Neural Image Generation Using Keyframes and Residual Frames

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Solution Overview

Problem

Processing video data requires significant memory, time, and computational resources, which existing technologies have not efficiently addressed.

Innovation Solution

Utilizing neural networks to process video frames by categorizing them into keyframes and residual frames, employing sparse compression techniques, and using multiple layers to extract features efficiently.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If traditional video processing methods are used, then processing accuracy is maintained, but memory and computational resources are excessively consumed

Engineering Contradiction:

Improvememory and computational resourcesVSAvoidprocessing accuracy

Core Design Contradiction:

Quantity of substanceVSReliability

Solution Approach 1:

The patent divides video processing into two distinct pathways: processing keyframes through a first neural network and processing residual frames through a second neural network. This segmentation allows the system to allocate computational resources selectively, applying full processing only to keyframes while using a more efficient pathway for residual frames, thereby reducing overall memory and computational consumption while maintaining processing accuracy.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies partial processing to residual frames by using a second neural network that operates on compressed residual data rather than full-resolution frames. This partial action approach processes only the essential differences from keyframes, reducing the computational burden while still capturing necessary video information for accurate reconstruction.

Inventive Principle:

Principle #16Partial or excessive action

2Productivity

If full video frames are processed, then feature extraction completeness is achieved, but processing time increases significantly

Engineering Contradiction:

Improveprocessing speedVSAvoidfeature extraction completeness

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The patent extracts and separates the essential video information by identifying and processing only keyframes in full detail while extracting only the residual differences for other frames. This extraction principle allows the system to capture all necessary features from keyframes while representing other frames through compact residual data, significantly reducing processing time while maintaining feature extraction completeness.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent applies different processing qualities to different parts of the video stream: keyframes receive full-quality processing through the first neural network, while residual frames receive compressed processing through the second neural network. This local quality approach ensures that critical frames are processed with complete feature extraction while less critical frames use efficient compressed processing, optimizing both speed and information retention.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS12536798B2Image generation using a neural network

Publication Date: 2026.01.27 NVIDIA CORP

AI summary

Apparatuses, systems, and techniques to generate an image. In at least one embodiment, one or more neural networks are to generate a second image based, at least in part, on a first image and information indicating zero or more differences between the first and second image.