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

• Main Authors: Azar, Hassane Samir, Pore, Vinayak, Prasad, Keshava
• Format: Patent
• Language: English
• Published: 16.03.2023
• Subjects: ELECTRIC COMMUNICATION TECHNIQUE; ELECTRICITY; PICTORIAL COMMUNICATION, e.g. TELEVISION

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.