Dynamic Spatial Metadata for Cropped Image Display Mapping
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Solution Overview
Problem
Existing image and video processing systems face challenges in accurately adapting content to different display devices due to the lack of effective dynamic spatial metadata, leading to potential artifacts and diminished user experience when images are cropped or zoomed.
Innovation Solution
Generating and applying dynamic spatial metadata by computing and smoothing metadata parameters at a lower resolution, down-sampling, and using region of interest (ROI) metadata to enhance display mapping and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dynamic spatial metadata is computed at full input spatial resolution, then measurement precision of metadata parameters is improved, but computational load and processing time increase
Solution Approach 1:
The patent divides the full-resolution image into multiple lower-resolution sub-images or regions, computes metadata parameters for each sub-image separately, and then combines them. This segmentation approach reduces the computational complexity for each individual metadata computation while maintaining overall precision through the aggregation of regional metadata.
Solution Approach 2:
The patent introduces a new dimension by computing metadata at multiple spatial resolutions simultaneously - both at the full input resolution and at reduced resolutions. This multi-resolution approach allows the system to leverage lower-resolution metadata for rapid processing while using full-resolution metadata where precision is critical, effectively resolving the contradiction between precision and speed.
2Adaptability or versatility
If dynamic spatial metadata is generated for the entire image, then comprehensive display adaptation is improved, but device complexity and processing overhead increase
Solution Approach 1:
The patent applies local quality by generating metadata specifically for regions of interest (ROI) rather than uniformly processing the entire image. By identifying and prioritizing important regions, the system achieves effective display adaptation for critical areas while reducing the overall processing burden and complexity.
Solution Approach 2:
The patent implements partial action by selectively processing only certain regions of the image at full metadata detail, while using simplified or lower-resolution metadata for other regions. This approach maintains adequate display adaptation capability for the most important areas while reducing overall system complexity through selective processing.
3Productivity
If metadata parameters are smoothed and down-sampled, then computational load is reduced and processing efficiency is improved, but measurement precision of spatial metadata deteriorates
Solution Approach 1:
The patent applies dynamics by making the metadata resolution adaptive rather than static. The system dynamically selects the appropriate metadata resolution and smoothing level based on the specific processing requirements, region importance, and display characteristics. This allows the system to optimize the balance between precision and efficiency for each particular case rather than using a fixed approach.
Solution Approach 2:
The patent changes parameters by computing metadata at multiple spatial resolutions and selectively applying different levels of smoothing and down-sampling based on the specific processing needs. This multi-parameter approach allows the system to adjust the precision-efficiency trade-off dynamically, using higher precision where needed and lower precision where acceptable, thereby resolving the contradiction.
Data Source
AI summary
Methods and systems for generating and using dynamic spatial metadata in image and video processing are described. In an encoder, in addition to global metadata, local, spatial metadata for two or more image regions or image objects are generated, smoothed, and embedded as spatial metadata values. In a decoder, the decoder can reconstruct the spatial metadata and use interpolation techniques to generate metadata for specific regions of interest. Examples of generating spatial metadata related to min, mid, and max luminance values in an image are provided.