Adaptive Frame Generation with Motion-Based Mode Selection
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Solution Overview
Problem
Conventional frame generation methods in real-time graphics rendering suffer from inefficiencies such as increased latency and reduced image quality due to fixed patterns, especially during scene changes, and are computationally intensive, making them unsuitable for real-time applications.
Innovation Solution
A method and processor that utilize motion vectors to dynamically decide whether to copy, generate, or render frames based on the extent of change between successive frames, employing a Neural Network (NN) for generation and Graphics Processing Unit (GPU) for rendering, with adaptive threshold adjustments to optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If frame interpolation is used to generate new frames, then frame rate is improved, but input latency increases due to requiring future frames
Solution Approach 1:
The patent inverts the traditional frame interpolation approach by using frame extrapolation instead. Rather than predicting intermediate frames between past and future frames (which requires future frames and causes latency), the system predicts future frames based solely on past frames, eliminating the need to wait for future frames and thus reducing input latency while maintaining frame rate improvement
2Loss of time
If frame extrapolation is used to reduce latency, then input latency is reduced, but frame quality becomes unreliable
Solution Approach 1:
The patent applies dynamics by making the frame generation approach adaptive rather than fixed. The system dynamically switches between frame copying, frame generation, and frame rendering based on motion detection. When motion is detected, it uses frame extrapolation to maintain low latency; when scene changes are detected, it switches to frame rendering to ensure quality, thus making the system reliable across different scenarios
3Device complexity
If fixed pattern frame generation is used, then implementation is simplified, but image quality deteriorates during scene changes
Solution Approach 1:
The patent replaces the fixed pattern with a dynamic decision-making process. A motion detection module continuously monitors motion vectors between frames, and based on this real-time information, the system adapts its frame generation strategy. This dynamic approach maintains simplicity while significantly improving image quality during scene changes by switching to full rendering when motion exceeds thresholds
4Reliability
If every second frame is rendered at full resolution, then image quality is maintained, but computational overhead increases
Solution Approach 1:
The patent applies local quality by differentiating treatment between different types of frames based on motion characteristics. Instead of uniformly rendering every second frame, the system selectively renders only those frames where motion exceeds a threshold, while using lighter frame copying or generation for low-motion frames. This local differentiation maintains image quality where needed while reducing computational overhead overall
Data Source
AI summary
A method and processor for real-time frame processing of a sequence of frames, focusing on motion vector determination, decision metric calculation, and selective triggering of modes for frame processing. It involves calculating motion vectors and their metrics based on pixel displacement and gradient magnitude between consecutive frames to decide on the most appropriate processing mode-copying, generating via neural networks, or rendering using GPUs. The method adapts over time by adjusting thresholds for motion vector metrics, employing decay functions to refine the decision-making process for subsequent frames. This adaptive approach aims to optimize frame generation and rendering quality in dynamic sequences, providing a sophisticated method for managing and enhancing visual media in real-time applications. The processor is configured to execute these steps, adjusting its operations as it processes more frames, ensuring efficient and high-quality visual outputs.