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 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 various projection formats, such as Equi-Rectangular, CubeMap, OctaHedron, and IcoSahedral, to enhance compression performance.
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 360-degree image is divided into multiple projection formats (Equi-Rectangular, CubeMap, OctaHedron, IcoSahedral) and processed in segmented regions. The decoding process handles different projection types separately, allowing efficient compression and management of large data volumes while maintaining high image quality for virtual and augmented reality applications.
2Manufacturing precision
If image processing systems process large amounts of data for 360-degree images, then image quality is maintained, but system performance becomes insufficient
Solution Approach 1:
The processing system is divided into specialized decoding units that handle different projection formats (Equi-Rectangular, CubeMap, OctaHedron, IcoSahedral) independently. This segmentation allows parallel processing of multiple regions simultaneously, improving overall system performance while maintaining high image quality for 360-degree content.
Solution Approach 2:
The patent performs preliminary decoding and reconstruction of 360-degree images into multiple projection formats before final rendering. By pre-processing the image data into various projection types, the system reduces the computational burden during real-time virtual reality and augmented reality applications, thereby improving processing performance.
3Device complexity
If conventional image encoding and decoding methods are used, then processing is simpler, but compression performance is insufficient for 360-degree images
Solution Approach 1:
The patent applies different encoding parameters and reconstruction methods tailored to each projection format (Equi-Rectangular, CubeMap, OctaHedron, IcoSahedral). By changing the processing parameters according to the specific projection type, the system achieves superior compression performance for 360-degree images while managing complexity through standardized decoding workflows.
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.