360-Degree Image Encoding And Decoding With Recursive Block Division
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, necessitating improved performance in image encoding and decoding, particularly for high-resolution and high-quality 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 in specific projection formats like Equi-Rectangular, CubeMap, OctaHedron, and IcoSahedral, enhancing 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 of high-resolution 360-degree images
Solution Approach 1:
The patent divides the 360-degree image into multiple projection formats (Equi-Rectangular, CubeMap, OctaHedron, IcoSahedral) and processes each format separately with optimized algorithms. The image is also divided into multiple blocks that can be processed in parallel, enabling the system to handle large data volumes through segmented processing rather than treating the entire image as a single unit.
Solution Approach 2:
The patent transforms the 360-degree image from a spherical coordinate system into multiple 2D projection formats. This dimensionality change allows conventional 2D image processing algorithms to be applied effectively to 360-degree content, bridging the gap between immersive 3D imagery and standard processing pipelines.
2Manufacturing precision
If high-resolution 360-degree images are processed, then the image quality is improved, but the amount of data generated increases massively
Solution Approach 1:
The patent employs different processing parameters and algorithms optimized for each projection format. By changing the processing parameters according to the specific projection type (ERP, CMP, OHP, ISP), the system maintains high image quality while optimizing the data representation for each format, reducing redundant information.
Solution Approach 2:
The patent applies different processing strategies to different regions and formats of the 360-degree image. Each projection format receives tailored processing optimized for its specific characteristics, allowing high quality output while minimizing the data volume required for each region based on its unique properties.
3Adaptability or versatility
If multiple projection formats are supported for 360-degree images, then the versatility is improved, but the device complexity increases
Solution Approach 1:
The patent creates a universal processing framework that can handle multiple projection formats (Equi-Rectangular, CubeMap, OctaHedron, IcoSahedral) through a common architecture. The system uses a unified approach to image division, parallel processing, and reconstruction that works across all formats, reducing the need for separate dedicated processing pipelines for each format.
Solution Approach 2:
The patent transforms complex 360-degree spherical data into multiple 2D projection formats that can be processed using standard 2D image processing techniques. This dimensional transformation simplifies the processing architecture while maintaining support for multiple formats, as each projection can be handled as a separate 2D plane or set of planes.
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.