360-Degree Image Decoding with Scaled Inter 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, necessitating improved performance in image encoding and decoding, particularly for high-resolution and high-quality images.
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 in specific projection formats, utilizing image expansion based on partitioning units and motion vector candidates to enhance compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional image processing methods are used for 360-degree images, then the system can handle basic encoding and decoding, but the performance is insufficient when processing large amounts of high-resolution data
Solution Approach 1:
The patent divides the 360-degree image into multiple projection formats (ERP, CMP, OHP, ISP) and processes each format separately with dedicated decoding paths. This segmentation allows the system to handle large data amounts efficiently by processing smaller chunks in parallel rather than treating the entire 360-degree image as a single unit.
Solution Approach 2:
The patent transforms the 360-degree spherical image data into multiple 2D projection formats, changing the dimensional representation. This allows conventional 2D image processing systems to handle 360-degree content by converting it into manageable 2D projections, effectively adding a projection dimension to the processing approach.
2Manufacturing precision
If high-resolution 360-degree images are processed, then image quality improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies different processing strategies to different projection formats based on their specific characteristics. For example, ERP format uses different reconstruction approaches compared to CMP or OHP formats. This local quality approach optimizes processing for each format's specific requirements, maintaining high image quality without uniformly increasing complexity across all processing paths.
Solution Approach 2:
The patent performs preliminary organization of syntax information and projection format identification before the actual decoding process. By preparing and categorizing the incoming bitstream information in advance, the system reduces the complexity of the main decoding operation, allowing high-resolution processing without proportionally increasing overall system complexity.
3Adaptability or versatility
If multiple projection formats are supported, then versatility improves, but the system complexity increases
Solution Approach 1:
The patent creates a universal decoding framework that can handle multiple projection formats (ERP, CMP, OHP, ISP) through a common architecture. The syntax information structure and decoding pipeline are designed to be format-agnostic, with format-specific parameters that can be configured based on the input. This allows one system to serve multiple functions without requiring separate decoding paths for each format.
Solution Approach 2:
The patent manages complexity by using parameter-based configuration rather than structural changes. Each projection format is handled by changing specific parameters (projection type indicators, syntax information structures) within a unified decoding framework, rather than implementing completely separate processing systems for each format.
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.