Inter Prediction Scaling for 360-Degree Image Decoding
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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 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 360-degree images are processed using conventional image processing systems, then the processing can be performed with standard methods, but the performance is insufficient due to the massive amount of data generated
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. This allows the massive data to be handled in manageable segments, improving processing performance while maintaining comprehensive coverage of the spherical image data
Solution Approach 2:
The patent transforms the 360-degree spherical image data into multiple 2D projection formats, changing the dimensional representation from a spherical coordinate system to planar projections. This dimensionality change enables conventional 2D image processing systems to handle the data more efficiently, improving productivity without requiring specialized spherical processing hardware
2Loss of energy
If image encoding and decoding is performed on 360-degree images without optimization, then the process is simple, but the compression performance is insufficient for large data volumes
Solution Approach 1:
The encoding and decoding process is segmented into multiple stages: generating predicted images for each projection format, calculating residuals, and separately encoding/decoding the predicted images and residual images. This segmentation improves compression efficiency by allowing optimized processing for each projection type while managing complexity through modular, step-by-step operations
Solution Approach 2:
The patent performs preliminary actions by generating predicted images before the actual encoding process. These predicted images serve as references that simplify the subsequent encoding of residual differences, improving compression efficiency by encoding only the differences rather than the full image 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.