Tree-Structured Block Division for 360-Degree Image Coding
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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.
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 decoded image based on projection formats like ERP, CMP, OHP, and ISP, with image expansion and rearrangement based on partitioning units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multi-view images are captured with a plurality of cameras for 360-degree images, then the realism and quality of the image is improved, but the amount of data generated increases massively
Solution Approach 1:
The 360-degree image is divided into multiple projection faces (e.g., cube faces in CMP format), and each face is further divided into partitioning units. This segmentation allows independent encoding and processing of different regions, reducing the overall data processing burden while maintaining image quality.
Solution Approach 2:
The patent transforms the 360-degree spherical image into multiple 2D projection planes (ERP, CMP, OHP, ISP formats). This dimensional transformation enables standard 2D image processing techniques to be applied to 360-degree content, improving processing efficiency without sacrificing visual quality.
2Manufacturing precision
If the amount of data for 360-degree images increases massively, then the image quality and coverage are improved, but the performance of the image processing system becomes insufficient
Solution Approach 1:
Each projection face is divided into multiple partitioning units that can be independently processed. This allows parallel processing of different units, significantly improving processing throughput and system performance while handling large 360-degree image data.
Solution Approach 2:
The patent applies different processing strategies to different partitioning units based on their characteristics. Not all units require the same level of processing intensity, allowing the system to optimize performance by applying appropriate processing levels to each unit.
3Measurement precision
If image expansion is performed on partitioning units for prediction, then the prediction accuracy is improved, but the processing complexity increases
Solution Approach 1:
Image expansion is performed selectively on specific partitioning units based on their characteristics and prediction needs. Not all units undergo expansion, which reduces overall processing complexity while maintaining prediction accuracy where it matters most.
Solution Approach 2:
The processing complexity is distributed across multiple partitioning units rather than applied uniformly to the entire image. This segmentation allows the system to manage complexity by processing smaller, manageable units independently.
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.