360-Degree Image Decoding with Projection-Aware Prediction Modes
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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 various projection formats, such as Equi-Rectangular, CubeMap, OctaHedron, and IcoSahedral, while performing image expansion and intra-prediction based on partitioning units.
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 360-degree image is divided into multiple projection formats (ERP, CMP, OHP, ISP) and processed separately. The encoding apparatus performs image expansion and intra-prediction based on partitioning units specific to each projection format, allowing parallel processing of different image representations to handle large data volumes efficiently
Solution Approach 2:
The patent transforms the processing approach by introducing projection format as an additional dimension. Instead of processing all 360-degree images uniformly, the system converts images into different projection formats (2D plane, cube, octahedron, polyhedron) and applies format-specific encoding/decoding methods, thereby managing the complexity of massive data through dimensional transformation
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 applies different processing strategies to different regions and projection formats based on their specific characteristics. Each projection format (ERP, CMP, OHP, ISP) receives tailored image expansion and intra-prediction processing suited to its geometric properties, allowing efficient compression of high-resolution images by adapting the processing quality to local format requirements
Solution Approach 2:
The system changes processing parameters according to projection format type. The encoding apparatus adjusts image expansion methods, partitioning unit sizes, and intra-prediction modes based on the specific projection format being processed, enabling efficient handling of high-resolution images across multiple formats without uniform over-processing
3Adaptability or versatility
If multiple projection formats are supported, then the versatility of the system is improved, but the complexity of the processing system increases
Solution Approach 1:
The encoding and decoding apparatus are designed with multi-functional capabilities to handle multiple projection formats (ERP, CMP, OHP, ISP) within a single system architecture. The same apparatus can switch between different projection formats and apply appropriate processing methods, reducing the need for separate dedicated systems for each format while maintaining versatility
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.