Live Video Ingest System Using Neural Super-Resolution

The patent introduces a neural network-based super-resolution model as an intermediary processing layer between the streamer and the content distribution server. This intermediary performs super-resolution processing on received video streams, transforming low-resolution videos into high-resolution versions without requiring the streamer to transmit high-resolution content directly. The intermediary handles the computational burden of resolution enhancement, allowing viewers to access high-resolution content even when streamer bandwidth or encoding capacity is limited.

The patent replaces traditional mechanical bandwidth optimization approaches with neural network-based super-resolution technology. Instead of relying solely on increasing transmission bandwidth or encoding capacity to achieve high resolution, the system uses AI-driven algorithms to synthetically enhance video resolution. This substitution allows high-resolution video delivery without proportionally increasing infrastructure capacity requirements.

The super-resolution model acts as an intermediary that decouples video resolution quality from streamer transmission environment and viewer terminal performance. The model receives video streams at various resolutions from streamers with different transmission capabilities and outputs standardized high-resolution content, insulating both the streamer and viewer from environmental constraints. This intermediary processing layer ensures consistent high-quality output regardless of upstream or downstream performance variations.

The system dynamically adjusts video resolution parameters based on available bandwidth and processing capabilities. The super-resolution model can process videos at different input resolutions and transform them to target high-resolution output, allowing flexible adaptation to varying transmission conditions. This parameter transformation capability enables high-resolution delivery without requiring fixed high-bandwidth channels throughout the entire transmission path.

The patent employs pre-trained neural network models that have been previously trained on large datasets of video pairs, eliminating the need for real-time training during streaming. The model weights are prepared in advance through extensive offline training, allowing immediate deployment for real-time super-resolution processing. This preliminary training action removes the computational barrier to real-time application, enabling low-latency video enhancement without requiring complex online learning operations.

The system uses pre-trained model copies that can be efficiently loaded and executed in real-time. Instead of training models dynamically during streaming, the patent leverages replicated pre-trained model versions that capture optimal super-resolution transformations. These model copies are deployed across the system to process incoming video streams, providing consistent real-time enhancement performance without the computational overhead of ongoing training operations.