360-Degree Image Reconstruction Across Projection Formats
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing systems struggle with the massive data generated from processing multi-view images for 360-degree images in virtual and augmented reality, leading to insufficient performance in encoding and decoding high-resolution 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, with image expansion based on partitioning units to enhance compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-view images are processed for 360-degree images, then the quality and realism of virtual/augmented reality media service is improved, but the amount of data generated increases massively and processing performance becomes insufficient
Solution Approach 1:
The patent divides the 360-degree image into multiple projection format images (e.g., CubeMap, OctaHedron, IcoSahedral) and processes each segment independently. This segmentation allows parallel processing of different regions, reducing the overall processing time and improving productivity while maintaining the quality of the complete 360-degree image.
Solution Approach 2:
The patent transforms the 360-degree image from a single complex view into multiple 2D projection format images representing different spatial dimensions (cube faces, octahedron faces, icosahedron faces). This dimensional transformation enables standard image processing techniques to be applied to each projection, improving processing efficiency without compromising the immersive quality of the virtual/augmented reality experience.
2Measurement precision
If high-resolution images are encoded and decoded, then the quality of image data is improved, but the compression performance and processing efficiency deteriorate
Solution Approach 1:
The patent applies segmentation by dividing the high-resolution 360-degree image into multiple smaller projection format images. Each projection image can be compressed and processed independently, reducing the computational complexity and improving compression performance while maintaining the overall high resolution and quality of the complete image through proper stitching of the projected segments.
3Reliability
If image processing is performed on massive data from multi-view images, then the quality of 360-degree images is improved, but the processing time and system resource consumption increase
Solution Approach 1:
The patent segments the massive 360-degree image data into multiple manageable projection format images that can be processed in parallel. This segmentation reduces the time complexity of image processing operations while maintaining the quality of the final 360-degree image through proper integration of the processed projection segments.
Solution Approach 2:
The patent performs preliminary transformation of the 360-degree image into multiple projection format images before main processing operations. This preliminary action organizes the data in a more efficient structure that facilitates faster processing during subsequent encoding, filtering, and other image operations, thereby reducing overall processing time.
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.