360-Degree Image Decoding Using Projection-Aware MPM Prediction
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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, 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 and intra-prediction techniques to enhance compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional image encoding methods are used for 360-degree images, then the encoding process is simple, but the compression performance is insufficient for large data volumes
Solution Approach 1:
The patent divides the 360-degree image into multiple projection formats (ERP, CMP, OHP, ISP) and processes different regions with different encoding strategies. The image is segmented into face regions, edge regions, and corner regions, each handled with appropriate prediction and transformation methods to optimize compression while managing complexity.
Solution Approach 2:
The patent transforms the 360-degree image from spherical coordinate system to multiple 2D projection formats. This dimensional transformation allows conventional 2D encoding techniques to be applied to 360-degree content, improving compression performance while maintaining compatibility with existing decoders.
2Reliability
If image processing is performed on all 360-degree image data, then complete coverage is achieved, but the processing load increases massively
Solution Approach 1:
The patent applies different processing quality levels to different regions of the 360-degree image. Face regions (central viewing areas) are processed with higher quality and more detailed encoding, while edge and corner regions use lower quality settings. This regional differentiation maintains reliability for important areas while reducing overall data volume.
Solution Approach 2:
The patent focuses processing resources on the most important regions (face regions) rather than uniformly processing all areas. By applying partial action to critical areas and reduced action to less important areas, the system achieves acceptable completeness while significantly reducing processing load and data volume.
3Adaptability or versatility
If multiple projection formats are supported, then versatility is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal encoding framework that can handle multiple projection formats (ERP, CMP, OHP, ISP) through a common architecture. The encoder selects appropriate projection formats and transformation methods based on the input content, providing multi-functionality without requiring separate processing pipelines for each format.
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.