Camera Dynamics Modeling for Video Coding Quality
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Solution Overview
Problem
Existing video coding systems fail to effectively manage scene dynamics and camera adjustments, leading to degraded video quality due to inherent flaws in current coding standards, especially in real-time video conferencing scenarios under constant bit rate conditions.
Innovation Solution
A method that involves receiving camera dynamic parameters, determining reference transform parameters, and applying them to generate and encode video images, using camera dynamics modeling to improve compression efficiency by mimicking environmental and camera adjustments through model-based transforms, which are transmitted and applied at the decoder side for better prediction and reduced prediction residuals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing video coding standards are used to encode video with camera dynamics, then coding simplicity is maintained, but video quality is significantly degraded due to inability to properly handle scene dynamics
Solution Approach 1:
The patent applies camera dynamics modeling to predict and compensate for scene changes before encoding. By pre-processing reference frames using modeled camera movements (pan, tilt, zoom, focus, exposure changes), the system prepares transformed reference data that anticipates dynamic scene variations, thereby improving video quality without requiring complex real-time handling of dynamics during encoding
Solution Approach 2:
The patent introduces camera dynamics modeling as an intermediary layer between the video camera and the encoder. This modeling component translates raw camera parameters into transform operations that bridge the gap between static coding standards and dynamic scene changes, enabling quality improvement without directly modifying the core encoding standard
2Manufacturing precision
If camera dynamics are properly modeled and compensated, then video quality improves, but processing complexity and computational requirements increase
Solution Approach 1:
The patent transforms complex camera dynamics into a limited set of parametric models (pan angle, tilt angle, zoom factor, focus distance, exposure changes). By representing diverse camera movements as changes in these fundamental parameters, the system achieves quality improvement through efficient parameter-based transformations rather than computationally intensive pixel-level processing
Solution Approach 2:
The patent applies different transform operations to different regions of the video frame based on local camera dynamics characteristics. By focusing computational resources on regions most affected by camera movements while simplifying processing in stable regions, the system reduces overall processing requirements while maintaining video quality
3Manufacturing precision
If more data is encoded to capture scene dynamics, then video quality improves, but bandwidth consumption and bit rate increase
Solution Approach 1:
The patent extracts and separately encodes camera dynamics parameters from the video content. By isolating the dynamic components (camera movement parameters) from the static content, the system can efficiently represent scene changes using compact parameter descriptions rather than encoding entire dynamic frames at full resolution, thereby reducing overall data quantity while preserving quality
Solution Approach 2:
The patent performs camera dynamics compensation in advance to generate transformed reference frames. By pre-processing these references to account for anticipated camera movements, the system reduces the amount of data needed during actual encoding, as the dynamic variations are already accounted for in the reference material rather than requiring additional bits to describe them
4Productivity
If real-time video coding is performed under constant bit rate, then bandwidth efficiency is maintained, but video quality degrades due to inability to adapt to scene dynamics
Solution Approach 1:
The patent introduces dynamic adaptation into the encoding process by continuously updating camera dynamics models based on incoming camera parameters. This allows the system to adapt its encoding strategy in real-time to scene changes while maintaining constant bit rate, improving video quality without sacrificing bandwidth efficiency through dynamic rate adjustment
Data Source
AI summary
A method is provided in one example embodiment and includes receiving a camera dynamic parameter; determining a reference transform parameter based on the camera dynamic parameter; applying the reference transform parameter to generate a video image; and encoding the reference transform parameter in a bitstream for transmission with the video image. In other more specific instances, the method may include decoding a particular video image; decoding a particular reference transform parameter; and applying a particular reference transform parameter to the particular video image. The entropy-decoded data can undergo inverse quantization and transformation such that reference transformed data is combined with the entropy-decoded data. Additionally, the entropy-decoded data can be subjected to filtering before decoded video images are rendered on a display.