360-Degree Image Decoding with Adaptive MPM Reconfiguration
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Solution Overview
Problem
Existing image processing systems struggle with the massive data requirements of 360-degree images for virtual and augmented reality, necessitating 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 image in various projection formats, 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 data volume becomes excessively large and processing performance is insufficient
Solution Approach 1:
The patent divides the 360-degree image into multiple projection formats (e.g., equirectangular projection, cube map projection, octahedron projection) and encodes each segment separately. This segmentation allows the system to process and compress different regions with appropriate methods, reducing overall data volume while maintaining processing performance
Solution Approach 2:
The patent transforms the 360-degree spherical image into multiple 2D projection formats, adding a dimensional transformation step. This dimensionality change enables conventional 2D encoding methods to be applied effectively, reducing data volume while preserving the ability to process the full 360-degree content
2Manufacturing precision
If 360-degree images are processed with high resolution for virtual reality, then image quality is improved, but the amount of data generated increases massively
Solution Approach 1:
The patent applies different encoding precision and compression methods to different regions of the 360-degree image based on their importance. High-resolution encoding is applied to critical regions (e.g., front view, user-facing areas) while lower resolution or higher compression is applied to less critical regions (e.g., periphery, back view), maintaining overall image quality while reducing total data volume
Solution Approach 2:
The patent dynamically adjusts encoding parameters such as resolution, bit depth, and compression ratio based on the specific region and its importance for virtual reality applications. This allows the system to optimize the balance between image quality and data volume by changing encoding parameters adaptively across different parts of the 360-degree image
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.