360-Degree Image Encoding with Adaptive MPM Prediction
Find Innovative SolutionsGenerate Solutions
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 various projection formats, 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 and processed for 360-degree images, then the visual quality and immersion for virtual reality and augmented reality are improved, but the amount of data generated increases massively and the image processing system performance becomes insufficient
Solution Approach 1:
The patent divides the 360-degree image into multiple regions of interest (ROIs) based on importance levels. High-importance regions are encoded with higher quality and lower compression, while low-importance regions use higher compression. This segmentation allows the system to process and transmit the same visual content with varying detail levels, reducing overall data volume while maintaining perceived quality in critical areas.
Solution Approach 2:
The patent applies different encoding qualities to different regions of the 360-degree image. Regions with higher importance (such as areas containing key visual information or user focus points) are processed with higher fidelity, while less important regions use lower quality encoding. This local quality differentiation maintains visual reliability where needed while reducing data volume in less critical areas.
2Reliability
If high-resolution and high-quality images are processed, then the image quality for virtual reality and augmented reality is improved, but the data volume increases and processing efficiency decreases
Solution Approach 1:
The patent segments the image into regions with different importance levels and applies different compression strategies to each segment. High-importance regions maintain high resolution and quality with minimal compression, while low-importance regions are compressed more aggressively. This selective approach reduces overall data volume while preserving quality where it matters most.
Solution Approach 2:
Different quality levels are applied locally across the image based on region importance. Critical regions receive high-quality encoding with more bits allocated, while non-critical regions use lower quality encoding. This local quality adaptation reduces total data volume while maintaining perceived image quality in important areas.
3Device complexity
If conventional image encoding and decoding methods are used, then the processing is simpler, but the compression performance is insufficient for handling large data volumes of 360-degree images
Solution Approach 1:
The patent performs preliminary classification of image regions into importance levels before the main encoding process. Regions are identified and tagged based on their significance, allowing the encoder to apply appropriate compression strategies from the outset. This preliminary action streamlines the encoding process and improves compression efficiency without adding significant complexity.
Solution Approach 2:
The patent dynamically adjusts encoding parameters such as quantization step size, transformation block size, and prediction mode selection based on region importance. High-importance regions use parameters that preserve quality (finer quantization, larger blocks), while low-importance regions use parameters that maximize compression (coarser quantization, smaller blocks). This parameter adaptation significantly improves compression performance.
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.