Elastic Algorithms for Region of Interest Video Compression, with Application to Mobile Telehealth
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Video is the most demanding modality from the viewpoints of bandwidth, computational complexity, and resolution. Thus, there has been limited progress in the field of mobile video technology. In the research, the focus is on elastic wireless video technology, and its adaptation to diagnostic application requirements in real-time clinical assessment. It is important and timely to apply wireless video technology to real-time remote diagnosis of emergent medical events. This premise comes from initial successes in telehealth based on wired networks. The enablement of mobility (for the physician and/or the patient) by wireless communication will be a next major step, but this advance will depend on definitive and compelling demonstrations of reliability. Thus, an important goal of the research is to develop a complete methodology that will be embraced by physicians. Acute pediatric asthma has been identified as a domain where this new capability will be highly welcome. The research uses flexible and interactive algorithms for Region-of-Interest (ROI) processing. ROI processing is a useful approach to achieve the optimal balance in the quality-bandwidth tradeoff characteristic of visual communication services. The notion of ROI has been traditionally used mostly for foreground-background separation in scene rendering and manipulation, and only more recently for variably quality compression. Even when the latter goal is considered, quality criteria have been ad-hoc and at best useful for video conferencing, given that the medical domain has its own fidelity criteria. The research thus focuses on the design of an elastic ROI-based compression paradigm with medical diagnosis as a central criterion. The research describes the methodology to achieve elasticity through rate control algorithms at the encoder. An elastic non-parametric approach is proposed that uses a priori user-specified video quality information, quantifies this information, and incorporates this into the encoder in the form of region-quality mappings. This method is compared to a parametric bit allocation approach that is based on region-features and a set of tuning weights. A number of videos of actual patients were filmed and used as the video database for the developed algorithms. In testing the elastic non-parametric and parametric algorithms, both objective measures in the form of Peak Signal to Noise Ratio (PSNR), and subjective evaluations were used. Thus, in this work, the focus is on domain relevance of the algorithms developed, as opposed to network related issues such as packet losses. This is justified in that these may have broader value with other applications, and continuation of this work will include realistic network conditions. To summarize, the research shows the usefulness of ROI processing as a means of achieving a gain (in a bits per pixel sense) over uniform compression at the same bitrate. It also shows how quantifying a notion of functionally lossless video quality diagnostically lossless video quality in a video-based telehealth system, in a bits per pixel sense is useful from an applications and bitrate perspective. Finally, a combination of these two concepts is advantageous i.e. diagnostically lossless ROI video quality is achievable over bitrate limited channels.