Recursive Block Image Decoding for 360-Degree Projection Formats
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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, requiring 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 like Equi-Rectangular, CubeMap, OctaHedron, and IcoSahedral, with image expansion based on partitioning units and adjacent pixel information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional image processing methods are used for 360-degree images, then the system can process standard images, but the performance is insufficient for handling massive data volumes of high-resolution 360-degree images
Solution Approach 1:
The patent divides the 360-degree image into multiple projection formats (ERP, CMP, OHP, ISP) and processes each format separately with optimized encoding/decoding methods. The image is also divided into blocks and partitions for parallel processing, enabling the system to handle massive data volumes by breaking down the complex 360-degree image into manageable segments that can be processed efficiently
Solution Approach 2:
The patent transforms the processing approach by converting 360-degree spherical images into multiple 2D projection formats. This dimensional transformation allows standard 2D image processing systems to handle 360-degree content by projecting it onto flat planes (ERP), cube surfaces (CMP), octahedron faces (OHP), or icosahedron faces (ISP), thereby improving processing performance without requiring specialized spherical processing hardware
2Manufacturing precision
If high-resolution encoding is used for 360-degree images, then image quality is improved, but the compression efficiency decreases
Solution Approach 1:
The patent applies different quality levels and compression strategies to different regions and projection formats of the 360-degree image. By analyzing the characteristics of each projection format (ERP, CMP, OHP, ISP) and their respective viewing importance, the system can apply higher quality encoding to critical regions while using more aggressive compression for less critical areas, thereby maintaining overall image quality while improving compression efficiency
Solution Approach 2:
The patent dynamically adjusts encoding parameters such as quantization levels, transformation block sizes, and prediction modes based on the specific projection format and regional importance. This parameter adaptation allows the system to optimize the balance between image quality and compression ratio for each specific case, preventing uniform over-compression that would degrade quality while maintaining efficiency
3Adaptability or versatility
If multiple projection formats are supported, then versatility is improved, but system complexity increases
Solution Approach 1:
The patent designs a unified encoding and decoding framework that can handle multiple projection formats (ERP, CMP, OHP, ISP) through a common architecture. The system uses universal data structures and processing pipelines that can accommodate different projection types by switching configuration parameters rather than implementing separate dedicated systems for each format, thereby supporting multiple formats while controlling overall system complexity
Solution Approach 2:
The patent introduces intermediate representation structures that serve as mediators between the various projection formats and the core processing engine. These intermediate structures allow different projection formats to be converted into a standardized internal representation for processing, then converted back to the target format, simplifying the system architecture by decoupling format-specific details from the core encoding/decoding logic
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.