Video Encoder ROI Segmentation for Power Efficiency
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Solution Overview
Problem
Current video encoding technologies face challenges in efficiently compressing high-quality video data, particularly in battery-powered mobile devices, where power consumption and compression efficiency are critical, especially when handling high-definition and ultra-HD videos.
Innovation Solution
The method involves dividing an input picture into a region of interest (ROI) and a non-region of interest (non-ROI), down-scaling the non-ROI picture, and determining distinct quantization parameters for each to efficiently encode the video data, allowing for adaptive partial encoding that prioritizes the ROI with lower loss and non-ROI with higher loss, thereby optimizing compression efficiency and power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If video data is encoded with high quality to meet increasing demands for HD and UHD videos, then video quality is improved, but power consumption increases and compression efficiency deteriorates
Solution Approach 1:
The patent applies local quality by differentiating encoding quality across different spatial regions of the video picture. The picture is divided into ROI and non-ROI regions, with ROI encoded at higher quality (lower quantization parameter) and non-ROI at lower quality (higher quantization parameter). This allows the system to maintain high video quality in important areas while reducing overall power consumption and improving compression efficiency by accepting lower quality in less important areas.
2Manufacturing precision
If uniform high quality encoding is applied to the entire picture, then video quality is improved, but compression efficiency deteriorates due to increased data size
Solution Approach 1:
The patent implements local quality by assigning different quantization parameters to different regions. ROI regions use lower quantization parameters (higher quality) while non-ROI regions use higher quantization parameters (lower quality). This differential encoding approach maintains high video quality in important regions while significantly improving compression efficiency by reducing the data size required for less important regions, thereby resolving the contradiction between quality and compression efficiency.
3Manufacturing precision
If the entire picture is processed at full resolution, then video quality is improved, but processing complexity and power consumption increase
Solution Approach 1:
The patent applies segmentation by dividing the picture into multiple regions (ROI and non-ROI) with different processing requirements. The non-ROI regions are down-scaled to lower resolution before encoding, while ROI regions maintain full resolution. This segmentation reduces the overall processing complexity and power consumption by performing fewer calculations on the down-scaled non-ROI regions, while still maintaining high video quality in the important ROI regions.
Data Source
AI summary
In a method of encoding video data, an input picture is divided into a first picture corresponding to a region of interest (ROI) and a second picture corresponding to a non-region of interest (non-ROI). A third picture is generated by down-scaling the second picture. A quantization parameter for the ROI is determined based on the first picture. A quantization parameter for the non-ROI is determined based on the third picture. A compressed picture is generated by encoding the input picture based on the quantization parameter for the ROI and the quantization parameter for the non-ROI.