Video Encoding Parameter Selection for Field of View Analysis
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Solution Overview
Problem
Existing home security systems with audio/video devices face inefficiencies in video encoding due to pre-configured parameters that do not account for the unique field of view characteristics of each camera device, leading to suboptimal video quality and resource usage.
Innovation Solution
A remote system analyzes video data from camera devices to identify stationary and moving objects within their field of view, using image segmentation and computer-vision techniques to select customized encoding parameters such as quantization and motion vector parameters, optimizing video encoding for better quality and resource conservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pre-configured encoding parameters are used for all camera devices, then device complexity is reduced and ease of operation is improved, but video encoding quality deteriorates due to inability to account for unique field of view characteristics
Solution Approach 1:
The system performs self-service by automatically analyzing its own field of view characteristics and selecting appropriate encoding parameters without requiring manual configuration. The camera device or remote system analyzes video data to identify stationary and moving objects, then autonomously determines optimal quantization and motion vector parameters based on the detected scene characteristics.
Solution Approach 2:
The system dynamically changes encoding parameters (quantization parameters and motion vector parameters) based on the analyzed field of view characteristics. Different parameter sets are selected according to the types of objects detected in the scene, allowing the encoding parameters to adapt to the specific viewing conditions rather than using fixed pre-configured values.
2Manufacturing precision
If customized encoding parameters are selected for each camera device based on field of view analysis, then video encoding quality is improved, but device complexity and computing resource usage increase
Solution Approach 1:
An intermediary system (remote system or processing module) is introduced to perform the complex field of view analysis and parameter selection tasks. This intermediary analyzes video data to identify object types and determines optimal encoding parameters, then provides these parameters to the camera device. This approach transfers the computational complexity from the camera device to a dedicated processing system.
Solution Approach 2:
The system performs preliminary analysis of the field of view characteristics before actual video encoding takes place. By analyzing video data to identify stationary and moving objects and determining optimal encoding parameters in advance, the system prepares the encoding configuration beforehand, which simplifies the actual encoding process and reduces real-time computational requirements.
3Loss of energy
If pre-configured encoding parameters are used, then bandwidth usage and computing resources are conserved, but video quality deteriorates due to suboptimal encoding for specific field of view conditions
Solution Approach 1:
The system changes encoding parameters dynamically based on the detected field of view characteristics. By identifying stationary and moving objects in the scene, the system selects appropriate quantization and motion vector parameters that are optimized for the specific scene type, achieving better video quality at comparable or reduced bandwidth usage compared to generic pre-configured parameters.
Solution Approach 2:
The system applies different encoding parameters to different regions or types of content within the video stream based on local field of view characteristics. By segmenting the analysis according to object types (stationary vs. moving objects) and applying针对性 encoding parameters to different scene elements, the system achieves optimized quality for each region while managing overall bandwidth usage efficiently.
Data Source
AI summary
This disclosure describes, in part, techniques for selecting encoding parameters for an electronic device. For instance, remote system(s) may receive, from the electronic device, video data representing a video. The remote system(s) may then analyze the video data to identify portions of the video that represented different areas located within the field of view (FOV) of the electronic device. The areas may include static areas, which do not include recurring motion, and/or dynamic areas, which include recurring motion. Additionally, the remote system(s) may analyze the video data to select encoding parameters for encoding the video data and/or for encoding the portions of the video data that represent the identified areas. The electronic device may then receive the encoding parameters from the remote system(s) and use the encoding parameters to encode additional video data that is generated by the electronic device.