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

Abstract: 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.

Inventor(s): Keshava Prasad, Hassane Samir Azar, Vinayak Pore

CPC Classification: H04N19/117 (Filters, e.g. for pre-processing or post-processing (sub-band filter banks ))

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