360-Degree Image Decoding Across Projection Formats and MPM Prediction
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Solution Overview
Problem
Existing image processing systems struggle with the massive data generated from multi-view images for 360-degree images in 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 specific 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
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 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 modes. The image is segmented into face regions and non-face regions, with different intra-prediction modes applied to each segment, enabling efficient handling of large data volumes through localized processing strategies.
Solution Approach 2:
The patent applies different intra-prediction modes (planar, angular, vertical, horizontal) to different regions of the 360-degree image based on local characteristics. Face regions use specific prediction modes while non-face regions use others, optimizing compression performance for each local area rather than using a uniform approach across the entire image.
2Manufacturing precision
If high-resolution 360-degree images are processed, then the image quality improves, but the data amount increases massively
Solution Approach 1:
The patent changes the projection format parameters (ERP, CMP, OHP, ISP) and prediction mode parameters based on the image content and region type. By dynamically selecting and switching between different projection formats and prediction modes, the system maintains high resolution while optimizing the data representation to reduce the overall data amount.
3Adaptability or versatility
If multiple projection formats are supported, then the versatility for different VR/AR applications improves, but the device complexity increases
Solution Approach 1:
The patent creates a universal decoding framework that can handle multiple projection formats (ERP, CMP, OHP, ISP) within a single system. The apparatus includes a projection format determination unit that automatically identifies the format and applies the appropriate processing pipeline, making the system multi-functional without requiring separate dedicated systems for each format.
Solution Approach 2:
The patent performs preliminary determination of the projection format before the actual decoding process. The projection format determination unit identifies the format in advance, allowing the system to prepare the appropriate processing parameters and prediction modes beforehand, which simplifies the subsequent decoding operations and reduces runtime complexity.
4Measurement precision
If image expansion is performed on reference pictures, then the prediction accuracy improves, but the processing time increases
Solution Approach 1:
The patent performs image expansion only on the necessary reference picture regions that are actually needed for prediction, rather than expanding the entire reference picture. This partial action approach maintains prediction accuracy for the relevant areas while minimizing the overall processing time by avoiding unnecessary expansion operations on unrelated regions.
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.