Depth-Based Motion Estimation for Efficient Video Frame Interpolation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
High-quality video processing is resource-intensive and complex, making effective processing difficult due to the large amount of information and computing resource limitations.
Innovation Solution
Utilizing neural networks to generate intermediate frames by spatial upsampling and blending motion warped color frames with blending factors generated by a neural network, optimizing frame interpolation and downsampling processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high resolution video processing is performed to maintain video quality, then video quality is improved, but memory and computing resources are significantly consumed
Solution Approach 1:
The patent segments the video processing task into multiple frames and processes them independently using parallel computing operations. By dividing the complex high-resolution video processing into manageable frame-level segments, the system can maintain video quality while reducing the memory footprint and computing resource requirements for each individual processing step
Solution Approach 2:
The patent transitions from processing video in the spatial domain to processing in the frequency domain using transforms. This dimensional change allows for more efficient compression and processing of high-resolution video data, maintaining quality while significantly reducing the computational resources and memory needed for processing
2Measurement precision
If complex video processing operations are performed to handle multiple subjects and pixel changes, then video processing accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing video frames to identify and mark regions of interest containing multiple subjects or complex pixel changes before the main processing operation. This preliminary segmentation allows the system to apply optimized processing algorithms specifically to these complex regions, maintaining processing accuracy while significantly reducing the overall processing time by avoiding exhaustive analysis of entire frames
Solution Approach 2:
The patent employs dynamic processing strategies that adapt to the complexity of each video frame. By detecting the presence of multiple subjects and complex pixel changes in real-time, the system dynamically adjusts processing intensity and resource allocation, maintaining high processing accuracy for complex regions while using faster, simplified processing for simpler frames, thus reducing overall processing time
3Productivity
If video processing is performed quickly to meet real-time requirements, then processing speed is improved, but processing quality deteriorates
Solution Approach 1:
The patent implements periodic processing cycles that alternate between fast-preview processing and detailed quality processing. During the fast-preview phase, the system quickly processes frames to maintain real-time speed, identifying key regions of interest. During detailed processing phases, it applies higher-quality algorithms to these identified regions. This periodic alternation maintains overall processing speed while ensuring quality is preserved for critical frames and regions
Solution Approach 2:
The patent applies local quality enhancement by processing different regions of video frames with different quality levels. Critical regions containing multiple subjects or complex motions receive full-quality processing, while simpler regions use faster, lower-resource processing. This localized approach maintains overall processing quality for important content while significantly improving processing speed by avoiding exhaustive processing of all frame regions
Data Source
AI summary
Apparatuses, systems, and techniques to process image frames. In at least one embodiment, motion information of one or more pixels in one or more video frames is generated based, at least in part, on depth information of the one or more pixels.