360-Degree Image Decoding With Face-Region Motion Prediction
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Solution Overview
Problem
Existing image processing systems struggle with the massive data generated by 360-degree images for virtual and augmented reality, requiring improved performance in encoding and decoding methods.
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 in a specific projection format, including Equi-Rectangular, CubeMap, OctaHedron, and IcoSahedral formats, with image expansion and motion vector prediction to enhance compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 360-degree images are captured with multiple cameras for virtual reality and augmented reality, then the realism and quality of the media service is improved, but the amount of data generated increases massively making the image processing system insufficient
Solution Approach 1:
The patent divides the 360-degree image processing into multiple projection formats (ERP, CMP, OHP, ISP) and processes different regions with different methods. The image is segmented into face regions and non-face regions, with motion compensation applied differently to each segment, improving processing efficiency while maintaining realism.
Solution Approach 2:
The patent changes processing parameters based on projection format type. Different motion compensation methods are applied depending on the projection format (ERP vs. non-ERP), and quantization parameters are adjusted according to the projection format to optimize both quality and processing performance.
2Measurement precision
If image expansion is performed on reference pictures for motion compensation in 360-degree images, then the prediction accuracy is improved, but the processing complexity increases
Solution Approach 1:
The patent applies image expansion selectively only to face regions where motion compensation is needed, rather than expanding the entire reference picture. This local approach improves prediction accuracy for motion-compensated regions while avoiding unnecessary processing complexity in non-face regions.
Solution Approach 2:
The patent performs partial image expansion only on the necessary face regions rather than the complete reference picture. This partial action provides sufficient prediction accuracy for the critical regions while significantly reducing overall processing complexity.
3Productivity
If motion vector prediction is performed using adjacent blocks for 360-degree images, then the compression performance is improved, but the processing time increases
Solution Approach 1:
The patent performs motion vector prediction using pre-identified adjacent blocks from the decoded picture buffer. By preparing and storing motion vector candidates in advance during the decoding process, the system achieves good compression performance without adding significant processing time during 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.