360-Degree Image Decoding with Recursive Block Partitioning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing systems struggle with the massive data generated for 360-degree images in virtual and augmented reality, requiring improved performance in encoding and decoding methods.
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, 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 large data volumes
Solution Approach 1:
The patent divides the 360-degree image into multiple projection formats (ERP, CMP, OHP, ISP) and processes different regions with different encoding strategies. The image is segmented into face regions, edge regions, and corner regions, each handled with appropriate prediction and transformation methods to optimize compression while managing complexity.
Solution Approach 2:
The patent transforms the 360-degree image from spherical coordinate system to multiple 2D projection formats. This dimensional transformation allows conventional 2D video coding techniques to be applied to 360-degree content, improving compression performance while maintaining compatibility with existing decoders.
2Adaptability or versatility
If multiple projection formats are supported for 360-degree images, then the adaptability increases, but the device complexity increases
Solution Approach 1:
The patent creates a universal encoding framework that can handle multiple projection formats (ERP, CMP, OHP, ISP) within a single system. The same encoding architecture and processing steps are used across all formats, allowing the system to be versatile without proportionally increasing complexity.
Solution Approach 2:
The patent uses syntax elements to indicate different projection formats and processing parameters. By changing parameters rather than restructuring the entire system, the encoder can adapt to different formats efficiently. The projection format information is conveyed through compact syntax in the bitstream.
3Measurement precision
If image expansion is performed on partitioning units for prediction, then the prediction accuracy improves, but the processing time increases
Solution Approach 1:
The patent performs image expansion only on necessary partitioning units (faces, edges, corners) rather than uniformly processing the entire image. This selective approach improves prediction accuracy where needed while minimizing unnecessary processing time in regions where expansion is not beneficial.
Solution Approach 2:
Different prediction strategies are applied to different regions: face regions use one approach, edge regions use another, and corner regions use a third. This local optimization improves overall prediction accuracy without uniformly increasing processing time across the entire image.
Data Source
AI summary
Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.