360-Degree Image Decoding with Adaptive 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
1Reliability
If 360-degree images are captured with multiple cameras for virtual reality and augmented reality, then the realism and quality of the media service are improved, but the amount of data generated increases massively making the image processing system performance insufficient
Solution Approach 1:
The patent divides the 360-degree image processing into multiple stages: capture by multiple cameras, projection onto specific formats (ERP, CMP, OHP, ISP), region-wise packing, and block-based encoding. This segmentation allows the system to handle the massive data from multiple cameras through structured processing steps, improving overall processing performance while maintaining realism
Solution Approach 2:
The patent transforms 360-degree spherical images into different projection formats (2D Equi-Rectangular, CubeMap, OctaHedron, IcoSahedral projections). This dimensional transformation allows the immersive 360-degree content to be processed using conventional 2D image processing techniques, resolving the performance bottleneck while preserving the realism of the media service
2Productivity
If image expansion is performed on reference pictures to generate predicted images, then the compression performance is improved, but the complexity of the encoding process increases
Solution Approach 1:
The patent performs image expansion on reference pictures before generating predicted images for the current block. This preliminary action prepares the reference data in advance, allowing the prediction process to achieve better compression performance while the expansion operation is performed systematically on already-decoded reference blocks
Solution Approach 2:
The patent applies image expansion selectively based on the prediction mode and block characteristics. Different expansion techniques are applied to different regions and blocks depending on their properties, improving compression performance for suitable blocks while avoiding unnecessary complexity for blocks where simple prediction suffices
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.