Video Frame Upsampling With Jitter-Aligned Neural Refinement

The system performs preliminary upsampling using simple interpolation methods to generate an initial high-resolution frame before neural network refinement. This preliminary action reduces the complexity of the subsequent neural network processing by providing a closer starting point to the final solution, thereby reducing overall processing time while maintaining image quality.

The super resolution process is divided into multiple stages: initial upsampling using interpolation, followed by selective neural network refinement only for specific regions or frames. This segmentation allows the system to achieve high image quality where needed while minimizing the computational burden and processing time across the entire video sequence.

Instead of applying full neural network processing to every frame, the system uses partial action by selectively applying neural network refinement only to certain frames or regions that benefit most from it. This reduces overall power consumption while maintaining acceptable image quality across the video sequence.

The preliminary interpolation-based upsampling provides a good initial solution that requires minimal power, with neural network refinement applied only when necessary. This two-stage approach significantly reduces power consumption compared to applying full neural network processing to all frames.

The system segments the processing workload between simple interpolation operations that can be performed by basic hardware and more complex neural network operations. This segmentation allows the use of simpler, less expensive hardware while still achieving high image quality through the coordinated use of both approaches.

The system uses computationally inexpensive interpolation methods as a disposable preliminary step that generates an initial upscaled frame. This cheap approach handles the bulk of the work, reserving more expensive neural network processing only for final refinements, thereby reducing overall device complexity.

The preliminary interpolation step generates an initial high-resolution frame using minimal memory, as interpolation only requires storing the original low-resolution frame and computing new pixel values on-the-fly. This reduces peak memory usage compared to loading entire neural network models into memory.

The system extracts and processes only the necessary portions of image data at each stage, rather than loading and processing complete high-resolution frames through the entire pipeline. This extraction approach reduces memory usage by keeping only essential data in memory at any given time.