360-Degree Image Decoding with MPM Reconfiguration and Projection Formats
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Solution Overview
Problem
Existing image processing systems struggle with the massive data generated from processing multi-view 360-degree images for virtual and augmented reality, leading to insufficient performance in encoding and decoding high-resolution 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 specific projection formats like ERP, CMP, OHP, and ISP, with image expansion and intra-prediction techniques 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 massive data volumes from multi-view images
Solution Approach 1:
The patent divides the 360-degree image into multiple projection formats (ERP, CMP, OHP, ISP) and processes different regions using different prediction methods. The image is segmented into face regions, boundary regions, and transition regions, each handled with appropriate encoding strategies to manage the massive data volume efficiently.
Solution Approach 2:
Different prediction methods are applied to different regions of the 360-degree image based on their local characteristics. Intra-prediction is used for face regions with high image continuity, while other methods are applied to boundary and transition regions, optimizing compression performance for each local area.
2Manufacturing precision
If high-resolution 360-degree images are processed, then the image quality is improved, but the data amount increases massively making processing difficult
Solution Approach 1:
The patent changes the projection format parameters to transform the 360-degree image into different 2D representations (ERP, CMP, OHP, ISP). This parameter transformation allows high-resolution images to be processed in formats optimized for specific regions, reducing the overall data amount while maintaining quality.
Solution Approach 2:
The patent converts the 360-degree spherical image into multiple 2D planar projections, changing the dimensional representation. This dimensionality change allows the massive 3D data to be processed as multiple manageable 2D images with reduced data requirements.
3Measurement precision
If image expansion is performed on reference pictures, then the prediction accuracy is improved, but the processing complexity increases
Solution Approach 1:
The patent performs image expansion on reference pictures before the prediction process. By pre-expanding the reference pictures to match the current picture dimensions, the prediction accuracy is improved without adding complexity during the main encoding/decoding process.
Data Source
AI summary
A method for decoding a 360-degree image includes: receiving a bitstream obtained by encoding a 360-degree image; generating a prediction image by making reference to syntax information obtained from the received bitstream; combining the generated prediction image with a residual image obtained by dequantizing and inverse-transforming the bitstream, so as to obtain a decoded image; and reconstructing the decoded image into a 360-degree image according to a projection format. Here, generating the prediction image includes: checking, from the syntax information, prediction mode accuracy for a current block to be decoded; determining whether the checked prediction mode accuracy corresponds to most probable mode (MPM) information obtained from the syntax information; and when the checked prediction mode accuracy does not correspond to the MPM information, reconfiguring the MPM information according to the prediction mode accuracy for the current block.