Neural Video Coding for Variable Frame Rates and Spatial Resolutions
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Solution Overview
Problem
Existing video coding technologies lack the ability to decode video at arbitrary frame rates and spatial resolutions, failing to adapt to the content dynamics of the video and efficiently utilize computational resources.
Innovation Solution
Implementing a scene representation neural network that encodes video frames between key frames, allowing for dynamic adjustment of frame rates and spatial resolutions based on content analysis, including gaze direction and temporal detail, to generate high-resolution images at specific parts of the frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video is decoded at fixed frame rate and spatial resolution, then the decoding process is simple and efficient, but the system cannot adapt to content dynamics and cannot optimize computational resources
Solution Approach 1:
The patent implements dynamic frame rate and spatial resolution adjustment during video decoding. The system analyzes video content characteristics in real-time and dynamically modifies decoding parameters (frame rate and resolution) to match content complexity, enabling the system to adapt from static fixed parameters to dynamic content-aware parameters.
Solution Approach 2:
The system changes decoding parameters (frame rate and spatial resolution) based on video content analysis. By monitoring content dynamics and computational resource availability, the system adjusts these parameters to optimize the balance between decoding quality and computational efficiency.
2Manufacturing precision
If high frame rate and high spatial resolution are used for all video segments, then video quality is maximized, but computational resources are wasted on static or low-detail content
Solution Approach 1:
The system applies different decoding qualities to different video segments based on their content characteristics. High frame rate and high spatial resolution are applied only to segments with dynamic content or when computational resources are abundant, while lower quality decoding is used for static or low-detail segments, optimizing the quality-resource trade-off.
Solution Approach 2:
Instead of applying maximum decoding quality uniformly across all video content, the system applies partial quality adjustment based on content needs. This avoids excessive computational expenditure on segments that do not require high quality, while still providing adequate quality where needed.
3Productivity
If variable frame rate and spatial resolution are implemented, then computational resources are optimized and content adaptation is improved, but the decoding system becomes more complex
Solution Approach 1:
The system incorporates feedback mechanisms that monitor video content characteristics and computational resource usage in real-time. This feedback information is used to dynamically adjust frame rate and spatial resolution parameters, creating a closed-loop system that optimizes computational efficiency while managing complexity through automated control.
Data Source
AI summary
Systems and methods for encoding video, and for decoding video at an arbitrary temporal and/or spatial resolution. The techniques use a scene representation neural network that, in implementations, is configured to represent frames of a 2D or 3D video as a 3D model encoded in the parameters of the neural network.