360-Degree Image Decoding Using Recursive Tree 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, necessitating 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 various projection formats, such as Equi-Rectangular, CubeMap, OctaHedron, and IcoSahedral, to enhance compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If 360-degree images are captured with multiple cameras for virtual reality and augmented reality, then image quality and realism are improved, but the amount of data generated increases massively
Solution Approach 1:
The patent applies segmentation by dividing the 360-degree image into multiple projection formats (ERP, CMP, OHP, ISP) and processing different regions with different encoding strategies. The image is segmented into face regions, boundary regions, and transition regions, each handled differently to optimize compression while maintaining quality.
Solution Approach 2:
The patent changes encoding parameters dynamically based on the projection format and region type. Different quantization parameters, transformation blocks, and prediction modes are applied to different regions (e.g., face regions vs. boundary regions) to achieve optimal compression performance while preserving image quality.
2Manufacturing precision
If high-resolution and high-quality images are processed, then image quality is improved, but the performance of the image processing system becomes insufficient
Solution Approach 1:
The patent segments the image processing into parallel streams for different projection formats and regions, allowing simultaneous processing of multiple regions without sequential bottlenecks. This improves throughput while maintaining high quality output.
Solution Approach 2:
The patent applies partial action by selectively applying full-resolution encoding only to critical regions (face regions) while using compressed encoding for less critical regions (boundary regions), optimizing the balance between quality and processing performance.
3Ease of manufacture
If conventional image encoding methods are used for 360-degree images, then implementation simplicity is maintained, but compression performance is insufficient
Solution Approach 1:
The patent creates a universal encoding framework that handles multiple projection formats (ERP, CMP, OHP, ISP) and multiple region types using a unified architecture. This maintains implementation simplicity while achieving superior compression through multi-functional processing capabilities.
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.