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Abstract: Early determination of coronavirus disease 2019 (COVID-19) pneumonia from numerous suspected cases is critical for the early isolation and treatment of patients.The purpose of the study was to develop and validate a rapid screening model to predict early COVID-19 pneumonia from suspected cases using a random forest algorithm in China.A total of 914 initially suspected COVID-19 pneumonia in multiple centers were prospectively included. The computer-assisted embedding method was used to screen the variables. The random forest algorithm was adopted to build a rapid screening model based on the training set. The screening model was evaluated by the confusion matrix and receiver operating characteristic (ROC) analysis in the validation.The rapid screening model was set up based on 4 epidemiological features, 3 clinical manifestations, decreased white blood cell count and lymphocytes, and imaging changes on chest X-ray or computed tomography. The area under the ROC curve was 0.956, and the model had a sensitivity of 83.82% and a specificity of 89.57%. The confusion matrix revealed that the prospective screening model had an accuracy of 87.0% for predicting early COVID-19 pneumonia.Here, we developed and validated a rapid screening model that could predict early COVID-19 pneumonia with high sensitivity and specificity. The use of this model to screen for COVID-19 pneumonia have epidemiological and clinical significance., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

The latest international video coding standard, H.266/Versatile Video Coding (VVC), supports high-definition videos, with resolutions from 4 K to 8 K or even larger. It offers a higher compression ratio than its predecessor, H.265/High Efficiency Video Coding (HEVC). In addition to the quadtree partition structure of H.265/HEVC, the nested multi-type tree (MTT) structure of H.266/VVC provides more diverse splits through binary and ternary trees. It also includes many new coding tools, which tremendously increases the encoding complexity. This paper proposes a fast intra coding algorithm for H.266/VVC based on visual perception analysis. The algorithm applies the factor of average background luminance for just-noticeable-distortion to identify the visually distinguishable (VD) pixels within a coding unit (CU). We propose calculating the variances of the numbers of VD pixels in various MTT splits of a CU. Intra sub-partitions and matrix weighted intra prediction are turned off conditionally based on the variance of the four variances for MTT splits and a thresholding criterion. The fast horizontal/vertical splitting decisions for binary and ternary trees are proposed by utilizing random forest classifiers of machine learning techniques, which use the information of VD pixels and the quantization parameter. Experimental results show that the proposed algorithm achieves around 47.26% encoding time reduction with a Bjøntegaard Delta Bitrate (BDBR) of 1.535% on average under the All Intra configuration. Overall, this algorithm can significantly speed up H.266/VVC intra coding and outperform previous studies. [ABSTRACT FROM AUTHOR]

This paper designs a novel method to reduce the coding complexity of 3D‐HEVC encoder by utilizing the properties of human visual perception. Two vision‐oriented edge detections are proposed: for colour texture detection, the authors adopt the Just‐Noticeable Distortion (JND); for depth map, the authors combine the Sample Adaptive Offset (SAO) and the Just Noticeable Depth Difference (JNDD) model. The authors also analyse the properties of colour texture and depth map to classify the coding tree unit (CTU) into various kinds of types, including complex‐edge CTU, moderate‐edge CTU and homogeneous CTU. Besides, fast mode decisions and early termination criteria are performed individually on each type of CTUs according to their characteristics. Especially for those CTUs with more edge information, the proposed projection‐based fast mode decision and residual‐based early termination preserve important colour texture while speeding up the coding at the same time. The proposed vision‐oriented algorithm reduces 31.981% of the overall average coding time with only 1.580% BD‐Bitrate increase. Experimental results show that the proposed algorithm can provide considerable time‐saving while still maintain the video quality, which outperforms the previous researches. [ABSTRACT FROM AUTHOR]

The 3D extension of High Efficiency Video Coding (3D-HEVC) is the latest international 3D video coding standard, which enhances the compression efficiency of 3D videos with multi-view plus depth (MVD) format. However, it is at the cost of increasing coding complexity. Therefore, this paper proposes a fast inter-prediction algorithm based on depth segmentation for the depth coding of 3D-HEVC to reduce the coding complexity of the depth map. We aim to reduce the coding time by efficiently utilizing the properties of the depth map. First, the proposed algorithm divides a depth map into background, middle ground and foreground, based on automatic thresholding technique. Then, we adjust the search range according to the classification of the coding tree unit (CTU). In addition, an early termination decision by utilizing the correlation of spatial, temporal, and inter-view neighboring CTUs and a fast prediction unit mode decision are also proposed to reduce coding time. The experimental results show that the proposed algorithm can reduce the depth coding time and still maintain good video quality. [ABSTRACT FROM AUTHOR]

Presents information on a study which proposed an algorithm to search the best matching for the lost motion vector for compressed video by using genetic algorithm. Background of the robust and effective genetic algorithm; Proposed error concealment algorithm; Results and discussion.

In response to the increased demand for high-resolution video, the new generation of video standards, High Efficiency Video Coding (HEVC) and its scalable extension (SHVC) have been finalized. The compression of HEVC/SHVC is efficiently improved and supports ultra-high resolution (UHD). Therefore, the coding complexity of HEVC/SHVC is much higher than those of previous standards. The framework of SHVC is based on HEVC and is divided into several types of scalable video. SHVC can be decoded into various video resolutions, frame rates and qualities, and only needs to be encoded once, but with higher complexity than HEVC. Thus, how to reduce the coding complexity of SHVC is the purpose of this paper. Our proposed algorithm accelerates the enhancement layer (EL) prediction by utilizing encoded Coding Unit (CU) sizes, prediction modes, motion vectors and Rate-Distortion Costs (RD-Costs) of the base layer (BL) and encoded CU sizes of the enhancement layer for quality scalability of SHVC. Experimental results show that the proposed algorithm can save lots of time while maintaining good video quality, and the performance is better than those of previous works. [ABSTRACT FROM AUTHOR]