TEMPORAL STRUCTURE-BASED CONDITIONAL CONVOLUTIONAL NEURAL NETWORKS FOR VIDEO COMPRESSION

Video encoding and decoding is implemented with auto encoders using luminance information to derive motion information for chrominance prediction. In one embodiment conditional convolutions are used to encode motion flow information. A current condition, for example, GOP structure, is used as input...

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Bibliographic Details
Main Authors BEGAINT, Jean, RACAPE, Fabien, PUSHPARAJA, Akshay, FELTMAN, Simon
Format Patent
LanguageEnglish
Published 06.06.2024
Subjects
ELECTRIC COMMUNICATION TECHNIQUE
ELECTRICITY
PICTORIAL COMMUNICATION, e.g. TELEVISION
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Summary:Video encoding and decoding is implemented with auto encoders using luminance information to derive motion information for chrominance prediction. In one embodiment conditional convolutions are used to encode motion flow information. A current condition, for example, GOP structure, is used as input to a succession of fully connected layers to implement the conditional convolution. In a related embodiment, more than one reference frame is used to encode motion flow information.
Bibliography:Application Number: US202218281844