360-Degree Image Motion Prediction for Higher Compression
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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 and apparatus for encoding and decoding 360-degree images that enhance compression performance by partitioning and resizing images into layers, using tree-based block forms, and performing inter-prediction with non-translation motion models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 360-degree images are captured with multiple cameras to provide virtual reality and augmented reality services, then the realism and quality of the media service is improved, but the amount of data generated increases massively
Solution Approach 1:
The 360-degree image is divided into multiple partitioning units (e.g., rectangular regions or triangular regions) that can be processed independently. Each partitioning unit is further divided into blocks for encoding. This segmentation allows the large 360-degree image data to be managed and compressed in smaller, more efficient units, reducing the overall data processing burden while maintaining the complete spherical coverage for realistic VR/AR service.
2Loss of information
If the image encoding and decoding system processes large amounts of 360-degree image data, then the completeness of the visual information is improved, but the performance of the image processing system becomes insufficient
Solution Approach 1:
The patent applies different encoding strategies to different regions of the 360-degree image based on their importance. High-importance regions (such as areas corresponding to the user's foveal vision or regions with significant motion) are encoded with higher quality and more detail, while low-importance regions use more aggressive compression. This local quality approach maintains visual information completeness where needed while improving overall processing performance by reducing the complexity in less critical areas.
Solution Approach 2:
The encoding system dynamically adjusts processing parameters based on the characteristics of different regions and the desired quality level. The partitioning into resolution units and the selective application of encoding methods allow the system to adaptively allocate processing resources, maintaining high performance while preserving necessary visual information.
3Device complexity
If traditional image encoding methods are used for 360-degree images, then the simplicity of the encoding process is maintained, but the compression performance is insufficient
Solution Approach 1:
The patent introduces a hierarchical dimension to the encoding process by partitioning the 360-degree image into multiple resolution units with different resolutions. Instead of applying a single encoding method uniformly across the entire image, the system processes each resolution unit independently with appropriate encoding parameters. This dimensional approach to organization enables better compression performance while keeping the overall process structure relatively simple and manageable.
Data Source
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AI summary
Disclosed is an image data encoding/decoding method and apparatus. A method for decoding a 360-degree image comprises the steps of: receiving a bitstream obtained by encoding a 360-degree image; generating a prediction image by making reference to syntax information obtained from the received bitstream; combining the generated prediction image with a residual image obtained by dequantizing and inverse-transforming the bitstream, so as to obtain a decoded image; and reconstructing the decoded image into a 360-degree image according to a projection format. Here, the step of generating the prediction image comprises the steps of: obtaining, from motion information included in the syntax information, a motion vector candidate group including a motion vector of a block adjacent to a current block to be decoded; deriving a prediction motion vector from the motion vector candidate group, on the basis of selection information extracted from the motion information; and determining a prediction block for the current block to be decoded, using a fmal motion vector derived by adding the prediction motion vector to a differential motion vector extracted from the motion information.