Until vaccines and effective therapeutics become available, the practical solution to transit safely out of the current coronavirus disease 19 (CoVID-19) lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of results, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ~200 coronavirus disease (CoVID-19)-suspected NHS patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. Therefore, this system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories.
Abstract-Determining the best partitioning structure of a Coding Tree Unit (CTU) is one of the most time consuming operations in HEVC encoding. Specifically, it is the evaluation of the quadtree hierarchy using the Rate-Distortion (RD) optimization that has the most significant impact on the encoding time, especially in the cases of High Definition (HD) and Ultra High Definition (UHD) videos. In order to expedite the encoding for low delay applications, this paper proposes a Coding Unit (CU) size selection and encoding algorithm for inter-prediction in the HEVC. To this end, it describes (i) two CU classification models based on Inter N×N mode motion features and RD cost thresholds to predict the CU split decision, (ii) an online training scheme for dynamic content adaptation, (iii) a motion vector reuse mechanism to expedite the motion estimation process, and finally introduces (iv) a computational complexity to coding efficiency trade-off process to enable flexible control of the algorithm. The experimental results reveal that the proposed algorithm achieves a consistent average encoding time performance ranging from 55% -58% and 57% -61% with average Bjøntegaard Delta Bit Rate (BDBR) increases of 1.93% -2.26% and 2.14% -2.33% compared to the HEVC 16.0 reference software for the low delay P and low delay B configurations, respectively, across a wide range of content types and bit rates.
Abstract-It is well known that when the two eyes are provided with two views of different resolutions the overall perception is dominated by the high resolution view. This property, known as binocular suppression, is effectively used to reduce the bit rate required for stereoscopic video delivery, where one view of the stereo pair is encoded at a much lower quality than the other. There have been significant amount of effort in the recent past to measure the just noticeable level of asymmetry between the two views, where asymmetry is achieved by encoding views at two quantization levels. However, encoding artifacts introduce both blurring and blocking artifacts in to the stereo views, which are perceived differently by the human visual system. Therefore, in this paper, we design a set of psycho-physical experiments to measure the just noticeable level of asymmetric blur at various spatial frequencies, luminance contrasts and orientations. The subjective results suggest that humans could tolerate a significant amount of asymmetry introduced by blur, and the level of tolerance is independent of the spatial frequency or luminance contrast. Furthermore, the results of this paper illustrate that when asymmetry is introduced by unequal quantization, the just noticeable level of asymmetry is driven by the blocking artifacts. In general, stereoscopic asymmetry introduced by way of asymmetric blurring is preferred over asymmetric compression. It is expected that the subjective results of this paper will have important use cases in objective measurement of stereoscopic video quality and asymmetric compression and processing of stereoscopic video.
Abstract-The energy consumption of Consumer Electronic (CE) devices during media playback is inexorably linked to the computational complexity of decoding compressed video. Reducing a CE device's the energy consumption is therefore becoming ever more challenging with the increasing video resolutions and the complexity of the video coding algorithms. To this end, this paper proposes a framework that alters the video bit stream to reduce the decoding complexity and simultaneously limits the impact on the coding efficiency. In this context, this paper (i) first performs an analysis to determine the trade-off between the decoding complexity, video quality and bit rate with respect to a reference decoder implementation on a General Purpose Processor (GPP) architecture. Thereafter, (ii) a novel generic decoding complexity-aware video coding algorithm is proposed to generate decoding complexity-rate-distortion optimized High Efficiency Video Coding (HEVC) bit streams. The experimental results reveal that the bit streams generated by the proposed algorithm achieve 29.43% and 13.22% decoding complexity reductions for a similar video quality with minimal coding efficiency impact compared to the state-of-the-art approaches when applied to the HM16.0 and openHEVC decoder implementations, respectively. In addition, analysis of the energy consumption behavior for the same scenarios reveal up to 20% energy consumption reductions while achieving a similar video quality to that of HM 16.0 encoded HEVC bit streams.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.