Post-Processing Video Re-Stabilization Using In-Camera Metadata
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Solution Overview
Problem
Existing Electronic Image Stabilization (EIS) techniques are limited by resource constraints in-camera, leading to inefficiencies in post-processing stabilization, particularly when combining in-camera and post-processing methods, which results in reduced designated views and increased processing complexity.
Innovation Solution
A camera-aware post-processing system that utilizes in-camera stabilization metadata to improve re-stabilization by identifying problematic frames and reducing processing complexity, leveraging sensor data and orientation information to reconstruct and enhance video stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If in-camera stabilization is used, then immediate sharing and reduced file sizes are achieved, but video quality is limited by camera device resources
Solution Approach 1:
The system performs preliminary in-camera stabilization during capture to enable immediate sharing, then applies post-processing re-stabilization afterward to enhance video quality. This two-stage approach allows the video to be stabilized and shared immediately while still permitting quality improvement later through more sophisticated processing.
Solution Approach 2:
The stabilization process is divided into two independent stages: in-camera stabilization during capture and post-processing re-stabilization afterward. Each stage can be optimized separately for its specific goals (speed/immediate sharing vs. quality), and the results are combined to achieve both objectives.
2Manufacturing precision
If post-processing stabilization is performed, then better video quality is achieved, but processing time increases
Solution Approach 1:
In-camera stabilization is performed as a preliminary action during capture, providing immediate stabilization results. This reduces the burden on post-processing, allowing quality enhancement without requiring complete re-stabilization from scratch, thereby reducing overall processing time.
Solution Approach 2:
The system uses feedback from the in-camera stabilization metadata and performance to guide the post-processing re-stabilization. By analyzing how well the in-camera stabilization performed, the post-processing stage can focus computational resources only where needed, optimizing the balance between quality improvement and processing time.
3Manufacturing precision
If combining in-camera and post-processing stabilization methods, then video quality is improved, but processing complexity increases
Solution Approach 1:
In-camera stabilization metadata serves as an intermediary between the two stabilization stages. This metadata contains information about camera motion and stabilization parameters that guides the post-processing re-stabilization, reducing its complexity by providing a head start rather than requiring complete independent analysis.
Solution Approach 2:
The system extracts and utilizes stabilization metadata from the in-camera process, separating the stabilization information from the full video processing pipeline. This extracted metadata is then used to simplify the post-processing stage by providing pre-computed motion compensation data.
Data Source
AI summary
Methods and apparatus for post-processing in-camera stabilized video. Embodiments of the present disclosure reconstruct and re-stabilize an in-camera stabilized video to provide for improved stabilization (e.g., a wider crop, etc.). In-camera sensor data may be stored and used to re-calculate orientation metadata in post-production. In-camera stabilization provides several benefits (e.g., the ability to share stabilized videos from the camera without additional post-processing as well as reduced file sizes of the shared videos). Camera-aware post-processing can reuse portions of the in-camera stabilized videos while providing additional benefits (e.g., the ability to regenerate the original captured videos in post-production and re-stabilize the videos). Camera-aware post-processing can also improve orientation metadata and remove sensor error. The disclosed techniques also enable assisted optical flow-based stabilization using the refined metadata.