API to Disable Neural Network Frame Interpolation

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

High-quality video processing is hindered by significant memory and resource requirements, particularly in handling high-resolution videos with complex content, due to limitations in computing resources.

Innovation Solution

The use of neural networks to generate interpolated video frames by blending motion warped color frames based on blending factors, which are upscaled to match the resolution of the original frames, allowing for efficient frame rate increase without excessive resource utilization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If neural networks are used to generate interpolated video frames, then frame rate is increased and processing efficiency is enhanced, but memory and computational resources are significantly consumed

Engineering Contradiction:

Improveframe rateVSAvoidcomputational resources

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The video processing task is divided into separate functions: motion estimation, blending factor generation, and frame blending. Each function is processed independently using specialized neural network modules, allowing efficient resource utilization by processing only the necessary portions of video data through targeted computational operations rather than monolithic processing

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically adjusts blending factors as parameters to control the interpolation process. By optimizing blending factor generation through neural networks and adjusting these parameters adaptively, the system achieves high frame rates while managing computational resource consumption through intelligent parameter control rather than brute force processing

Inventive Principle:

Principle #35Parameter changes

2Manufacturing precision

If high-resolution video processing is performed, then video quality is improved, but memory requirements and processing time increase significantly

Engineering Contradiction:

Improvevideo qualityVSAvoidmemory requirements

Core Design Contradiction:

Manufacturing precisionVSQuantity of substance

Solution Approach 1:

Instead of processing entire high-resolution video frames through all processing stages, the system applies partial processing by generating motion information and blending factors selectively. The neural networks process only the essential features and motion vectors needed for quality enhancement, achieving high video quality while reducing overall memory requirements through partial action on critical data elements

Inventive Principle:

Principle #16Partial or excessive action

3Speed

If video processing is performed quickly, then processing speed is improved, but processing accuracy and quality may be compromised

Engineering Contradiction:

Improveprocessing speedVSAvoidprocessing accuracy

Core Design Contradiction:

SpeedVSManufacturing precision

Solution Approach 1:

The system replaces traditional mechanical processing methods with neural network-based computational models. By substituting conventional image processing algorithms with trained neural networks that can rapidly estimate motion and generate blending factors, the system achieves both high processing speed and accurate quality enhancement through learned patterns rather than computationally intensive manual processing

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20240095881A1Application programming interface to disable frame interpolation

Publication Date: 2024.03.21 NVIDIA CORP

AI summary

Apparatuses, systems, and techniques to process image frames. In at least one embodiment, an application programming interface (API) is performed to disable frame interpolation to use one or more neural networks.