360-Degree Image Encoding Across Multiple Projection Formats
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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 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 various projection formats, such as Equi-Rectangular, CubeMap, OctaHedron, and IcoSahedral, while optimizing compression performance through image expansion and motion vector prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If 360-degree images are processed using conventional image encoding methods, then the images can be captured and stored, but the data volume becomes excessively large and processing performance is insufficient
Solution Approach 1:
The 360-degree image is divided into multiple projection formats (Equi-Rectangular, CubeMap, OctaHedron, IcoSahedral), allowing selective processing and encoding of different regions according to their characteristics, thereby reducing overall data volume while maintaining processing efficiency
Solution Approach 2:
The patent applies different quantization parameters and transformation methods based on the projection format and region characteristics. By dynamically adjusting encoding parameters for different parts of the 360-degree image, the system achieves optimal compression ratios while preserving visual quality, effectively managing data volume without sacrificing processing performance
2Loss of energy
If image encoding is performed with high compression ratios, then data transmission efficiency improves, but image quality and reconstruction accuracy deteriorate
Solution Approach 1:
Different regions of the 360-degree image are encoded with different quality levels based on their importance and characteristics. Critical regions maintain higher reconstruction accuracy while less critical regions use higher compression, achieving overall transmission efficiency without uniform quality loss
Solution Approach 2:
The encoding system dynamically adjusts compression ratios and quality parameters based on the specific projection format and regional characteristics. This dynamic adaptation allows the system to optimize the balance between transmission efficiency and reconstruction accuracy for each specific encoding scenario
3Adaptability or versatility
If multiple projection formats are supported for 360-degree images, then versatility and adaptability improve, but system complexity increases
Solution Approach 1:
The encoding apparatus is designed with multi-functional capabilities to handle multiple projection formats (Equi-Rectangular, CubeMap, OctaHedron, IcoSahedral) within a single unified system. This universal design allows the same apparatus to adapt to different format requirements without requiring separate specialized systems, managing complexity while maintaining versatility
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.