Projection-Based Image Decoding for 360-Degree Compression Bottlenecks
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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 based on projection formats like Equi-Rectangular, CubeMap, OctaHedron, and IcoSahedral Projection, with image expansion and motion vector prediction to enhance compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-view images are processed for 360-degree images, then the realism and quality of virtual/augmented reality media service is improved, but the amount of data generated increases massively
Solution Approach 1:
The 360-degree image is divided into multiple projection formats including Equi-Rectangular Projection (ERP), CubeMap Projection (CMP), OctaHedron Projection (OHP), and IcoSahedral Projection (ISP). Each projection format segments the spherical image data into different geometric representations, allowing selective processing and transmission of only necessary portions based on viewing requirements.
Solution Approach 2:
The patent applies different encoding and processing qualities to different regions of the 360-degree image based on their importance. Regions with higher visual importance or those more likely to be viewed are processed with higher quality, while less critical regions use lower quality encoding, optimizing the balance between overall quality and data volume.
2Measurement precision
If high-resolution images are encoded and decoded, then the image quality is improved, but the processing performance becomes insufficient
Solution Approach 1:
The patent implements dynamic adjustment of encoding parameters and processing complexity based on the specific requirements of different image regions and their importance. The encoding process adapts its complexity dynamically, using more sophisticated methods for critical regions and simpler methods for less important areas, optimizing processing performance while maintaining necessary quality.
Solution Approach 2:
The patent changes multiple encoding parameters including projection format selection, block size, transformation types, and quantization levels to optimize the balance between image quality and processing performance. Different parameter sets are applied based on the specific characteristics and importance of different image regions.
Data Source
AI summary
A method of decoding an image, includes obtaining at least one offset for a picture, deriving a variable for scaling for the picture based on the at least one offset, and performing inter prediction based on the variable for scaling for the picture. The at least one offset is defined with a direction of scaling.