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 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 methods are used for 360-degree images, then the encoding process is simple, but the compression performance is insufficient for massive data volumes
Solution Approach 1:
The patent divides the 360-degree image into multiple projection formats (ERP, CMP, OHP, ISP) and processes different regions using appropriate projection formats. The image is segmented into face regions, boundary regions, and transition regions, each handled with specific encoding strategies to optimize compression while managing complexity.
Solution Approach 2:
The patent transforms the 360-degree spherical image into multiple 2D projection formats (Equi-Rectangular, CubeMap, OctaHedron, IcoSahedral). This dimensional transformation allows conventional 2D encoding methods to be applied while achieving better compression for the massive data volumes of 360-degree images.
2Reliability
If image processing is performed on the entire 360-degree image, then comprehensive processing is achieved, but the processing time and computational load increase significantly
Solution Approach 1:
The patent applies different processing qualities and methods to different regions of the 360-degree image. Face regions receive full processing, boundary regions use simplified processing, and transition regions use intermediate processing. This local quality approach maintains overall image quality while reducing total processing time and computational load.
Solution Approach 2:
The patent performs comprehensive processing only on critical face regions while using simplified or skipped processing for less critical boundary and transition regions. This partial action approach ensures essential image quality is maintained while significantly reducing processing time for the entire 360-degree image.
3Adaptability or versatility
If multiple projection formats are supported, then versatility is improved, but system complexity increases
Solution Approach 1:
The patent designs the encoding system to handle multiple projection formats (ERP, CMP, OHP, ISP) using a unified processing framework. The same encoding pipeline can process different projection formats by selecting appropriate processing parameters, achieving versatility without proportionally increasing system complexity.
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.