Projection-Based 360-Degree Image Decoding for Compression Efficiency
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Solution Overview
Problem
Existing image processing systems struggle with the massive data generated for 360-degree images in virtual and augmented reality, requiring improved performance in image encoding and decoding, particularly for 360-degree images.
Innovation Solution
A method for decoding 360-degree images involves generating a predicted image using syntax information, combining it with a residual image, and reconstructing the image in a specific projection format, utilizing projection formats like Equi-Rectangular, CubeMap, OctaHedron, and IcoSahedral, and performing image expansion based on partitioning units to enhance compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional image encoding/decoding methods are used for 360-degree images, then the processing can be performed with standard algorithms, but the performance is insufficient for handling the massive data volume generated by multi-camera systems
Solution Approach 1:
The patent applies segmentation by dividing the 360-degree image into multiple projection formats (Equi-Rectangular, CubeMap, OctaHedron, IcoSahedral) and processing them separately. The image is also divided into blocks and partitioning units for independent encoding and decoding operations, enabling parallel processing and improved performance for large data volumes
Solution Approach 2:
The patent transforms the 360-degree spherical image into multiple 2D projection formats, adding a dimensional transformation step. This allows the system to process the immersive image data using conventional 2D image processing algorithms while maintaining the benefits of 360-degree coverage
2Measurement precision
If image expansion is performed on the entire reference picture, then the prediction accuracy is improved, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent divides the reference picture into multiple partitioning units and performs image expansion only on specific partitioning units that require it, rather than expanding the entire reference picture. This selective approach maintains prediction accuracy where needed while reducing overall computational complexity
Solution Approach 2:
The patent applies different processing qualities to different regions by performing image expansion selectively on specific partitioning units based on their prediction needs. Regions requiring high prediction accuracy receive expansion processing, while other regions use the original reference picture data
Data Source
AI summary
A method of decoding an image, includes obtaining at least one offset for a picture, deriving a variable for scaling for the picture based on the at least one offset, and performing inter prediction based on the variable for scaling for the picture. The at least one offset is defined with a direction of scaling.