360-Degree Image Decoding with Scaling Offsets and Partitioned Prediction
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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 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 a specific projection format, utilizing image expansion based on partitioning units and motion vector candidates to enhance compression performance.
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 faces (e.g., cube map faces) and further segmented into partitioning units. This segmentation allows independent encoding and processing of different regions, reducing the overall data volume while maintaining processing efficiency through parallel handling of segmented units.
Solution Approach 2:
The patent transforms the 360-degree spherical image into multiple 2D projection planes (cube map, equirectangular, etc.). This dimensional transformation from 3D spherical coordinates to 2D planar representations reduces data complexity and enables conventional 2D image processing techniques to be applied efficiently, improving processing performance while managing data volume.
2Measurement precision
If image expansion is performed on the reference picture to generate predicted images, then prediction accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
Image expansion is performed selectively based on the characteristics of each partitioning unit. Rather than uniformly expanding all reference pictures, the method applies expansion operations only to specific regions or units where it provides significant prediction improvement, thereby maintaining prediction accuracy while reducing overall processing time and computational complexity.
3Adaptability or versatility
If multiple projection formats (ERP, CMP, OHP, ISP) are supported for 360-degree images, then versatility and adaptability improve, but the device complexity and processing overhead increase
Solution Approach 1:
The encoding apparatus is designed with a universal projection format identification mechanism that can recognize and handle multiple projection formats (ERP, CMP, OHP, ISP) through a unified processing framework. The system identifies the projection format from syntax information and applies appropriate decoding operations, enabling multi-format support without requiring separate dedicated processing paths for each format, thus managing device 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.