Browsing by Author "Nortje, Andre"

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    Deep image and video compression
    (Stellenbosch : Stellenbosch University, 2020-04) Nortje, Andre; Kamper, M. J.; Engelbrecht, H. A.; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
    ENGLISH ABSTRACT: Forecasts indicate that video will make up 82% of all Internet traffic by 2022. Advancing video compression efficiency will play a crucial role in curbing high bitrates and mitigating excessive bandwidth consumption. To this end, recent deep learning models are emerging as likely successors to hand-tuned standard video codecs. Our goal is to further refine the compression quality of existing video codecs by improving their ability to predict video content. We subdivide video compression into two focus areas: 1. Still image compression of video frames, for which we propose the Binary Inpainting Network (BINet). 2. Motion compression in video, for which we learn binary motion codes (P-FrameNet and B-FrameNet). With BINet we learn to inpaint an image patch from the binary codes of its nearest neighbours to better compress a still image or single video frame (intra-frame compression). We adapt BINet to perform inter-frame prediction with P-FrameNet and B-FrameNet by learning binary motion codes that compensate for the relative displacement undergone by objects in a video sequence across time. Within the context of video compression our prediction methods are, to the best of our knowledge, the first fully parallelisable means of video intra-frame and inter-frame prediction. We show how inclusion of the BINet framework improves the intra-frame compression of a competitive deep image codec across a range of bitrates such that it outperforms the standard image codec JPEG. Experiments also highlight that its full-context patch inpaitings are of a higher quality than those sequentially predicted by the standard image codec WebP. In terms of inter-frame video prediction, we show that our learned binary motion codes describe more complex motion than the block-based optical flow algorithms employed by the standard video codecs: H.264 and H.265. This indicates that the BINet and our learned binary motion codes could be valuable extensions to existing video codecs, specifically in improving their intra-frame and inter-frame compression capabilities.
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    The utility of high-flow nasal cannula oxygen therapy in the management of respiratory failure secondary to COVID-19 pneumonia
    (Health & Medical Publishing Group, 2020-05-07) Lalla, Usha; Allwood, Brian W.; Louw, Elizabeth H.; Nortje, Andre; Parker, Arifa; Taljaard, Jantjie J.; Moodley, Desiree; Koegelenberg, Coenraad F. N.
    COVID-19 is a potentially fatal infection caused by SARS-CoV-2.[1] As of 4 May 2020, more than 6 000 cases had been confirmed in South Africa (SA) with numbers rising steadily, a situation that will place a major strain on the country’s health resources, including its ability to provide intensive care and ventilatory support to patients with severe disease.