360-Degree Image Decoding With Adaptive 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 high-resolution and high-quality 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 a 360-degree image format, including projection formats like Equi-Rectangular, CubeMap, and OctaHedron, with image expansion and intra-prediction techniques.
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 the massive data volume of high-resolution 360-degree 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 strategies. The image is segmented into partitioning units with boundary pixels that are processed differently to handle the large data volume efficiently.
Solution Approach 2:
The patent applies different prediction modes and image expansion techniques to different regions of the 360-degree image based on their characteristics. Boundary pixels of partitioning units are processed with special attention to maintain image quality while improving processing performance.
2Manufacturing precision
If image expansion is performed on all partitioning units, then the predicted image quality improves, but the processing time and computational complexity increase
Solution Approach 1:
The patent performs image expansion selectively on boundary pixels of partitioning units rather than on all pixels. This partial action approach maintains the necessary image quality for accurate prediction while significantly reducing the computational complexity and processing time.
Solution Approach 2:
The patent performs image expansion on boundary pixels in advance before the main prediction process. This preliminary action ensures that high-quality reference pixels are available for prediction while separating the computationally intensive expansion operation from the real-time prediction requirement.
3Adaptability or versatility
If multiple projection formats are supported for 360-degree images, then the adaptability to different VR/AR applications improves, but the device complexity increases
Solution Approach 1:
The patent designs a universal decoding framework that can handle multiple projection formats (ERP, CMP, OHP, ISP) using the same basic architecture. The system selects the appropriate projection format based on the input bitstream, allowing one system to serve multiple VR/AR application requirements without requiring separate processing chains 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.