360-Degree Image Decoding with MPM Reconfiguration for Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing systems struggle with the massive data generated from processing multi-view images for 360-degree images in virtual and augmented reality, leading to insufficient performance in encoding and decoding high-resolution images.
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 based on projection formats like ERP, CMP, OHP, and ISP, with image expansion and intra-prediction techniques to enhance compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multi-view images are processed for 360-degree images, then the realism and quality of virtual/augmented reality media service is improved, but the amount of data generated increases massively
Solution Approach 1:
The 360-degree image is divided into multiple view images captured by different cameras. Each view image is processed and encoded separately, allowing the large data volume to be managed in segmented units rather than as a single massive dataset
Solution Approach 2:
The patent transforms the 360-degree image data from its original multi-dimensional spatial representation into a compressed bitstream representation through encoding. This dimensional transformation reduces the data volume while preserving the essential visual information needed for high-quality reconstruction
2Manufacturing precision
If high-resolution images are processed, then the quality of virtual/augmented reality media service is improved, but the performance of the image processing system becomes insufficient
Solution Approach 1:
The image encoding process is performed in advance to convert high-resolution images into compressed bitstream format. This preliminary compression reduces the data volume that needs to be processed during real-time virtual/augmented reality media service delivery, improving system performance
Solution Approach 2:
The patent applies various encoding parameters and techniques including prediction modes, transformation types, and quantization levels to optimize the balance between image resolution quality and processing efficiency. These parameter adjustments enable high-quality output with reduced computational burden
3Manufacturing precision
If the amount of data for 360-degree images is increased, then the quality of virtual/augmented reality media service is improved, but the processing capability of the image processing system becomes insufficient
Solution Approach 1:
The patent extracts only the essential visual information from the full 360-degree image data through selective encoding and compression. Non-essential or redundant data elements are removed or compressed, reducing the processing burden while maintaining the quality needed for virtual/augmented reality media service
Solution Approach 2:
Complex processing operations such as prediction, transformation, and quantization are performed in advance during the encoding phase. This preliminary processing simplifies the data structure before transmission or storage, reducing the complexity of processing required during real-time media service delivery
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.