360-Degree Image Decoding with Adaptive MPM Reconfiguration
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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 it into the 360-degree format, utilizing projection formats like Equi-Rectangular, CubeMap, OctaHedron, and IcoSahedral, with image expansion and intra-prediction techniques to enhance compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 360-degree images are captured with multiple cameras for virtual reality and augmented reality, then the realism and quality of the media service are improved, but the amount of data generated increases massively
Solution Approach 1:
The patent divides the 360-degree image data into multiple projection formats (Equi-Rectangular, CubeMap, OctaHedron, IcoSahedral) and processes each format separately with optimized encoding parameters. This segmentation allows the system to handle the massive data volume by breaking it into manageable chunks while maintaining the realism required for VR/AR applications
Solution Approach 2:
The patent transforms 360-degree spherical image data into multiple 2D projection formats, effectively converting a 3D spherical coordinate system into 2D planar representations. This dimensionality change reduces the complexity of processing and storage while preserving the visual realism needed for virtual and augmented reality experiences
2Adaptability or versatility
If conventional image encoding and decoding methods are used for 360-degree images, then the processing can be performed with existing systems, but the performance is insufficient for handling large data volumes
Solution Approach 1:
The patent creates a universal encoding and decoding framework that can handle multiple projection formats (Equi-Rectangular, CubeMap, OctaHedron, IcoSahedral) and various image resolutions within a single system. This multi-functional approach maintains compatibility with existing image processing systems while significantly improving processing performance through optimized algorithms tailored for 360-degree imagery
Solution Approach 2:
The patent optimizes encoding parameters such as block size, transformation types, and quantization settings specifically for 360-degree images in different projection formats. By adjusting these parameters according to the specific projection format and content characteristics, the system achieves higher processing efficiency and better compression ratios while maintaining compatibility with standard video coding frameworks
3Device complexity
If image data is processed without optimized projection formats, then the processing flow is simpler, but the compression performance is insufficient
Solution Approach 1:
The patent performs preliminary transformation of 360-degree images into optimized projection formats before the main encoding process. By pre-processing the image data into appropriate projection formats (Equi-Rectangular, CubeMap, OctaHedron, or IcoSahedral) based on content characteristics, the system improves compression efficiency while keeping the main encoding flow relatively simple and manageable
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.