ENTROPY-BASED PRE-FILTERING USING NEURAL NETWORKS FOR STREAMING APPLICATIONS

In various examples, a deep neural network (DNN) based pre-filter for content streaming applications is used to dynamically adapt scene entropy (e.g., complexity) in response to changing network or system conditions of an end-user device. For example, where network and/or system performance issues o...

Full description

Saved in:
Bibliographic Details
Main Authors Azar, Hassane Samir, Pore, Vinayak, Prasad, Keshava
Format Patent
LanguageEnglish
Published 16.03.2023
Subjects
ELECTRIC COMMUNICATION TECHNIQUE
ELECTRICITY
PICTORIAL COMMUNICATION, e.g. TELEVISION
Online AccessGet full text

Cover

More Information
Summary:In various examples, a deep neural network (DNN) based pre-filter for content streaming applications is used to dynamically adapt scene entropy (e.g., complexity) in response to changing network or system conditions of an end-user device. For example, where network and/or system performance issues or degradation are identified, the DNN may be implemented as a frame pre-filter to reduce the complexity or entropy of the frame prior to streaming-thereby allowing the frame to be streamed at a reduced bit rate without requiring a change in resolution. The DNN-based pre-filter may be tuned to maintain image detail along object, boundary, and/or surface edges such that scene navigation-such as by a user participating in an instance of an application-may be easier and more natural to the user.
Bibliography:Application Number: US202117466176