360-Degree Image Decoding with Scaling Offsets and Region Prediction
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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 encoding and decoding methods.
Innovation Solution
A method for encoding and decoding 360-degree images that includes generating a predicted image using syntax information, combining it with a residual image, and reconstructing the image based on projection formats like ERP, CMP, and ISP, with image expansion and motion vector generation 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 generated by multi-view images
Solution Approach 1:
The patent divides the 360-degree image processing into multiple projection formats (ERP, CMP, OHP, ISP) and processes different regions according to their specific characteristics. The image is segmented into face regions, boundary regions, and transition regions, each handled with appropriate encoding strategies to improve overall processing performance while managing large data volumes.
Solution Approach 2:
Different encoding parameters and prediction methods are applied to different regions of the 360-degree image. Face regions use standard prediction modes, while boundary and transition regions employ specialized handling. This local optimization improves compression efficiency without uniformly increasing complexity across the entire image.
2Adaptability or versatility
If multiple projection formats are supported for 360-degree images, then the adaptability increases for different VR/AR applications, but the device complexity increases
Solution Approach 1:
The patent designs a universal decoding framework that can handle multiple projection formats (ERP, CMP, OHP, ISP) through a common architecture. The system uses format identification flags and standardized parameter sets that allow the same core processing pipeline to adapt to different projection types, reducing overall system complexity while maintaining broad compatibility.
Solution Approach 2:
Different projection formats are handled by changing specific parameters (projection type flags, coordinate transformation matrices, region definition parameters) rather than requiring fundamentally different processing algorithms. This parameter-based approach allows flexible format support with minimal increase in device complexity.
3Measurement precision
If image expansion is performed on reference pictures for prediction, then the prediction accuracy improves, but the processing time and computational load increase
Solution Approach 1:
Image expansion is performed selectively only in regions where it provides benefit (boundary regions and transition regions between faces) rather than uniformly across the entire reference picture. This partial application of expansion reduces computational load while maintaining prediction accuracy where it matters most.
Solution Approach 2:
The patent applies different prediction strategies to different regions: standard prediction for face regions and expanded prediction for boundary/transition regions. This localized approach improves overall prediction accuracy without incurring the full computational cost of universal expansion.
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.