Block Partition Decoding for 360-Degree Image Compression
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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, necessitating 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 specific projection format, utilizing image expansion based on partitioning units and motion information 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 generated by 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, thereby improving overall processing performance for large data volumes.
Solution Approach 2:
The patent transforms the 360-degree spherical image into multiple 2D projection planes (cube map, equirectangular, etc.). This dimensional transformation allows standard 2D image processing algorithms to be applied efficiently to each projection face, significantly improving processing performance while maintaining the ability to handle the complete 360-degree content.
2Productivity
If 360-degree images are processed using standard projection formats, then compatibility is maintained, but compression performance is insufficient
Solution Approach 1:
Different projection formats and encoding parameters are applied to different regions or faces of the 360-degree image based on their specific characteristics. For example, cube map faces with high texture detail use different compression settings than faces with smooth regions, optimizing compression performance while adapting to local image properties.
Solution Approach 2:
The patent employs multiple projection formats (cube map, equirectangular, octahedron, icosahedron) and allows dynamic selection and adjustment of projection parameters to optimize compression performance. By changing the projection parameters adaptively, the system achieves better compression ratios while managing encoding complexity through standardized format options.
Data Source
AI summary
Disclosed are methods and apparatuses for image data encoding/decoding. A method of decoding an image includes receiving a bitstream in which the image is encoded; obtaining index information for specifying a block division type of a current block in the image; and determining the block division type of the current block from a candidate group pre-defined in the decoding apparatus. The candidate group includes a plurality of candidate division types, including at least one of a non-division, a first quad-division, a second quad-division, a binary-division or a triple-division. The method also includes dividing the current block into a plurality of sub-blocks; and decoding each of the sub-blocks with reference to syntax information obtained from the bitstream.