Projection-Scaled Inter Prediction for 360-Degree Image Decoding
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Solution Overview
Problem
Existing image processing systems struggle with the massive data requirements of 360-degree images for virtual and augmented reality, necessitating improved performance in image encoding and decoding, particularly for 360-degree 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 various 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
1Productivity
If 360-degree images are processed using conventional image processing systems, then the images can be encoded and decoded, but the performance is insufficient due to the massive amount of data generated
Solution Approach 1:
The 360-degree image is divided into multiple projection formats (ERP, CMP, OHP, ISP) and processed separately. The encoding apparatus performs image expansion and reconstruction for each projection format independently, allowing parallel processing and reducing the computational burden on a single processing path.
Solution Approach 2:
The patent transforms the processing approach by introducing projection format as an additional dimension. Instead of processing the massive 360-degree image data as a single entity, the system converts it into multiple 2D projection representations, enabling more efficient encoding and decoding operations in lower dimensional spaces.
2Measurement precision
If image expansion is performed on the reference picture to generate predicted images, then the prediction accuracy improves, but the processing complexity increases
Solution Approach 1:
The reference picture is pre-expanded into multiple projection formats before the actual encoding process. This preliminary expansion creates ready-to-use predicted images for various projection formats, eliminating the need for repeated expansion operations during encoding and reducing overall processing complexity.
Solution Approach 2:
The system performs image expansion for all possible projection formats (ERP, CMP, OHP, ISP) regardless of which format is ultimately needed. This excessive action ensures that predicted images are available for any projection format, simplifying the encoding process by eliminating the need for format-specific expansion operations during actual encoding.
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.