360-Degree Image Decoding with MPM Reconfiguration and Projection Mapping
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Solution Overview
Problem
Existing image processing systems struggle with the massive data generated from processing multi-view 360-degree images for virtual and augmented reality, necessitating improved performance in image encoding and decoding.
Innovation Solution
A method for encoding and decoding 360-degree images that includes generating a predicted image using syntax information, combining it with a residual image, and reconstructing the image in specific 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
1Reliability
If multi-view images are captured with a plurality of cameras for 360-degree images, then the realism and quality of virtual reality and augmented reality services are improved, but the amount of data generated increases massively, overwhelming the processing performance of image processing systems
Solution Approach 1:
The 360-degree image is divided into multiple projection formats (ERP, CMP, OHP, ISP) and processed in segmented manner. The encoding system segments the image data into different projection types, allowing selective processing and transmission of only necessary portions, thereby reducing overall data volume while maintaining quality.
Solution Approach 2:
Different projection formats are applied to different regions or aspects of the 360-degree image based on local requirements. ERP format may be used for certain regions while CMP or OHP formats are used for others, optimizing quality where needed while reducing data where full quality is not required.
2Productivity
If image expansion and intra-prediction techniques are used for decoding, then compression performance is enhanced, but the complexity of the encoding and decoding process increases
Solution Approach 1:
Image expansion and intra-prediction operations are performed during the encoding phase as preliminary actions. By pre-processing the image data to expand it to full resolution and apply prediction algorithms before compression, the system reduces the burden on the decoding side while achieving better compression ratios.
Solution Approach 2:
The patent introduces intermediate processing steps including image expansion to reference picture resolution and intra-prediction as mediator operations. These intermediate steps create a refined reference picture that facilitates better motion compensation and reduces residual data, improving overall compression efficiency.
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.