360-Degree Image Decoding Across Projection Formats and Block Trees
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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 decoding 360-degree images involves generating a predicted image using syntax information, combining it with a residual image, and reconstructing the image in various projection formats, including Equi-Rectangular, CubeMap, OctaHedron, and IcoSahedral, with image expansion based on partitioning units and adjacent pixel information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If 360-degree images are processed with high resolution and quality for virtual reality and augmented reality, then image quality is 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 format separately. This segmentation allows the system to handle large data amounts by breaking them into manageable parts while maintaining high image quality for each segment.
Solution Approach 2:
The patent transforms 360-degree spherical images into multiple 2D projection formats, changing the dimensional representation. This allows the same 360-degree content to be encoded in different geometric projections, reducing the computational burden and data processing requirements while preserving image quality.
2Device complexity
If conventional image encoding and decoding methods are used for 360-degree images, then processing is simpler, but performance is insufficient for large data amounts
Solution Approach 1:
The patent creates a universal encoding and decoding framework that handles multiple projection formats (Equi-Rectangular, CubeMap, OctaHedron, IcoSahedral) through a single apparatus. This multi-functional approach improves processing performance by optimizing each format specifically while maintaining a unified system architecture, rather than using separate conventional methods for each format.
Solution Approach 2:
The patent changes key processing parameters by implementing format-specific optimization strategies. Different projection formats use different encoding/decoding parameters and algorithms tailored to their geometric characteristics, which significantly improves processing performance compared to using generic conventional methods for all formats.
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.