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, necessitating 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 according to 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 existing systems, but the performance is insufficient due to the massive data volume generated
Solution Approach 1:
The patent applies segmentation by dividing the 360-degree image into multiple projection formats (ERP, CMP, OHP, ISP) and processing different regions with different encoding strategies. The image is segmented into face regions, boundary regions, and transition regions, allowing selective application of compression techniques to different segments based on their characteristics and importance.
Solution Approach 2:
The patent implements local quality by applying different compression rates and quality levels to different regions of the 360-degree image. Important regions such as face centers maintain high quality with less compression, while less critical regions undergo more aggressive compression. This allows the system to handle massive data volumes while preserving quality where it matters most.
2Quantity of substance
If high compression is applied to reduce data volume, then storage and transmission efficiency improve, but image quality and processing accuracy deteriorate
Solution Approach 1:
The patent applies different compression rates to different regions: face center regions use lower compression rates to preserve quality, while face boundary and transition regions use higher compression rates. This regional differentiation allows significant overall data reduction while maintaining acceptable quality in critical areas.
Solution Approach 2:
The patent dynamically adjusts compression parameters based on region type and importance. Compression rates, quantization parameters, and encoding precision are changed according to the specific region being processed, allowing optimization of the quality-compression tradeoff for each local area rather than applying uniform compression.
3Reliability
If complex projection formats are used to accurately represent 360-degree images, then image realism and viewing quality improve, but device complexity and processing requirements increase
Solution Approach 1:
The patent segments the complex 360-degree image representation into multiple simpler projection formats (ERP, CMP, OHP, ISP), each suited for different viewing scenarios. This segmentation allows the system to switch between or combine simpler projections rather than processing the entire complex representation at once, reducing device complexity while maintaining realism.
Solution Approach 2:
The patent creates a universal encoding framework that supports multiple projection formats within a single system. The encoding apparatus can handle ERP, CMP, OHP, and ISP formats using a unified approach, making the system multi-functional and adaptable to different viewing requirements without requiring separate specialized systems for each projection type.
4Quantity of substance
If region-wise packing and image expansion are applied to improve compression, then data volume reduction improves, but processing time and computational complexity increase
Solution Approach 1:
The patent performs image expansion and region-wise packing as preliminary actions during the encoding phase. By pre-processing the image to expand certain regions and pack them efficiently before compression, the system reduces the overall data volume that needs to be compressed, thereby offsetting the additional processing time with reduced compression workload.
Solution Approach 2:
The patent applies image expansion selectively to specific regions rather than the entire image. By expanding only certain regions that benefit from it (such as transition zones or boundary areas) and leaving other regions unchanged, the system achieves data reduction benefits while minimizing the additional processing time required for expansion operations.
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.