360-Degree Image Encoding/Decoding With Adaptive MPM Prediction
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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, reconstructing the image according to projection formats like ERP, CMP, OHP, and ISP, and performing image expansion and intra-prediction based on partitioning units 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 large data volumes from multi-view images
Solution Approach 1:
The patent divides the 360-degree image processing into multiple projection formats (ERP, CMP, OHP, ISP) and processes different regions with different prediction methods. The image is segmented into partitioning units that can be independently processed, allowing parallel processing and improved throughput for large data volumes.
Solution Approach 2:
The patent applies different intra-prediction modes and image expansion techniques to different partitioning units based on their local characteristics. Syntax information is used to adaptively select prediction methods for each region, optimizing compression performance for locally varying image content while maintaining high processing speed.
2Loss of information
If image expansion and intra-prediction are performed for each partitioning unit, then compression performance is improved, but the complexity of the encoding/decoding process increases
Solution Approach 1:
The patent performs image expansion on reference pictures before the main encoding/decoding process. By pre-expanding the reference images to match the target projection format, the complexity during main processing is reduced, as the expansion operation needs to be performed only once rather than for each partitioning unit.
Solution Approach 2:
The patent uses syntax information from the bitstream to copy and reuse prediction modes and parameters across partitioning units with similar characteristics. This reduces the computational complexity by avoiding redundant calculations while maintaining compression performance.
3Adaptability or versatility
If multiple projection formats are supported, then the system becomes more versatile for different VR/AR applications, but the initial image setting process becomes more complex
Solution Approach 1:
The patent designs a universal decoding framework that can handle multiple projection formats (ERP, CMP, OHP, ISP) through a common architecture. The system uses format identification syntax information to route processing to the appropriate projection handler, allowing one system to serve multiple VR/AR application requirements 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.