360-Degree Image Decoding with MPM Reconfiguration and Projection Formats
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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 encoding and decoding methods.
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 the image according to projection formats like ERP, CMP, OHP, or ISP, 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 the massive data generated by multi-camera systems
Solution Approach 1:
The patent divides the 360-degree image into multiple projection formats (ERP, CMP, OHP, ISP) and processes different regions using appropriate prediction techniques. The image is segmented into face regions, boundary regions, and transition regions, each handled with specialized encoding methods to improve overall processing performance.
Solution Approach 2:
Different prediction modes are applied to different regions of the 360-degree image. Intra-prediction is used for face regions with high image continuity, while other prediction methods are applied to boundary and transition regions. This localized approach optimizes compression performance for each specific region's characteristics.
2Loss of information
If image expansion is performed on the reference picture to generate predicted images, then compression performance is enhanced, but the processing complexity increases
Solution Approach 1:
The reference picture is pre-processed with image expansion before prediction operations. Partitioning units are identified and expanded in advance, with boundary pixels calculated and stored for subsequent prediction steps. This preliminary preparation reduces the complexity of the actual prediction process.
Solution Approach 2:
The expansion process uses boundary pixels from adjacent partitioning units that are spatially or continuously adjacent. The system leverages existing image data and continuity relationships to generate expansion pixels without requiring external information or complex computations.
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.