360-Degree Image Decoding with Adaptive MPM Prediction
Find Innovative SolutionsGenerate Solutions
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 high-resolution and high-quality 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 it into a 360-degree image format, utilizing projection formats like Equi-Rectangular, CubeMap, and OctaHedron, 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 standard algorithms, but the performance is insufficient for handling the massive data volume generated by high-resolution 360-degree images
Solution Approach 1:
The patent applies segmentation by dividing the 360-degree image into multiple projection formats (ERP, CMP, OHP, ISP) and processing each format with optimized algorithms. The image is segmented into different regions that can be handled independently, allowing parallel processing and improved performance for large data volumes
Solution Approach 2:
The patent changes processing parameters by adapting encoding/decoding algorithms to specific projection formats. Different parameter sets are used for different projection types (e.g., equi-rectangular vs. cube map), optimizing the processing efficiency for each format while maintaining quality
2Manufacturing precision
If high-resolution encoding is used for 360-degree images, then image quality is improved, but the data volume and processing complexity increase significantly
Solution Approach 1:
The patent applies local quality by using different encoding precision levels for different regions of the 360-degree image. High-resolution encoding is applied to important regions (e.g., front view, high-priority areas) while lower resolution is used for less critical regions, maintaining overall image quality while reducing processing complexity
Solution Approach 2:
The patent introduces dynamic processing where the encoding complexity adapts based on the projection format and regional importance. The system dynamically adjusts processing parameters during encoding/decoding operations, allowing high quality output without static high complexity throughout the entire processing pipeline
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.