Recursive Block Image Coding for 360-Degree Projection Compression
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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, 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
1Productivity
If conventional image encoding/decoding methods are used for 360-degree images, then the processing can be performed with standard algorithms, but the performance is insufficient for handling massive data volumes of high-resolution 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 format-specific optimization. The image is segmented into different regions that can be encoded with different parameters, allowing efficient handling of large data volumes through divided processing.
Solution Approach 2:
The patent applies different encoding parameters and quality settings to different regions and projection formats of the 360-degree image. Each projection format (ERP, CMP, OHP, ISP) can have customized encoding settings, allowing high quality where needed while reducing data volume in less critical areas.
2Manufacturing precision
If high-resolution encoding is applied to maintain image quality, then the quality of decoded image is improved, but the compression performance deteriorates due to increased data volume
Solution Approach 1:
The patent changes encoding parameters based on the projection format and image characteristics. Different quantization parameters, transformation blocks, and prediction modes are applied to different projection formats (ERP, CMP, OHP, ISP) to achieve optimal balance between quality and compression ratio for each format.
Solution Approach 2:
Different quality levels and encoding parameters are applied to different regions corresponding to different projection formats. This allows high-resolution encoding for formats requiring high quality while using more aggressive compression for other formats, maintaining overall image quality while improving compression performance.
3Adaptability or versatility
If multiple projection formats are supported for versatile application, then the adaptability is improved, but the device complexity increases due to multiple processing paths
Solution Approach 1:
The patent creates a universal encoding framework that handles multiple projection formats (ERP, CMP, OHP, ISP) through a common architecture. The system can select and process different formats using the same basic encoding infrastructure, reducing complexity compared to implementing separate processing systems for each format.
Solution Approach 2:
The processing system is segmented into modular components that can independently handle different projection formats. Each format has its own optimized processing path, but all paths converge on a common framework, allowing versatility without proportionally increasing overall system complexity.
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.