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1. WO2020194254 - QUALITY ASSESSMENT OF A VIDEO

Publication Number WO/2020/194254
Publication Date 01.10.2020
International Application No. PCT/IB2020/052921
International Filing Date 27.03.2020
IPC
G06K 9/46 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
46Extraction of features or characteristics of the image
G06K 9/62 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
G06N 3/02 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
CPC
G06N 20/10
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
10using kernel methods, e.g. support vector machines [SVM]
G06N 3/0481
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0481Non-linear activation functions, e.g. sigmoids, thresholds
G06N 3/084
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
084Back-propagation
G06T 2207/10016
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
2207Indexing scheme for image analysis or image enhancement
10Image acquisition modality
10016Video; Image sequence
G06T 2207/20076
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
2207Indexing scheme for image analysis or image enhancement
20Special algorithmic details
20076Probabilistic image processing
G06T 2207/20081
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
2207Indexing scheme for image analysis or image enhancement
20Special algorithmic details
20081Training; Learning
Applicants
  • OTROSHI SHAHREZA, Hatef [IR]/[IR]
  • AMINI, Arash [IR]/[IR]
  • BEHROOZI, Hamid [IR]/[IR]
Inventors
  • OTROSHI SHAHREZA, Hatef
  • AMINI, Arash
  • BEHROOZI, Hamid
Agents
  • IDESAZAN ASR AFTAB
Priority Data
62/824,32927.03.2019US
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) QUALITY ASSESSMENT OF A VIDEO
(FR) ÉVALUATION DE LA QUALITÉ D'UNE VIDÉO
Abstract
(EN)
A method for quality assessment of a video that includes M video frames. The method includes repeating a first iterative process M times and extracting a score distribution for a plurality of scores. An mth iteration of the first iterative process, where m ∈ [1,M], includes generating an mth frame-level feature set, generating a first recurrent output of a plurality of recurrent outputs based on a zeroth recurrent output of the plurality of recurrent outputs, and generating an mth recurrent output of the plurality of recurrent outputs based on an (m-1)th recurrent output of the plurality of recurrent outputs. The first recurrent output is generated by feeding a first frame-level feature set to a recurrent neural network. The mth recurrent output is generated by feeding the mth frame-level feature set to the recurrent neural network. The score distribution is extracted from an Mth recurrent output of the plurality of recurrent outputs.
(FR)
L'invention concerne un procédé d'évaluation de la qualité d'une vidéo qui comprend M trames vidéo. Le procédé consiste à répéter un premier processus itératif M fois et à extraire une distribution de score pour une pluralité de scores. Une mième itération du premier processus itératif, dans laquelle m ∈ [1,M], consiste à générer un mième ensemble de caractéristiques de niveau de trame, à générer une première sortie récurrente d'une pluralité de sorties récurrentes sur la base d'une zéroième sortie récurrente de la pluralité de sorties récurrentes, et à générer une mième sortie récurrente parmi la pluralité de sorties récurrentes sur la base d'une (m-1)ième sortie récurrente parmi la pluralité de sorties récurrentes. La première sortie récurrente est générée par l'alimentation d'un premier ensemble de caractéristiques de niveau de trame à un réseau neuronal récurrent. La mième sortie récurrente est générée par l'alimentation du mième ensemble de caractéristiques de niveau de trame au réseau neuronal récurrent. La distribution de score est extraite d'une Mième sortie récurrente parmi la pluralité de sorties récurrentes.
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