Video Frame Bit Allocation by Region Complexity
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Solution Overview
Problem
Existing video transmission systems face challenges in efficiently allocating limited bandwidth to represent different portions of video frames, particularly in prioritizing regions of interest (ROI) while minimizing data transmission while maintaining video quality.
Innovation Solution
The system allocates more bits to regions of interest (ROI) based on their complexity and area ratio within the frame, using quantization parameters to encode ROI and non-ROI areas differently, and identifies skin pixels to determine facial regions for targeted bit allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If uniform quantization is applied to all regions of video frames, then encoding simplicity is maintained, but transmission efficiency deteriorates due to insufficient prioritization of important regions
Solution Approach 1:
The patent applies different quantization parameters to different regions of the video frame. Specifically, regions of interest (ROI) such as facial areas are assigned lower quantization parameters (higher quality, more bits) while non-ROI areas receive higher quantization parameters (lower quality, fewer bits). This local differentiation resolves the contradiction by maintaining encoding simplicity through automated region classification while improving transmission efficiency through selective bit allocation.
2Manufacturing precision
If more bits are allocated to regions of interest, then video quality in important areas is improved, but total bandwidth consumption increases
Solution Approach 1:
The patent dynamically changes the quantization parameter based on region importance. By identifying ROI areas (such as facial regions detected through skin tone analysis) and assigning them lower quantization parameters while assigning higher parameters to non-ROI areas, the system optimizes the distribution of bits. This parameter differentiation improves video quality in important regions while controlling overall bandwidth consumption by reducing bits allocated to less important areas.
3Measurement precision
If complex region analysis is performed to identify facial areas, then bit allocation accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent employs automated skin tone detection and facial region identification algorithms that operate independently without requiring manual intervention. The system automatically analyzes each video frame, identifies ROI areas based on color space characteristics (such as YUV chrominance values), and allocates bits accordingly. This self-service approach improves bit allocation accuracy while avoiding the added complexity of manual region specification or complex external processing systems.
Data Source
AI summary
A technique of encoding video frames allocates an available number of bits to different portions of the video frame. A processing unit identifies a region of interest (ROI) in a video frame, and computes a first and second complexity parameter respectively representing the change in video information in the ROI portions and non-ROI portions in the video frame relative to a reference frame. Bits are allocated to the ROI portion proportional (positive correlation) to the first complexity parameter and a ratio of the area of the ROI to the area of the frame. The remaining available bits are allocated to the non-ROI. In an embodiment, the bits are encoded according to H.264 standard.