360-Degree Image Decoding Using Projection-Aware MPM Reconfiguration
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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 360-degree 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 the image in a specific projection format, utilizing image expansion and intra-prediction techniques to enhance compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional image encoding methods are used for 360-degree images, then the encoding process is simple, but the compression performance is insufficient for massive data volumes
Solution Approach 1:
The 360-degree image is divided into multiple projection formats (e.g., equirectangular, cube map, octahedron, icosahedron), and the encoder selectively applies different encoding processes to different projection formats based on their characteristics. This segmentation allows optimized compression for each format type while maintaining overall system efficiency.
Solution Approach 2:
The encoding process dynamically adapts to the specific projection format being processed. The encoder adjusts encoding parameters, prediction modes, and processing steps based on the detected projection format, enabling optimal compression performance for each format rather than using a fixed encoding approach.
2Productivity
If image processing systems process 360-degree images with multiple cameras, then the data volume increases massively, but the processing performance is insufficient
Solution Approach 1:
The system performs preliminary classification of the projection format before the main encoding process. By identifying the projection format early in the pipeline, the system can pre-configure optimal encoding parameters and processing paths, avoiding reprocessing and improving overall throughput for large data volumes.
Solution Approach 2:
The encoder changes encoding parameters based on the projection format type. Different quantization parameters, transformation block sizes, and prediction modes are applied according to the specific projection format, optimizing compression efficiency for each format while handling massive data from multiple cameras.
3Measurement precision
If projection format information is added to syntax information for 360-degree images, then the encoding accuracy improves, but the bitstream size increases
Solution Approach 1:
The projection format information is extracted and encoded as separate syntax elements that can be selectively included in the bitstream. The encoder determines which projection format information is necessary based on the image content and encoding conditions, including only essential information to maintain accuracy while minimizing bitstream overhead.
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.