360-Degree Image Decoding with Recursive Block Partitioning
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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 360-degree images.
Innovation Solution
A method for encoding and decoding 360-degree images involves 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, or IcoSahedral, with image expansion based on partitioning units and adjacent pixel information to enhance 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 when handling large amounts of data for high-resolution images
Solution Approach 1:
The patent divides the 360-degree image into multiple projection formats (ERP, CMP, OHP, ISP) and processes different regions using appropriate decoding methods. The image is segmented into face regions, boundary regions, and transition regions, each handled with specific optimization strategies to improve overall processing performance while managing large data volumes.
Solution Approach 2:
The patent transforms the 360-degree image from spherical coordinates to multiple 2D projection formats. This dimensional transformation allows standard 2D image processing algorithms to be applied to the complex 360-degree data, significantly improving processing efficiency while maintaining image quality.
2Manufacturing precision
If high-resolution 360-degree images are processed, then image quality is improved, but the amount of data generated increases massively
Solution Approach 1:
The patent applies different processing qualities to different regions of the 360-degree image. Face regions (central areas) are processed with high resolution and quality, while boundary and transition regions use optimized or compressed processing. This local quality differentiation maintains overall image quality while reducing total data requirements.
3Adaptability or versatility
If multiple projection formats are supported, then versatility is improved, but device complexity increases
Solution Approach 1:
The patent designs a universal decoding apparatus that can handle multiple projection formats (ERP, CMP, OHP, ISP) through a single integrated system. The apparatus includes configurable components that can adapt to different formats without requiring separate dedicated systems for each format, thereby reducing overall complexity while maintaining versatility.
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.