Projection-Aware 360-Degree Image Decoding for Compression Bottlenecks
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Solution Overview
Problem
Existing image processing systems struggle with the massive data generated from processing multi-view 360-degree images for virtual and augmented reality, necessitating improved performance in image encoding and decoding.
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 ERP, CMP, and ISP, while utilizing motion vector candidates and reference picture expansion to enhance compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional image encoding methods are used for 360-degree images, then the encoding process is simple, but the compression performance is insufficient for massive data volumes
Solution Approach 1:
The patent divides the 360-degree image into multiple projection formats (ERP, CMP, OHP, ISP) and processes each format with specific syntax information and motion vector candidates appropriate to its characteristics, enabling optimized compression for each projection type while maintaining overall system manageability
Solution Approach 2:
The patent dynamically selects motion vector candidates from reference pictures based on the current block's position and projection format, adapting the encoding strategy to local image characteristics and motion patterns, thereby improving compression efficiency without requiring a completely complex fixed-structure system
2Reliability
If multi-view 360-degree images are processed to enable virtual and augmented reality, then the realism and quality improve, but the data volume increases massively
Solution Approach 1:
The patent uses reference pictures from previous frames and adjacent views as templates for predicting current blocks, creating compressed representations that capture essential visual information without storing complete high-resolution multi-view images, thereby reducing data volume while preserving VR/AR quality
Solution Approach 2:
The patent changes encoding parameters such as motion vector precision, prediction mode selection, and syntax information based on the projection format and block characteristics, optimizing the balance between compression ratio and visual quality for different regions and formats of 360-degree images
3Measurement precision
If image expansion is performed on reference pictures to generate predicted images, then the prediction accuracy improves, but the processing time increases
Solution Approach 1:
The patent applies image expansion selectively to specific regions and blocks based on their motion characteristics and prediction needs, rather than uniformly processing the entire reference picture, thereby improving prediction accuracy for critical regions while minimizing unnecessary processing time
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.