360-Degree Image Reconstruction with Projection-Based Block Decoding
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.
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 360-degree images are captured with multiple cameras for virtual reality and augmented reality, then image quality and realism are improved, but the amount of data generated increases massively
Solution Approach 1:
The patent divides the 360-degree image into multiple projection formats (Equi-Rectangular, CubeMap, OctaHedron, IcoSahedral) and processes each segment independently. This segmentation allows the system to handle large data volumes by breaking them into manageable parts while maintaining overall image quality for virtual and augmented reality applications.
Solution Approach 2:
The patent transforms the 360-degree image data from a single complex representation into multiple dimensional projection formats. By mapping the spherical image data onto different geometric projections (cube, octahedron, icosahedron), the system reduces data complexity and volume while preserving the essential visual information needed for VR/AR reliability.
2Productivity
If image encoding and decoding processes are applied to 360-degree images, then data transmission efficiency is improved, but processing performance requirements increase
Solution Approach 1:
The patent applies different encoding and decoding strategies to different projection formats based on their local characteristics. Each projection format (Equi-Rectangular, CubeMap, OctaHedron, IcoSahedral) is processed with optimized algorithms suited to its geometric properties, improving overall transmission efficiency while managing processing complexity through localized optimization rather than uniform processing.
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.