360 Image Inter Prediction with Directional Scaling Offsets
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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 specific projection formats, such as Equi-Rectangular, CubeMap, OctaHedron, and IcoSahedral, while utilizing motion vector candidates and reference pictures for enhanced compression.
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 high-resolution images
Solution Approach 1:
The 360-degree image is divided into multiple projection formats (ERP, CMP, OHP, ISP) and processed separately. Each projection format has its own encoding parameters and prediction modes, allowing optimized compression for each segment while maintaining overall compression performance
Solution Approach 2:
The encoding process dynamically adapts to different projection formats by selecting appropriate prediction modes and motion vector candidates based on the specific format being encoded. The system adjusts encoding parameters dynamically rather than using a fixed approach, improving compression efficiency for each format
2Manufacturing precision
If high-resolution 360-degree images are processed, then image quality is improved, but the data volume increases massively
Solution Approach 1:
The patent changes encoding parameters such as block size, prediction mode, and transform size based on the projection format and image characteristics. By adapting these parameters to the specific requirements of each projection format, the system achieves better compression ratios while maintaining high image quality
Solution Approach 2:
Motion vector candidates and reference pictures are prepared in advance for different projection formats. This preliminary preparation allows the encoding process to efficiently handle high-resolution images by having pre-computed data ready, reducing the computational burden during actual encoding
3Adaptability or versatility
If multiple projection formats are supported, then adaptability is improved, but the processing complexity increases
Solution Approach 1:
The encoding apparatus is designed with universal components that can handle multiple projection formats (ERP, CMP, OHP, ISP) through a common architecture. The system uses a unified framework with format-specific modules, allowing it to process different projection formats without requiring completely separate processing pipelines for each format
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.