360-Degree Image Encoding and Decoding Through Projection Segmentation
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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 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, with 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 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 high-resolution 360-degree images
Solution Approach 1:
The 360-degree image is divided into multiple projection faces (e.g., cube map faces) that can be processed independently. Each face is encoded and decoded separately, allowing parallel processing and reducing the computational burden on any single processing unit. This segmentation enables the system to handle large data volumes by distributing the workload across multiple processing channels.
Solution Approach 2:
The patent transforms the 360-degree spherical image into multiple 2D projection planes (e.g., cube map projection). This dimensional transformation allows standard 2D image processing algorithms to be applied to each projection face, improving processing efficiency while maintaining the ability to reconstruct the full 360-degree view. The dimensionality change enables compatibility with existing high-performance 2D encoding/decoding systems.
2Manufacturing precision
If high-resolution images are processed to maintain image quality, then the visual fidelity is improved, but the data volume and processing requirements increase significantly
Solution Approach 1:
The patent applies different processing qualities to different regions of the 360-degree image based on their importance. High-resolution encoding is applied to central viewing regions where image quality is most critical, while peripheral regions use lower resolution. This local quality differentiation maintains visual fidelity in important areas while reducing overall data volume.
Solution Approach 2:
The patent dynamically adjusts encoding parameters such as quantization step size, transformation block size, and prediction mode selection based on the importance of different image regions. By changing these parameters locally, the system optimizes the balance between image quality and data compression ratio, maintaining high quality where needed while reducing data volume in less critical areas.
3Adaptability or versatility
If multiple projection formats are supported for different applications, then the adaptability is improved, but the device complexity increases
Solution Approach 1:
The patent designs a universal encoding/decoding framework that can handle multiple projection formats (e.g., equirectangular, cube map, octahedron) through a common processing architecture. The system uses format identification and appropriate transformation matrices to convert between different projection types, allowing a single device to support multiple formats without requiring separate dedicated processing paths 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.