Projection-Format 360 Image Reconstruction for Compression Bottlenecks
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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, requiring improved performance in image encoding and decoding, particularly for 360-degree 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 according to projection formats like Equi-Rectangular, CubeMap, OctaHedron, or IcoSahedral, enhancing compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If 360-degree images are captured with multiple cameras for virtual reality and augmented reality, then the amount of data generated increases massively, but the performance of image processing systems for handling large data volumes is insufficient
Solution Approach 1:
The patent divides the 360-degree image into multiple projection formats (e.g., equirectangular, cube map, octahedron, icosahedral) and processes each segment separately. This segmentation allows the system to handle large data volumes by processing manageable portions through optimized encoding/decoding operations for each projection type, thereby improving overall image processing performance while maintaining comprehensive 360-degree coverage.
2Ease of manufacture
If conventional image encoding and decoding methods are used for 360-degree images, then processing can be performed with existing systems, but compression performance is insufficient for large data volumes
Solution Approach 1:
The patent changes the parameter of projection format to optimize compression performance. By transforming 360-degree images into different projection formats (equirectangular, cube map, octahedron, icosahedral) before encoding, the system adapts the image data to characteristics that enable more efficient compression algorithms, thereby reducing data loss and improving compression ratios while maintaining ease of encoding and decoding through standardized processing pipelines.
Data Source
AI summary
Disclosed are methods and apparatuses for image data encoding/decoding. A method for decoding a 360-degree image includes the steps of: receiving a bitstream obtained by encoding a 360-degree image; generating a prediction image by making reference to syntax information obtained from the received bitstream; adding the generated prediction image to a residual image obtained by dequantizing and inverse-transforming the bitstream, so as to obtain a decoded image; and reconstructing the decoded image into a 360-degree image according to a projection format. Therefore, the performance of image data compression can be improved.