Face-Aware Spatially Weighted Image Encoding for Panoramic Distortion
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Solution Overview
Problem
Existing encoding and decoding technologies are poorly suited for non-uniform image capture, leading to visual distortions and increased processing burden, especially in non-traditional content like spherical or panoramic images.
Innovation Solution
Spatially weight encoding quality parameters based on the underlying quality of visual content, adjusting fidelity of reproduction to account for non-uniform elements and characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional uniform encoding quality parameters are used for non-uniform image capture, then encoding simplicity is maintained, but visual distortion and processing burden increase
Solution Approach 1:
The patent applies local quality by dividing the image space into multiple regions and assigning different encoding quality parameters to each region based on its spatial characteristics. This allows high-quality encoding for important regions (like the center of the image) and lower-quality encoding for less important regions (like the periphery), thereby reducing visual distortion in critical areas while maintaining encoding efficiency.
2Device complexity
If traditional uniform encoding quality parameters are used for non-uniform image capture, then device complexity is minimized, but processing burden increases
Solution Approach 1:
The patent segments the image space into multiple regions with distinct encoding characteristics. By processing different regions with appropriate quality parameters, the system reduces the overall processing burden compared to uniformly high-quality encoding, while still maintaining acceptable visual quality in critical areas.
3Quantity of substance
If traditional encoding methods are used for spherical or panoramic content, then bandwidth consumption is reduced through uniform compression, but rendering accuracy deteriorates
Solution Approach 1:
The patent applies different encoding quality parameters to different spatial regions of spherical or panoramic content. Regions that are more likely to be viewed (such as the center of the display area) are encoded with higher quality, while peripheral regions use lower quality parameters. This local differentiation reduces overall bandwidth consumption while maintaining rendering accuracy where it matters most.
Data Source
AI summary
Visual content that includes spatial portions is obtained. A determination is made that one of the spatial portions includes a face. Based on the determination, encoding quality parameters for the one of the spatial portions is identified. The encoding quality parameters are obtained by combining a first distortion model related to the obtaining the visual content with a second model that emphasizes the one of the spatial portions The visual content is encoded. The encoding quality parameters are stored, in association with but separate from, the one of the spatial portions. After decoding, the one of the spatial portions are rendered based on the encoding quality parameters. The encoding quality parameters are obtained by combining a first distortion model related to the obtaining the visual content with a second model that emphasizes the one of the spatial portions.