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There are 6 prediction modes for 4×4 sub-blocks, 4 prediction modes for 16×16 macroblocks, including DC prediction, vertical prediction, and horizontal prediction. (...) These pixels are already decoded and are used for prediction. The 6 intra prediction modes are labeled 0 to 5. (...) Therefore, the prediction may result in a large prediction error and obvious blocking artifacts.
Language:English
Score: 685907.5 - https://www.itu.int/wftp3/av-a...deo-site/0101_Eib/VCEG-L09.doc
Data Source: un
To remedy this problem, we propose to use a new motion vector prediction method (described below) in H.263L. The new method, shown in Figure 1(c), is similar in some way to the method used in the MPEG-4 VM 7.0 error resilient mode (Figure1(b)) (i.e. each packet is predictively coded independently, predictor is drawn from a MB or block on the same row of MB or block to be coded), but with major differences in the way prediction is performed when MBs with 4 MVs are used. (...) File:Q15C33.DOC Page: PAGE 1 Date Printed: DATE \* MERGEFORMAT 11/25/97 Figure 1(c) Proposed motion vector prediction method. (In the above figures, arrows designates motion vector prediction directions, MB (macro block) designates a 16x16 block in the frame, MV, PMV and MVD designates motion vector, predicted motion vector and motion vector difference respectively. ) Figure 1(a) H.263, H.263+ and default MPEG-4 motion prediction method. (...) MB with 1 MV MB with 1 MV Figure 1(b) MPEG-4 (VM 7.0) error resilient mode motion vector prediction. Predicted Motion Vector PMV=Median (MV1, MV2, MV3) Motion Vector Difference MVD=MV-PMV MV MV3 MV2 MV1
Language:English
Score: 685863.57 - https://www.itu.int/wftp3/av-a...video-site/9712_Eib/q15c35.doc
Data Source: un
SUBTOPIC CATEGORIES UNDER TG-CARDIO: Inclusive of the subtopic on CVD risk prediction, subtopics under TG-Cardio may address a range of applications of AI in CVD management which are broadly classified by Yan et al (2019) into the 4 subtopic categories listed below: [X] CLINICAL PREDICTIONS - Cardiovascular disease (CVD) Risk Prediction. (...) Benjamin Muthambi (IEPH, South Africa) o Subtopic Represented: CLINICAL PREDICTIONS - Cardiovascular disease (CVD) Risk Prediction. • TG-Cardio Topic Co-Chair: Dr. (...) The TG-Cardio topic group/community of stakeholders will be different... 2.2 Subtopic Group TG-CARDIO | CVD Risk Prediction: 3 Introductory Subject Information: AI for Health Topic Group: Cardiovascular Disease (CVD) Risk Prediction 3.1 Project Objectives/Problem to be addressed: Diabetics have higher CVD risk, hence improved CVD risk prediction is critical for better diabetes management and reducing mortality.
Language:English
Score: 685546.04 - https://www.itu.int/en/ITU-T/f...ardio-Clinical_Predictions.pdf
Data Source: un
In this contribution, we propose a simplification of the weighted prediction based on the proposals on the reflector with modifications; simplified implicit mode with single multiplication and combination of the implicit bi-prediction and the explicit single prediction. (...) Implementation consideration Table 1 summarizes the necessary operations per pixel to calculate the prediction signal for the weighted bi-predictive prediction of various documents, software and proposals. (...) Therefore, we recommend the following: · Replace the implicit weighted bi-prediction with the proposed simplified form using only one multiplication. · Allow combination of the explicit weighting for the single prediction and the implicit weighting for the bi-predictive prediction in B-slice. · Remove the explicit weighting for the bi-predictive prediction requiring two multiplications per pixel.
Language:English
Score: 685334.77 - https://www.itu.int/wftp3/av-a...ite/2002_12_Awaji/JVT-F077.doc
Data Source: un
PowerPoint Presentation PREDICT: A risk-based tool for regulated products 2 U.S. FDA’s Regulatory Programs • The Food Drug & Cosmetic Act established FDA as the regulatory body which ensures the safety and efficacy of the following products: – Foods – Drugs – Biologics – Medical devices – Radiation emitting electronics – Cosmetics – Veterinary products – Tobacco products www.fda.gov 3 FDA TBT Measures • FDA requires that all products covered under the FD&C meet the same technical requirements, whether imported or produced domestically. • This includes imports of drugs, medical devices, cosmetics, tobacco, and food which carries nutrition facts labels. www.fda.gov 4 Single Window • A single, harmonized data set collected electronically by CBP • Early validation of exporter’s paperwork results in better data quality and quicker admissibility decisions • Coordinated, consolidated status messaging across agencies www.fda.gov 5 Import Volume www.fda.gov 6 PREDICT • All imported products that FDA regulates are electronically screened before they enter the United States www.fda.gov 7 PREDICT • Purpose: Improve import screening and targeting to prevent entry of adulterated, misbranded, or otherwise violative goods into the United States and expedite the entry of non-violative goods. • Method: Replaced the admissibility portion of FDA’s legacy electronic screening process. www.fda.gov 8 PREDICT - Methods • Verification of applicable regulatory requirements, e.g. registration, approval status, etc. • Automated data mining and pattern discovery • Automated review of administrative requirements • Open source intelligence www.fda.gov 9 PREDICT – Improved Targeting • Evaluate shipments on the basis of risk factors and surveillance requirements. • Facilitate automated releases, giving border inspectors more time to evaluate higher risk lines. • For consignments not automatically admitted, identify risk factors for border inspectors to consider in determining disposition. www.fda.gov 10 PREDICT - Risk Factors  Inherent risk of the product  Results of field exams and analytical testing of previous entries from the same producer or country.  Results of facility inspections (foreign and domestic)  Accuracy of import and registration documents www.fda.gov 11 Additional Information https://www.fda.gov/ForIndustry/ImportProgram/default.htm www.fda.gov https://www.fda.gov/ForIndustry/ImportProgram/default.htm Slide Number 1 U.S. FDA’s Regulatory Programs FDA TBT Measures Single Window Import Volume PREDICT PREDICT PREDICT - Methods PREDICT – Improved Targeting PREDICT - Risk Factors Additional Information Slide Number 12
Language:English
Score: 685297.07 - https://www.wto.org/english/tr...tbt_committee_2-5-19_(002).pdf
Data Source: un
Summary of the features of technical proposals for H.26L Q15-F-11 (Telenor) Variable Block Size MC with 4x4 transform Intra Frame Coding 5 prediction mode for each 4x4 block (DC, horizontal, vertical, two diagonals) Prediction mode is coded refering to neighboring blocks Codeword is assigned based on the probability of the mode Inter Frame Prediction M.C. Prediction from more than one past frame (no B-pictures usage is intended) Different block sizes for prediction 16x16, 8x8, 4x4 M.E. (...) Residual coding Any other residual coding means can be combined Not used in the current implementation Current implementation Off-line codec Real-time encoder with different VQ code book Remarks on the results Better than anchor in high QP values (lower bitrate) Q15-F-24 (Nokia) MVC - Affine MC of segmented frame with multiple transform Intra Frame Coding Pixel prediction (7 directions) and DC prediction Prediction error is coded similar concept of Inter prediction DC transmitted separately Different VLC tables from Inter coding One more 4x4 ECVQ code book for (/8 direction Three directional 8x8 KLT Predetermined most probable basis functions Inter Frame Prediction M.C.
Language:English
Score: 685263.4 - https://www.itu.int/wftp3/av-a...video-site/9811_Seo/q15f43.doc
Data Source: un
SUBTOPIC CATEGORIES UNDER TG-CARDIO : Inclusive of the subtopic on CVD risk prediction, subtopics under TG-Cardio may address a range of applications of AI in CVD management which are broadly classified by Yan et al (2019 ) into the 4 subtopic categories listed below: [ X ] CLINICAL PREDICTIONS - Cardiovascular disease (CVD) Risk Prediction . (...) Benjamin Muthambi (IEPH, South Africa) · Subtopic Represented: CLINICAL PREDICTIONS - Cardiovascular disease (CVD) Risk Prediction. · TG-Cardio Topic Co-Chair: Dr. (...) Subtopic Group TG-CARDIO | CVD Risk Prediction: The TG-Cardio subtopic group on CVD risk prediction is a community of stakeholders from the health and AI communities with a shared interest in AI applications in CVD risk prediction .
Language:English
Score: 685160.06 - https://www.itu.int/en/ITU-T/f...ents/all/FGAI4H-H-006-A02.docx
Data Source: un
Mode 0 is ‘DC-prediction’ (see below). The other modes represent directions of predictions as indicated below. 2.5.1 Mode 0: DC prediction Generally all pixels are predicted by (A+B+C+D+E+F+G+H)//8. (...) A block may therefore always be predicted in this mode. 2.5.2 Mode 1: This mode is used only if all A,B,C,D are inside the picture. a is predicted by: (A+B)/2 e is predicted by B b,i are predicted by (B+C)/2 f,m are predicted by C c,j are predicted by (C+D)/2 d,g,h,k,l,n,o,p are predicted by D 2.5.3 Mode 2: Vertical prediction If A,B,C,D are inside the picture, a,e,i,m are predicted by A, b,f,j,n by B etc. 2.5.4 Mode 3: Diagonal prediction This mode is used only if all A,B,C,D,E,F,G,H,I are inside the picture. This is a 'diagonal' prediction. m is predicted by: (H+2G+F)//4 i,n are predicted by (G+2F+E)//4 e,j,o are predicted by (F+2E+I)//4 a,f,k,p are predicted by (E+2I+A)//4 b,g,l are predicted by (I+2A+B)//4 c,h are predicted by (A+2B+C)//4 d is predicted by (B+2C+D)//4 2.5.5 Mode 4:Horizontal prediction If E,F,G,H are inside the picture, a,b,c,d are predicted by E, e,f,g,h by F etc. 2.5.6 Mode 5: This mode is used only if all E,F,G,H are inside the picture. a is predicted by: (E+F)/2 b is predicted by F c,e are predicted by (F+G)/2 f,d are predicted by G i,g are predicted by (G+H)/2 h,j,k,l,m,n,o,p are predicted by H 2.5.7 Prediction of chroma blocks For chroma prediction there is only one mode.
Language:English
Score: 685139.45 - https://www.itu.int/wftp3/av-a...deo-site/0109_San/VCEG-N10.doc
Data Source: un
It predicts input frame as fine as possible using a large set of prediction tools, that results in large amount of overhead bits. (...) We would like to solve this problem of current TML specification by introducing low-overhead INTER prediction modes which maintains prediction performance. (...) Proposed set of INTER prediction modes 3.1. Motion model In this contribution, a new set of INTER prediction modes which improves prediction efficiency with small number of motion vectors is proposed.
Language:English
Score: 685034.13 - https://www.itu.int/wftp3/av-a...deo-site/0109_San/VCEG-N45.doc
Data Source: un
It predicts input frame as fine as possible using a large set of prediction tools, that results in large amount of overhead bits. (...) We would like to solve this problem of current TML specification by introducing low-overhead INTER prediction modes which maintains prediction performance. (...) Proposed set of INTER prediction modes 3.1. Motion model In this contribution, a new set of INTER prediction modes which improves prediction efficiency with small number of motion vectors is proposed.
Language:English
Score: 685034.13 - https://www.itu.int/wftp3/av-a...o-site/0109_San/VCEG-N45r1.doc
Data Source: un