Recursive Block Image Decoding for 360-Degree Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing systems struggle with the massive data generated by 360-degree images for virtual and augmented reality, requiring improved performance in encoding and decoding methods.
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, such as Equi-Rectangular, CubeMap, OctaHedron, and IcoSahedral, to enhance compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-view images are captured with multiple 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
Solution Approach 1:
The patent divides the 360-degree image into multiple regions (e.g., foreground and background regions) and applies different encoding strategies to each region. This segmentation allows the system to handle the massive data from multiple cameras by processing different parts of the image with appropriate compression levels, maintaining realism in critical areas while reducing overall data volume.
Solution Approach 2:
The patent changes encoding parameters dynamically based on region importance and viewer position. By adjusting compression ratios, resolution, and encoding complexity for different image regions, the system maintains high realism where needed while significantly reducing the total data amount generated from multi-camera captures.
2Manufacturing precision
If the amount of data for 360-degree images is increased to improve image quality, then the processing performance of the image processing system becomes insufficient
Solution Approach 1:
The patent applies different quality levels to different regions of the 360-degree image based on their importance. Critical regions (such as foreground objects or areas likely to be viewed) are encoded with high quality, while less important regions use lower quality encoding. This local quality approach maintains overall image quality while significantly improving processing performance by reducing the computational burden on the image processing system.
3Ease of manufacture
If conventional image encoding methods are used for 360-degree images, then the encoding process is simple, but the compression performance is insufficient for large data volumes
Solution Approach 1:
The patent performs preliminary actions by pre-processing the 360-degree image into multiple regions and preparing region-specific encoding parameters before the actual encoding process. This preliminary segmentation and parameter preparation maintains relative simplicity in the encoding process while enabling significantly improved compression performance through region-adaptive encoding strategies.
Data Source
AI summary
Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.