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, 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 in a specific projection format, with techniques like image expansion and intra-prediction to enhance compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
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 patent divides the 360-degree image into multiple projection formats (ERP, CMP, OHP, ISP) and processes different regions with different encoding strategies. The image is segmented into face regions, edge regions, and corner regions, each handled with appropriate prediction and transformation methods to optimize compression while managing complexity.
Solution Approach 2:
The patent transforms the 360-degree image from spherical coordinates to multiple 2D projection formats. This dimensional transformation allows conventional 2D encoding techniques to be applied to 360-degree content, improving compression performance while maintaining compatibility with existing decoding systems.
2Adaptability or versatility
If multiple projection formats are used to represent 360-degree images, then the image quality and versatility are improved, but the data volume and processing complexity increase
Solution Approach 1:
The patent performs image expansion and prediction operations before the main encoding process. By generating predicted images and calculating residuals in advance, the system reduces the amount of data that needs to be encoded and transmitted, thereby reducing overall data volume while maintaining multiple projection format capabilities.
Solution Approach 2:
The patent changes the representation parameters of the 360-degree image by using different projection formats for different regions. Instead of uniformly encoding the entire image in one format, it selectively applies ERP, CMP, OHP, or ISP formats based on regional characteristics, optimizing the balance between versatility and data efficiency.
3Productivity
If image expansion and prediction techniques are applied, then compression performance is enhanced, but the processing time and computational load increase
Solution Approach 1:
The patent applies different processing qualities to different regions of the 360-degree image. Face regions receive full prediction and expansion processing for high compression efficiency, while edge and corner regions use simplified methods. This local differentiation enhances overall compression performance while reducing total processing time by avoiding uniform high-cost processing across the entire image.
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.