustive search AMV quantization step size choices, at a quarter of the computational cost

In all of the video coding standards like H.26X and MPEG-X, much of the compression comes from motion compensated prediction (MCP). Translational motion vectors (MVs) poorly model complex motion and thus coders using polynomial or affine MVs have been proposed in the past. In this paper, we demonstrate a novel affine predictor stage which can be easily incorporated into current codecs greatly increasing MCP quality. If used passively to generate the final prediction, gains of up to 0.7 and 1.6 dB were easily realized for ldquomobilerdquo and ldquoflower gardenrdquo video sequences, respectively. In addition, when the translational MVs are refined, gains of up to 0.98 and 1.88 dB for ldquomobilerdquo and ldquoflower gardenrdquo video sequences were respectively realized.

An affine motion model provides better motion representation than a translational motion model. Therefore, it is a good candidate for advanced video compression algorithms, requiring higher compression efficiency than current algorithms. One disadvantage of the affine motion model is the increased number of motion vector parameters, therefore increased motion vector bit rate. We develop and analyze several simulation based approaches of entropy coding for orthonormalized affine motion vector (AMV) coefficients, by considering various context-types and coders. In our work we expand the traditional idea of a context type by introducing four new context types. We compare our method of contexts-type and coder selection with context quantization. The best of our contexts-type and coder solutions produces 4% to 15% average AMV bit-rate reductions over the original VLC approach. For more difficult content AMV bit rate reduction up to 26% is reported.

This paper derives the relationship between the energy of difference macroblock and affine motion vector quantization step size. This is an important step in the analysis of advanced motion models for future video coding methods, as it facilitates rate optimization for affine motion vectors. The derived model shows that the difference macroblock energy has a squared relationship with the affine motion vector quantization step size. In addition, experimental results are shown validating this result and providing further insight.

We address the problem of robust video transmission over unreliable networks. Our approach employs the principle of multiple-descriptions, through pre- and post-processing of the video data, without modification to the source or channel codecs. We employ oversampling to add redundancy to the original video data followed by a decomposition of the oversampled video frames into "equal" sub-images which can be coded and transmitted over separate channels. Simulations using two descriptions show that this approach maintains excellent reconstructed video quality when only one description is received.

We present an effective framework for increasing the error-resilience of low bit-rate video communications over an error-prone packet-switched network. Our framework is based on the principle of layered coding with transport prioritization. We introduce a rate-distortion optimized mode-selection algorithm for our prioritized layered framework. This algorithm is based on a joint source/channel-coding approach and trades off source coding efficiency for increased bitstream error-resilience to optimize the video coding mode selection within and across layers. The algorithm considers the channel conditions, as well as the error recovery and concealment capabilities, of the channel codec and source decoder, respectively. Important framework parameters including the packetization scheme, decoder error concealment method, and channel codec error-protection strength are considered. The effects of mismatch between the parameters employed by the encoder and the actual channel conditions are considered. Results are presented for a wide range of packet loss rates in order to illustrate the benefits of the proposed framework.

We present a rate-distortion optimal joint source/channel coding framework for efficient and robust low bit rate video communications in packet networks. The proposed algorithm combines layered coding with transport prioritization in a novel operational rate-distortion optimization framework that trades off source coding performance for channel coding protection. The system is demonstrated to achieve significant improvement in reconstructed video quality for a range of packet loss rates.

Summary form only given. We present our work on rate-distortion (RD) optimized spatial scalability for MC-DCT based video coding. Extending our work on RD optimized coding from the single layered to the multi-layered framework, we incorporate the additional inter-layer coding dependencies present in a multilayered framework into the set of permissible coding parameters. We employ the Lagrangian rate-distortion functional as it provides an elegant framework for determining the optimal choice of motion vectors, coding modes, and quantized coefficient levels by weighting a distortion term against a resulting rate term. We obtain a simple relationship between the Lagrangian parameter /spl lambda/, that controls rate-distortion tradeoffs, and the reference and enhancement layer quantization parameters QP, to allow the RD optimized framework to work easily in conjunction with rate control techniques that control the average bit rate by adjusting the quantization parameters. We then incorporate these relationships into our coder and generate two-layer bit streams with both the non-RD optimized coder and the RD optimized coder. We also generate RD optimized single-layer bit streams with the same resolution as the second layer of the two-layer bit streams. For the two-layer bit streams, we obtain a 0.6 to 1.4 dB improvement in PSNR by using RD optimization in both the base and enhancement layers. Compared to the single-layer bit stream, RD optimization in both the base and enhancement layers causes the decrease in PSNR to be reduced from 1.1 to 1.7 dB, to 0.3 to 0.5 dB.

It is well-known that flexibility and error resilience are significantly improved by employing a scalable bit stream. The major drawback of multi-layered representations within a motion compensated (MC) discrete cosine transform (DCT) based framework is the increase in bit rate as compared to a single-layered representation having the same frequency, spatial and temporal resolution as in the highest layer of the multi-layered representation. Using rate-distortion (RD) optimization techniques, we can improve the compression efficiency of MC-DCT based SNR and spatially scalable video coding framework. We first show how RD optimization techniques can be applied independently for each layer. We then extend the framework to consider coding decisions jointly across layers.

We present a semi-fixed-length motion vector coding method for H.263-based low bit rate video compression. The method exploits structural constraints within the motion field. The motion vectors are encoded using semi-fixed-length codes, yielding essentially the same levels of rate-distortion performance and subjective quality achieved by H.263's Huffman-based variable length codes in a noiseless environment. However, such codes provide substantially higher error resilience in a noisy environment.

We present an efficient computation constrained block-based motion vector estimation algorithm for low bit rate video coding that yields good tradeoffs between motion estimation distortion and number of computations. A reliable predictor determines the search origin, localizing the search process. An efficient search pattern exploits structural constraints within the motion field. A flexible cost measure used to terminate the search allows simultaneous control of the motion estimation distortion and the computational cost. Experimental results demonstrate the viability of the proposed algorithm in low bit rate video coding applications. The resulting low bit rate video encoder yields essentially the same levels of rate-distortion performance and subjective quality achieved by the UBC H.263+ video coding reference software. However, the proposed motion estimation algorithm provides substantially higher encoding speed as well as graceful computational degradation capabilities.

The emerging ITU-T H.263+ low bit-rate video coding standard is version 2 of the draft international standard ITU-T H.263. In this paper, we discuss this emerging video coding standard and present compression performance results based on our public domain implementation of H.263+.

We discuss the ITU-T H.263+ (or H.263 Version 2) low-bit-rate video coding standard. We first describe, briefly, the H.263 standard including its optional modes. We then address the 12 new negotiable modes of H.263+. Next, we present experimental results for these modes, based on our public-domain implementation (http:ilspmg.ece.ubc.ca). Tradeoffs among compression performance, complexity, and memory requirements for the H.263+ optional modes are discussed. Finally, results for mode combinations are presented.