Image Decoding With Scaling Offsets for 360-Degree Projections
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Solution Overview
Problem
Existing image processing systems struggle with the massive data generated by 360-degree images for virtual and augmented reality, requiring improved performance in encoding and decoding methods.
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, including Equi-Rectangular, CubeMap, OctaHedron, and IcoSahedral formats, with image expansion and motion vector prediction techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 360-degree images are captured with multiple cameras for virtual reality and augmented reality, then the realism and quality of media service are improved, but the amount of data generated increases massively
Solution Approach 1:
The patent divides the 360-degree image data into multiple projection formats (Equi-Rectangular Projection, CubeMap Projection, OctaHedron Projection, IcoSahedral Projection) and processes each format separately with optimized encoding schemes. This segmentation allows the system to handle the massive data volume by processing different portions of the spherical image data through specialized decoding paths, reducing the overall processing burden while maintaining realism.
Solution Approach 2:
The patent transforms the 360-degree spherical image data into multiple 2D projection formats, changing the dimensional representation of the data. By converting the spherical coordinate system into various 2D plane projections, the system can leverage traditional 2D image processing and compression techniques, effectively managing the data volume while preserving the immersive reality experience.
2Reliability
If the amount of data for 360-degree images increases massively, then the quality and coverage of virtual reality content are improved, but the performance of image processing systems becomes insufficient
Solution Approach 1:
The patent applies different decoding and processing strategies to different regions and formats of the 360-degree image data. Each projection format (ERP, CMP, OHP, ISP) receives tailored processing optimized for its specific characteristics, allowing the system to maintain high quality output while improving processing efficiency through localized optimization rather than uniform processing.
Solution Approach 2:
The patent performs preliminary organization and classification of the encoded bitstream data into different projection format categories before decoding. By pre-identifying and separating the different projection formats in the encoded data, the system can efficiently route each format to its appropriate decoding pipeline, improving overall processing performance and reducing system bottlenecks.
3Ease of manufacture
If traditional image encoding and decoding methods are used for 360-degree images, then the implementation is simple, but the compression performance and processing efficiency are insufficient
Solution Approach 1:
The patent creates a universal decoding framework that can handle multiple projection formats (Equi-Rectangular, CubeMap, OctaHedron, IcoSahedral) through a single integrated system. This multi-functional approach maintains implementation simplicity by providing a unified interface while internally optimizing compression performance for each specific format through specialized processing paths.
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.